diff --git a/CHANGELOG.md b/CHANGELOG.md index c0cd2410b..9a374eaf3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,13 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## EvolvDx fork (`evolvdx-main`) + +- Rebuilt on nf-core/methylseq 4.2.0. Added the EvolvDx QC steps (`--run_methylqc`, `--run_cpg_coverage`, `--run_methsnsv`, `--run_picardhs_report`), the `evolvdx*` profiles and the custom genomes. See `EVOLVDX.md`. +- `--run_methylqc` results now appear in the MultiQC report under "EvolvDx methylation QC": General Stats columns (spike-in and mean CpG methylation, CpG depth, % CpGs in repeats), CpG read metrics, methylation and depth distributions, M-bias, methylation by chromosome, Twist annotation and tiled beta values (new `METHYLQC_MULTIQC` module). +- methyl_qc fixes: "Methylated CpG Count" in `chr_percent_df.csv` no longer repeats the CpG count; read metrics are now computed from read counts (the old "% of total reads that are partial" was built from the bedGraph methylation column); tiles missing from a sample are left empty instead of set to beta 0; cohort inputs are ordered by sample id. +- methyl_qc modules reformatted with named emits, `when:` blocks and clean `versions.yml`; `env/exo` Dockerfiles and the methylkit environment now build. + ## [v4.2.0](https://github.com/nf-core/methylseq/releases/tag/4.2.0) - [2025-12-05] ### Bug fixes & refactoring diff --git a/EVOLVDX.md b/EVOLVDX.md new file mode 100644 index 000000000..9f58b9c95 --- /dev/null +++ b/EVOLVDX.md @@ -0,0 +1,46 @@ +# EvolvDx additions to nf-core/methylseq + +This fork tracks `nf-core/methylseq` (`upstream` remote). All EvolvDx changes live on the +`evolvdx-main` branch; `master` mirrors nf-core. Keep upstream files untouched where possible +so that new releases merge cleanly. + +## Running + +```bash +nextflow run EVolvDx/methylseq -r evolvdx-main -profile evolvdx,docker --input samplesheet.csv --outdir results +``` + +| Profile | Purpose | +|---|---| +| `evolvdx` | Production defaults: `bwameth`, `grch38_core_bs_controls`, `clip_r1 20`, `clip_r2 15`, EvolvDx QC steps on | +| `evolvdx_test` | Small NSQCAM529 subsample from S3, EvolvDx steps and Picard HS report on | +| `evolvdx_aws`, `evolvdx_aws_500gb` | AWS Batch queues (`conf/evolvdx/`) | + +Plain nf-core behaviour is unchanged unless one of the flags below is set. + +## What was added + +| Flag | Component | Notes | +|---|---|---| +| `--run_methylqc` | `subworkflows/local/methylqc` | Cohort-level methylKit/HDF5 merge, control stats, read stats, tiled stats, Twist and repeat annotation, per-chromosome histograms, all summarised in the MultiQC report ("EvolvDx methylation QC") with the raw tables and plots in `methqc/`. The samplesheet's optional `group` column sets the methylKit treatment groups. Needs `--aligner bwameth`; runs a second MethylDackel extraction for the methylKit files (do not also pass `--methyl_kit`, which would suppress the bedGraph output). | +| `--run_cpg_coverage` | `subworkflows/local/cpg_cov` | CpG island intersect and per-base coverage. Needs `--cpg_island_bed`. | +| `--run_methsnsv` | `modules/local/exo/methsnsv.nf` | Methylation SNV/SV statistics from MethylDackel bedGraphs. | +| `--run_picardhs_report` | `modules/local/exo/custom_multiqc` | EvolvDx Picard HS tables in MultiQC. Needs `--run_targeted_sequencing --collecthsmetrics`. | + +Also: `conf/evolvdx/custom_genomes.config` (S3 genomes under `--exogenomes_base`), `bin/` analysis +scripts, `assets/multiqc/` headers, `env/` Dockerfiles, `analysis/notebooks/`, `scripts/`, and +`tests/samplesheets`, `tests/scripts`. + +## Deliberately not carried over from the old fork + +- `PICARD_HS` subworkflow and local Picard modules: replaced by upstream `TARGETED_SEQUENCING` + (`--run_targeted_sequencing --target_regions_file ... --collecthsmetrics`). Upstream uses one + regions file for both bait and target; the old fork had separate `probe_bed` and `target_bed`. +- `SUBREAD_FEATURECOUNTS` (gene counts on the methylation BAMs). +- `cegx` and `epignome` library presets (use explicit `--clip_r1/--clip_r2/...`). +- `with_hybcap`, `probe_bed`, `target_bed`, `fai` params, and the `max_cpus/max_memory/max_time` params. + +## Known upstream issues (present in nf-core/methylseq 4.2.0 itself) + +`nextflow lint` reports errors in `modules/nf-core/rastair/mbiasparser/main.nf`, the `aws_batch` +profile include (`conf/aws/batch/nextflow.config` is missing) and `nf-test.config`. diff --git a/analysis/notebooks/basic_read_statistics.ipynb b/analysis/notebooks/basic_read_statistics.ipynb new file mode 100644 index 000000000..c81f5a864 --- /dev/null +++ b/analysis/notebooks/basic_read_statistics.ipynb @@ -0,0 +1,1440 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def insert_bedGraph_into_HDF5(run_id:str, base_data_directory:str, rep_file:str, rep_name:str, all_chr_names:list, tab_filtL_hdf5, dt):\n", + "\n", + " #try:\n", + " # tab = pd.read_table(rep_file, header=None, skiprows=1)\n", + " #except:\n", + " # tab = pd.read_table(base_data_directory+run_id+'/'+rep_file, header=None, skiprows=1)\n", + " tab = pd.read_table(base_data_directory+run_id+'/'+rep_file, header=None, skiprows=1)\n", + " tab.columns = ['chr','coord0','coord1','coverage','meth_reads','unmeth_reads']\n", + " read_num = tab.loc[:,'meth_reads'].add(tab.loc[:,'unmeth_reads']);\n", + " tab_filt = tab.where(tab.loc[:,'chr'].isin(all_chr_names)).dropna();\n", + "\n", + " shape_param = (tab_filt.shape[0], tab_filt.shape[1]-1)\n", + " dset = tab_filtL_hdf5.create_group(rep_name)\n", + " dset = tab_filtL_hdf5.create_dataset(rep_name+'/'+tab_filt.columns[0], shape=(shape_param[0],), dtype=dt, compression='gzip')\n", + " dset = tab_filtL_hdf5.create_dataset(rep_name+'/data', shape=shape_param, dtype='i', compression='gzip')\n", + "\n", + " tab_filtL_hdf5[rep_name+'/'+tab_filt.columns[0]][...]=tab_filt.iloc[:,0].values.astype(dt)\n", + " tab_filtL_hdf5[rep_name+'/data'][...]=tab_filt.iloc[:,1:].values" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def insert_bedGraph_into_HDF5(bedGraph_file:str, rep_name:str, all_chr_names:list, tab_filtL_hdf5, dt):\n", + "\n", + " #try:\n", + " # tab = pd.read_table(rep_file, header=None, skiprows=1)\n", + " #except:\n", + " # tab = pd.read_table(base_data_directory+run_id+'/'+rep_file, header=None, skiprows=1)\n", + " tab = pd.read_table(bedGraph_file, header=None, skiprows=1)\n", + " tab.columns = ['chr','coord0','coord1','coverage','meth_reads','unmeth_reads']\n", + " read_num = tab.loc[:,'meth_reads'].add(tab.loc[:,'unmeth_reads']);\n", + " tab_filt = tab.where(tab.loc[:,'chr'].isin(all_chr_names)).dropna();\n", + "\n", + " shape_param = (tab_filt.shape[0], tab_filt.shape[1]-1)\n", + " dset = tab_filtL_hdf5.create_group(rep_name)\n", + " dset = tab_filtL_hdf5.create_dataset(rep_name+'/'+tab_filt.columns[0], shape=(shape_param[0],), dtype=dt, compression='gzip')\n", + " dset = tab_filtL_hdf5.create_dataset(rep_name+'/data', shape=shape_param, dtype='i', compression='gzip')\n", + "\n", + " tab_filtL_hdf5[rep_name+'/'+tab_filt.columns[0]][...]=tab_filt.iloc[:,0].values.astype(dt)\n", + " tab_filtL_hdf5[rep_name+'/data'][...]=tab_filt.iloc[:,1:].values" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "with h5py.File(\"Healthy_1_100k.tab_filtL.hdf5\", \"w\") as tab_filtL_hdf5:\n", + "\tdt = h5py.string_dtype(encoding='utf-8')\n", + "\tinsert_bedGraph_into_HDF5(\n", + "\t\t\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_100k.markdup.sorted_CpG.bedGraph\",\n", + "\t\t\"Healthy_1_100k\",\n", + "\t\t['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19', 'chr20', 'chr21', 'chr22', 'chrX', 'chrY', 'chrM'],\n", + "\t\ttab_filtL_hdf5,\n", + "\t\tdt\n", + "\t)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So that seems to work in general? I think the next thing to do is just test out that I can take a list of files and merge those into an hdf5 file." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "with h5py.File(\"Healthy_1_100k.tab_filtL.hdf5\", \"w\") as tab_filtL_hdf5:\n", + "\tdt = h5py.string_dtype(encoding='utf-8')\n", + "\tinsert_bedGraph_into_HDF5(\n", + "\t\t\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_100k.markdup.sorted_CpG.bedGraph\",\n", + "\t\t\"Healthy_1_100k\",\n", + "\t\t['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19', 'chr20', 'chr21', 'chr22', 'chrX', 'chrY', 'chrM'],\n", + "\t\ttab_filtL_hdf5,\n", + "\t\tdt\n", + "\t)\n", + "\n", + "\tinsert_bedGraph_into_HDF5(\n", + "\t\t\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_200k.markdup.sorted_CpG.bedGraph\",\n", + "\t\t\"Healthy_1_200k\",\n", + "\t\t['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19', 'chr20', 'chr21', 'chr22', 'chrX', 'chrY', 'chrM'],\n", + "\t\ttab_filtL_hdf5,\n", + "\t\tdt\n", + "\t)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great, so in theory that should work for a list of file and IDs. Unfortunately it's going to have to be some sort of hacky way of grabbing a list [file1, and file2] and linking with [id1, id2]. But that would probably have to be done regardless in some sort of gather step. \n", + "\n", + "I can create a simple pandas dataframe to iterate through and then maybe write that as an output?\n", + "\n", + "So something like:\n", + "\n", + "```python\n", + "pd.Dataframe(\n", + "\t\"id\": id_list,\n", + "\t\"file\": file_list\n", + ")\n", + "```\n", + "\n", + "Just for my own sanity.." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "file_list = [\n", + "\t\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_100k.markdup.sorted_CpG.bedGraph\",\n", + "\t\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_200k.markdup.sorted_CpG.bedGraph\"\n", + "]\n", + "id_list = [\"Healthy_1_100k\",\"Healthy_1_200k\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idfile
0Healthy_1_100k/efs/scratch/jeffrey/code/github.com/exosomedx...
1Healthy_1_200k/efs/scratch/jeffrey/code/github.com/exosomedx...
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" + ], + "text/plain": [ + " id file\n", + "0 Healthy_1_100k /efs/scratch/jeffrey/code/github.com/exosomedx...\n", + "1 Healthy_1_200k /efs/scratch/jeffrey/code/github.com/exosomedx..." + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file_id_df = pd.DataFrame.from_dict({\n", + "\t\t\"id\": id_list,\n", + "\t\t\"file\": file_list\n", + "\t})\n", + "file_id_df" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "chromosomes_to_keep = ['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19', 'chr20', 'chr21', 'chr22', 'chrX', 'chrY', 'chrM']\n", + "with h5py.File(\"tab_filtL.hdf5\", \"w\") as tab_filtL_hdf5:\n", + "\tdt = h5py.string_dtype(encoding=\"utf-8\")\n", + "\t\n", + "\tfor index, row in file_id_df.iterrows():\n", + "\t\tinsert_bedGraph_into_HDF5(\n", + "\t\t\trow[\"file\"], \n", + "\t\t\trow[\"id\"],\n", + "\t\t\tchromosomes_to_keep,\n", + "\t\t\ttab_filtL_hdf5,\n", + "\t\t\tdt\n", + "\t\t)\t\n", + " \t" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['healthy_1', 'healthy_2', 'disease_1', 'disease_2']\n" + ] + } + ], + "source": [ + "import re\n", + "\n", + "# Your input string\n", + "input_string = \"[[id:healthy_1, group:healthy], [id:healthy_2, group:healthy], [id:disease_1, group:disease], [id:disease_2, group:disease]]\"\n", + "\n", + "# Extracting id values using regex\n", + "id_values = re.findall(r'id:([^,\\]]+)', input_string)\n", + "\n", + "# Displaying the list of id values\n", + "print(id_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_100k.markdup.sorted_CpG.bedGraph',\n", + " '/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_200k.markdup.sorted_CpG.bedGraph']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file_list = \"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_100k.markdup.sorted_CpG.bedGraph /efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_200k.markdup.sorted_CpG.bedGraph\".split()\n", + "file_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So now I just need to read in the hdf5 file and do stats and stuff to it!" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "tab_id_value_countsL = []\n", + "\n", + "rnf_countsL = []\n", + "rnf_medianL = []\n", + "rnf_meanL = []\n", + "\n", + "with h5py.File(\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methylqc/tab_filtL.hdf5\", \"r\") as tab_filtL_hdf5:\n", + "\tfor index, row in file_id_df.iterrows():\n", + "\t\ttab_id = tab_filtL_hdf5[row[\"id\"] +'/data'][:,2]\n", + "\t\ttab_id_value_counts = np.unique(tab_id, return_counts=True)\n", + "\t\treplicate_name_array = [row[\"id\"] for _ in tab_id_value_counts[0]]\n", + "\t\ttab_id_value_counts_df = pd.DataFrame(data={\"coverage\":tab_id_value_counts[0], \"count\":tab_id_value_counts[1], \"replicate\":replicate_name_array})\n", + "\t\ttab_id_value_countsL.append(tab_id_value_counts_df)\n", + "\n", + "\t\tread_num_filt = tab_filtL_hdf5[row[\"id\"]+'/data'][:,3]+tab_filtL_hdf5[row[\"id\"]+'/data'][:,4]\n", + "\t\trnf_value_counts = np.unique(read_num_filt, return_counts=True)\n", + "\t\trnf_value_counts_df = pd.DataFrame(rnf_value_counts, index=['read count','number of read count occurrences']).T\n", + "\t\trnf_countsL.append(rnf_value_counts_df)\n", + "\n", + "\t\trnf_medianL.append(np.median(read_num_filt))\n", + "\t\trnf_meanL.append(read_num_filt.mean())\n", + "\n", + "\t\t# mbias_case = mbias_ls[i]\n", + "\t\t# mbias_data = pd.read_csv(mbias_case, sep='\\t')\n", + "\t\t# mbias_dataL.append(mbias_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "mbias_file_str = \"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/mbias/Healthy_1_100k.mbias.txt /efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/mbias/Healthy_1_200k.mbias.txt\"\n", + "mbias_files = mbias_file_str.split()\n", + "mbias_dataL = [pd.read_csv(mbias_file, sep = '\\t') for mbias_file in mbias_files]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So I modified the mbias stuff.. but it seems like this is literally just a merge of the mbias files?? I guess it's fine to just grab all the mbias files here and throw them into a dataframe. I think we can get the id names from the mbias meta, but I'll have to check that." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def rep_id_series(tab: pd.DataFrame, rep_name: str, series_name: str = 'replicate') -> pd.Series:\n", + " ''' tab: pd.DataFrame \\n rep_name: str \\n Make a Pandas Series of the replicate name rep_name for a replicate with DataFrame tab that shares its index. '''\n", + " return pd.Series([rep_name for _ in tab.index], index = tab.index, name = series_name)\n", + "def map_rep_id_series(tab_filtL: list, rep_list: list) -> list:\n", + " ''' tab_filtL: A list of pd.DataFrame that is the chromosome-filtered BedGraph data \\n rep_list: List of all rep names in pd.Series format. \\n Create a list containing the replicate ID to all of the pd.DataFrame in tab_filtL. Assumes that the list of pd.DataFrame and the list of replicates are the same length and order.'''\n", + " return list(map(lambda tab, rep_name: rep_id_series(tab, rep_name), tab_filtL, rep_list))\n", + "def concat_ids_onto_df_list(tabL: list, rep_seriesL: list) -> list:\n", + " ''' tab_filtL: A list of pd.DataFrame that is the chromosome-filtered BedGraph data \\n rep_list: List of all rep names in pd.Series format. \\n\n", + " Append a column containing the replicate ID to all of the pd.DataFrame in tab_filtL.\n", + " Assumes that the list of pd.DataFrame and the list of replicate pd.Series are the same\n", + " length and same order.'''\n", + " return list(map(lambda tab, rep_series: pd.concat([tab, rep_series], axis=1), tabL, rep_seriesL))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1068 rows × 6 columns

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" + ], + "text/plain": [ + " Strand Read Position nMethylated nUnmethylated replicate\n", + "0 OT 1 1 785 949 Healthy_1_100k\n", + "1 OT 2 1 1127 430 Healthy_1_100k\n", + "2 OT 1 2 779 1032 Healthy_1_100k\n", + "3 OT 2 2 632 859 Healthy_1_100k\n", + "4 OT 1 3 857 995 Healthy_1_100k\n", + ".. ... ... ... ... ... ...\n", + "529 OB 2 132 1219 1339 Healthy_1_200k\n", + "530 OB 2 133 1228 1209 Healthy_1_200k\n", + "531 OB 2 134 1205 1180 Healthy_1_200k\n", + "532 OB 2 135 1144 927 Healthy_1_200k\n", + "533 OB 2 136 910 0 Healthy_1_200k\n", + "\n", + "[1068 rows x 6 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rep_series_mbiasL = map_rep_id_series(mbias_dataL, file_id_df[\"id\"])\n", + "mbias_data_idL = concat_ids_onto_df_list(mbias_dataL, rep_series_mbiasL)\n", + "mbias_data_id_df = pd.concat(mbias_data_idL, axis=0)\n", + "mbias_data_id_df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexcoveragecountreplicate
000244335Healthy_1_100k
11258Healthy_1_100k
223377Healthy_1_100k
33502960Healthy_1_100k
44601Healthy_1_100k
5566107Healthy_1_100k
66752Healthy_1_100k
77801Healthy_1_100k
88100210540Healthy_1_100k
900461754Healthy_1_200k
101204Healthy_1_200k
1122534Healthy_1_200k
123281Healthy_1_200k
13433467Healthy_1_200k
145406Healthy_1_200k
156422Healthy_1_200k
1675010201Healthy_1_200k
178572Healthy_1_200k
1896014Healthy_1_200k
191066824Healthy_1_200k
2011712Healthy_1_200k
21127576Healthy_1_200k
22138012Healthy_1_200k
2314837Healthy_1_200k
2415851Healthy_1_200k
2516100402543Healthy_1_200k
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" + ], + "text/plain": [ + " index coverage count replicate\n", + "0 0 0 244335 Healthy_1_100k\n", + "1 1 25 8 Healthy_1_100k\n", + "2 2 33 77 Healthy_1_100k\n", + "3 3 50 2960 Healthy_1_100k\n", + "4 4 60 1 Healthy_1_100k\n", + "5 5 66 107 Healthy_1_100k\n", + "6 6 75 2 Healthy_1_100k\n", + "7 7 80 1 Healthy_1_100k\n", + "8 8 100 210540 Healthy_1_100k\n", + "9 0 0 461754 Healthy_1_200k\n", + "10 1 20 4 Healthy_1_200k\n", + "11 2 25 34 Healthy_1_200k\n", + "12 3 28 1 Healthy_1_200k\n", + "13 4 33 467 Healthy_1_200k\n", + "14 5 40 6 Healthy_1_200k\n", + "15 6 42 2 Healthy_1_200k\n", + "16 7 50 10201 Healthy_1_200k\n", + "17 8 57 2 Healthy_1_200k\n", + "18 9 60 14 Healthy_1_200k\n", + "19 10 66 824 Healthy_1_200k\n", + "20 11 71 2 Healthy_1_200k\n", + "21 12 75 76 Healthy_1_200k\n", + "22 13 80 12 Healthy_1_200k\n", + "23 14 83 7 Healthy_1_200k\n", + "24 15 85 1 Healthy_1_200k\n", + "25 16 100 402543 Healthy_1_200k" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tab_id_value_counts_df = pd.concat(tab_id_value_countsL).reset_index()\n", + "tab_id_value_counts_df" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "rep_series_rnfL = map_rep_id_series(rnf_countsL, file_id_df[\"id\"])\n", + "rnf_counts_idL = concat_ids_onto_df_list(rnf_countsL, rep_series_rnfL)\n", + "rnf_counts_id_df = pd.concat(rnf_counts_idL, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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read countnumber of read count occurrencesreplicate
01437906Healthy_1_100k
1219237Healthy_1_100k
23853Healthy_1_100k
3433Healthy_1_100k
452Healthy_1_100k
01802861Healthy_1_200k
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34490Healthy_1_200k
4575Healthy_1_200k
5615Healthy_1_200k
679Healthy_1_200k
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" + ], + "text/plain": [ + " read count number of read count occurrences replicate\n", + "0 1 437906 Healthy_1_100k\n", + "1 2 19237 Healthy_1_100k\n", + "2 3 853 Healthy_1_100k\n", + "3 4 33 Healthy_1_100k\n", + "4 5 2 Healthy_1_100k\n", + "0 1 802861 Healthy_1_200k\n", + "1 2 66930 Healthy_1_200k\n", + "2 3 5570 Healthy_1_200k\n", + "3 4 490 Healthy_1_200k\n", + "4 5 75 Healthy_1_200k\n", + "5 6 15 Healthy_1_200k\n", + "6 7 9 Healthy_1_200k" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rnf_counts_id_df" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "report_table_precision = 3\n", + "tab_count_by_rep = tab_id_value_counts_df.loc[:,[\"coverage\", \"replicate\"]].groupby(\"replicate\").count()\n", + "lt100_tab_count_by_rep = tab_id_value_counts_df.where(tab_id_value_counts_df.loc[:,'coverage'].lt(100)).dropna().groupby(\"replicate\").count()\n", + "percent_partial_read_coverage = lt100_tab_count_by_rep.div(tab_count_by_rep).mul(100).loc[:,'coverage']\n", + "percent_partial_read_coverage = percent_partial_read_coverage.map(lambda x: np.round(x, report_table_precision))\n", + "percent_partial_read_coverage.name = '% of total reads that are partial'" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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coverage
replicate
Healthy_1_100k9
Healthy_1_200k17
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" + ], + "text/plain": [ + " coverage\n", + "replicate \n", + "Healthy_1_100k 9\n", + "Healthy_1_200k 17" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tab_count_by_rep" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexcoveragecount
replicate
Healthy_1_100k888
Healthy_1_200k161616
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" + ], + "text/plain": [ + " index coverage count\n", + "replicate \n", + "Healthy_1_100k 8 8 8\n", + "Healthy_1_200k 16 16 16" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lt100_tab_count_by_rep" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "replicate\n", + "Healthy_1_100k 88.889\n", + "Healthy_1_200k 94.118\n", + "Name: % of total reads that are partial, dtype: float64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "percent_partial_read_coverage" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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% of total reads that are partialmedian read #mean read #
Healthy_1_100k88.8891.01.045958
Healthy_1_200k94.1181.01.091294
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" + ], + "text/plain": [ + " % of total reads that are partial median read # mean read #\n", + "Healthy_1_100k 88.889 1.0 1.045958\n", + "Healthy_1_200k 94.118 1.0 1.091294" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics_table = pd.concat([percent_partial_read_coverage, pd.DataFrame({'median read #':rnf_medianL, 'mean read #':rnf_meanL}, index=file_id_df[\"id\"])], axis=1)\n", + "metrics_table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def print_header(header_text:str, header_text_size:int = 20) -> None:\n", + " fig = plt.figure(figsize=(5, 1.5))\n", + " ax = fig.add_axes([0, 0, 0, 0])\n", + " ax.text(0.5, 0.5, header_text, ha='center', va='center', size=header_text_size)\n", + " ax.set_axis_off()\n", + "\n", + "def output_basic_read_statistics(rnf_counts_id_df:pd.DataFrame,\n", + " mbias_data_id_df:pd.DataFrame,\n", + " tab_id_value_counts_df:pd.DataFrame,\n", + " metrics_table:pd.DataFrame,\n", + " rnf_apart:bool = True,\n", + " rep_designator:str = 'replicate',\n", + " statistic:str = 'coverage') -> None:\n", + "\n", + "\tprint('\\n')\n", + "\tprint('---------- Coverage Statistics ----------')\n", + "\tprint(metrics_table)\n", + "\tprint('\\n')\n", + "\n", + "\t# Combined Coverage Distribution\n", + "\tprint_header('Combined Coverage Distributions')\n", + "\tf,ax = plt.subplots()\n", + "\tsns.lineplot(data=tab_id_value_counts_df, x=statistic, y='count', hue=rep_designator, palette='Set2');\n", + "\tplt.yscale('log')\n", + "\tsns.despine(left=True, bottom=True);\n", + "\tf.savefig(\"combined_coverage_distributions.png\")\n", + "\n", + "\n", + " # Coverage distribution\n", + " # print_header('Individual Coverage Distributions')\n", + " # g = sns.FacetGrid(tab_id_value_counts_df, col=rep_designator, height=2, aspect=2, col_wrap=2)\n", + " # g.map(sns.lineplot, statistic, 'count')\n", + " # plt.yscale('log')\n", + "\n", + " # Read count occurrence histograms\n", + "\tprint_header('Read Count Occurrence Frequencies')\n", + "\tsns.lineplot(data=rnf_counts_id_df, x='read count', y='number of read count occurrences', hue='replicate', palette='Set2')\n", + "\tsns.scatterplot(data=rnf_counts_id_df, x='read count', y='number of read count occurrences', s=20, color='gray')\n", + "\tplt.xscale('log')\n", + "\tif rnf_apart:\n", + "\t\tg = sns.FacetGrid(rnf_counts_id_df.reset_index(), col=rep_designator, height=4, aspect=2, col_wrap=2)\n", + "\t\tg.map(sns.lineplot, 'read count', 'number of read count occurrences')\n", + "\t\tg.map(sns.scatterplot, 'read count', 'number of read count occurrences', s=20, color='gray')\n", + "\t\tplt.xscale('log')\n", + "\tplt.savefig(\"read_count_occurence_frequencies.png\")\n", + "\n", + " # Read bias plots: Methylated OT Strand\n", + "\tprint_header('Read Bias in Methylated OT Strand')\n", + "\tg = sns.FacetGrid(mbias_data_id_df.where(mbias_data_id_df.loc[:,'Strand'].eq('OT')), col=rep_designator, height=4, aspect=2, col_wrap=2, hue='Read', palette='Set1', sharey=False)\n", + "\tg.map(sns.lineplot, 'Position', 'nMethylated')\n", + "\tplt.ylim(bottom=0)\n", + "\tg.add_legend()\n", + "\tplt.savefig(\"read_bias_in_methylated_OT_strand.png\")\n", + "\n", + " # Read bias plots: Methylated OB Strand\n", + "\tprint_header('Read Bias in Methylated OB Strand')\n", + "\tg = sns.FacetGrid(mbias_data_id_df.where(mbias_data_id_df.loc[:,'Strand'].eq('OB')), col=rep_designator, height=4, aspect=2, col_wrap=2, hue='Read', palette='Set1', sharey=False)\n", + "\tg.map(sns.lineplot, 'Position', 'nMethylated')\n", + "\tplt.ylim(bottom=0)\n", + "\tg.add_legend()\n", + "\tplt.savefig(\"read_bias_in_methylated_OB_strand.png\")\n", + "\n", + " # Read bias plots: Unmethylated OT Strand\n", + "\tprint_header('Read Bias in Unmethylated OT Strand')\n", + "\tg = sns.FacetGrid(mbias_data_id_df.where(mbias_data_id_df.loc[:,'Strand'].eq('OT')), col=rep_designator, height=4, aspect=2, col_wrap=2, hue='Read', palette='Set1', sharey=False)\n", + "\tg.map(sns.lineplot, 'Position', 'nUnmethylated')\n", + "\tplt.ylim(bottom=0)\n", + "\tg.add_legend()\n", + "\tplt.savefig(\"read_bias_in_unmethylated_OT_strand.png\")\n", + "\n", + " # Read bias plots: Unmethylated OB Strand\n", + "\tprint_header('Read Bias in Unmethylated OB Strand')\n", + "\tg = sns.FacetGrid(mbias_data_id_df.where(mbias_data_id_df.loc[:,'Strand'].eq('OB')), col=rep_designator, height=4, aspect=2, col_wrap=2, hue='Read', palette='Set1', sharey=False)\n", + "\tg.map(sns.lineplot, 'Position', 'nUnmethylated')\n", + "\tplt.ylim(bottom=0)\n", + "\tg.add_legend()\n", + "\tplt.savefig(\"read_bias_in_unmethylated_OB_strand.png\")\n", + "# Output catch-all read statistics ##########" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "---------- Coverage Statistics ----------\n", + " % of total reads that are partial median read # mean read #\n", + "Healthy_1_100k 88.889 1.0 1.045958\n", + "Healthy_1_200k 94.118 1.0 1.091294\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "output_basic_read_statistics(rnf_counts_id_df,mbias_data_id_df,tab_id_value_counts_df,metrics_table)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "015455a9c6be9e7274ccecdbf26c5e74b1ea1b6d228cb963172aa34f90a4e9f8" + }, + "kernelspec": { + "display_name": "Python 3.10.2 ('methylseq')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/analysis/notebooks/control_stats.ipynb b/analysis/notebooks/control_stats.ipynb new file mode 100644 index 000000000..8224fa462 --- /dev/null +++ b/analysis/notebooks/control_stats.ipynb @@ -0,0 +1,699 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext rpy2.ipython" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──\n", + "\n", + "R[write to console]: ✔ ggplot2 3.4.2 ✔ purrr 1.0.1\n", + "✔ tibble 3.2.1 ✔ dplyr 1.1.2\n", + "✔ tidyr 1.3.0 ✔ stringr 1.5.0\n", + "✔ readr 2.1.4 ✔ forcats 1.0.0\n", + "\n", + "R[write to console]: ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n", + "✖ dplyr::collapse() masks IRanges::collapse()\n", + "✖ dplyr::combine() masks BiocGenerics::combine()\n", + "✖ dplyr::desc() masks IRanges::desc()\n", + "✖ tidyr::expand() masks S4Vectors::expand()\n", + "✖ dplyr::filter() masks stats::filter()\n", + "✖ dplyr::first() masks S4Vectors::first()\n", + "✖ dplyr::lag() masks stats::lag()\n", + "✖ ggplot2::Position() masks BiocGenerics::Position(), base::Position()\n", + "✖ purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()\n", + "✖ dplyr::rename() masks S4Vectors::rename()\n", + "✖ dplyr::select() masks methylKit::select()\n", + "✖ dplyr::slice() masks IRanges::slice()\n", + "✖ tidyr::unite() masks methylKit::unite()\n", + "\n" + ] + } + ], + "source": [ + "%%R \n", + "library(tidyverse)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import rpy2\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "methylRaw object with 516 rows\n", + "--------------\n", + " chr start end strand coverage numCs numTs\n", + "1 phage_lambda 1851 1851 + 12 0 12\n", + "2 phage_lambda 1870 1870 + 13 0 13\n", + "3 phage_lambda 1874 1874 + 12 0 12\n", + "4 phage_lambda 1884 1884 + 12 0 12\n", + "5 phage_lambda 1901 1901 + 11 0 11\n", + "6 phage_lambda 1904 1904 + 12 0 12\n", + "--------------\n", + "sample.id: Healthy_1-1 \n", + "assembly: hg38 \n", + "context: CpG \n", + "resolution: base \n", + "\n" + ] + } + ], + "source": [ + "%%R \n", + "methobj_R <- readRDS(\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k/methylqc/methobj.rds\")\n", + "methobj_R" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "methylRawList object with 2 methylRaw objects\n", + "\n", + "methylRaw object with 1556 rows\n", + "--------------\n", + " chr start end strand coverage numCs numTs\n", + "1 phage_lambda 367 367 + 10 0 10\n", + "2 phage_lambda 377 377 + 10 0 10\n", + "3 phage_lambda 380 380 + 10 0 10\n", + "4 phage_lambda 398 398 + 11 0 11\n", + "5 phage_lambda 409 409 + 12 0 12\n", + "6 phage_lambda 442 442 + 12 0 12\n", + "--------------\n", + "sample.id: Healthy_1_200k \n", + "assembly: hg38 \n", + "context: CpG \n", + "resolution: base \n", + "\n", + "methylRaw object with 516 rows\n", + "--------------\n", + " chr start end strand coverage numCs numTs\n", + "1 phage_lambda 1851 1851 + 12 0 12\n", + "2 phage_lambda 1870 1870 + 13 0 13\n", + "3 phage_lambda 1874 1874 + 12 0 12\n", + "4 phage_lambda 1884 1884 + 12 0 12\n", + "5 phage_lambda 1901 1901 + 11 0 11\n", + "6 phage_lambda 1904 1904 + 12 0 12\n", + "--------------\n", + "sample.id: Healthy_1_100k \n", + "assembly: hg38 \n", + "context: CpG \n", + "resolution: base \n", + "\n", + "treatment: 0 0 \n" + ] + } + ], + "source": [ + "%%R \n", + "methobj_R <- readRDS(\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methylqc/methobj.rds\")\n", + "methobj_R" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Healthy_1_200k\" \"Healthy_1_100k\"\n" + ] + } + ], + "source": [ + "%%R\n", + "getSampleID(methobj_R)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, : \n", + " arguments imply differing number of rows: 1556, 516\n", + "\n", + "R[write to console]: In addition: \n", + "R[write to console]: There were 11 warnings (use warnings() to see them)\n", + "R[write to console]: \n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, : \n", + " arguments imply differing number of rows: 1556, 516\n" + ] + }, + { + "ename": "RInterpreterError", + "evalue": "Failed to parse and evaluate line 'methobj_R_tibble <- as_tibble(data.frame(methobj_R))\\nmethobj_R_tibble\\n'.\nR error message: 'Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, : \\n arguments imply differing number of rows: 1556, 516'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRRuntimeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:280\u001b[0m, in \u001b[0;36mRMagics.eval\u001b[0;34m(self, code)\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 279\u001b[0m \u001b[39m# Need the newline in case the last line in code is a comment.\u001b[39;00m\n\u001b[0;32m--> 280\u001b[0m value, visible \u001b[39m=\u001b[39m ro\u001b[39m.\u001b[39;49mr(\u001b[39m\"\u001b[39;49m\u001b[39mwithVisible(\u001b[39;49m\u001b[39m{\u001b[39;49m\u001b[39m%s\u001b[39;49;00m\u001b[39m\\n\u001b[39;49;00m\u001b[39m})\u001b[39;49m\u001b[39m\"\u001b[39;49m \u001b[39m%\u001b[39;49m code)\n\u001b[1;32m 281\u001b[0m \u001b[39mexcept\u001b[39;00m (ri\u001b[39m.\u001b[39membedded\u001b[39m.\u001b[39mRRuntimeError, \u001b[39mValueError\u001b[39;00m) \u001b[39mas\u001b[39;00m exception:\n\u001b[1;32m 282\u001b[0m \u001b[39m# Otherwise next return seems to have copy of error.\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/__init__.py:451\u001b[0m, in \u001b[0;36mR.__call__\u001b[0;34m(self, string)\u001b[0m\n\u001b[1;32m 450\u001b[0m p \u001b[39m=\u001b[39m rinterface\u001b[39m.\u001b[39mparse(string)\n\u001b[0;32m--> 451\u001b[0m res \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49meval(p)\n\u001b[1;32m 452\u001b[0m \u001b[39mreturn\u001b[39;00m conversion\u001b[39m.\u001b[39mrpy2py(res)\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/functions.py:201\u001b[0m, in \u001b[0;36mSignatureTranslatedFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 200\u001b[0m kwargs[r_k] \u001b[39m=\u001b[39m v\n\u001b[0;32m--> 201\u001b[0m \u001b[39mreturn\u001b[39;00m (\u001b[39msuper\u001b[39;49m(SignatureTranslatedFunction, \u001b[39mself\u001b[39;49m)\n\u001b[1;32m 202\u001b[0m \u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs))\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/functions.py:124\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 123\u001b[0m new_kwargs[k] \u001b[39m=\u001b[39m conversion\u001b[39m.\u001b[39mpy2rpy(v)\n\u001b[0;32m--> 124\u001b[0m res \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m(Function, \u001b[39mself\u001b[39;49m)\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(\u001b[39m*\u001b[39;49mnew_args, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mnew_kwargs)\n\u001b[1;32m 125\u001b[0m res \u001b[39m=\u001b[39m conversion\u001b[39m.\u001b[39mrpy2py(res)\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/rinterface_lib/conversion.py:45\u001b[0m, in \u001b[0;36m_cdata_res_to_rinterface.._\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m---> 45\u001b[0m cdata \u001b[39m=\u001b[39m function(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 46\u001b[0m \u001b[39m# TODO: test cdata is of the expected CType\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/rinterface.py:810\u001b[0m, in \u001b[0;36mSexpClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[39mif\u001b[39;00m error_occured[\u001b[39m0\u001b[39m]:\n\u001b[0;32m--> 810\u001b[0m \u001b[39mraise\u001b[39;00m embedded\u001b[39m.\u001b[39mRRuntimeError(_rinterface\u001b[39m.\u001b[39m_geterrmessage())\n\u001b[1;32m 811\u001b[0m \u001b[39mreturn\u001b[39;00m res\n", + "\u001b[0;31mRRuntimeError\u001b[0m: Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, : \n arguments imply differing number of rows: 1556, 516\n", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mRInterpreterError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[61], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mR\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmethobj_R_tibble <- as_tibble(data.frame(methobj_R))\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43mmethobj_R_tibble\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/IPython/core/interactiveshell.py:2517\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2515\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuiltin_trap:\n\u001b[1;32m 2516\u001b[0m args \u001b[39m=\u001b[39m (magic_arg_s, cell)\n\u001b[0;32m-> 2517\u001b[0m result \u001b[39m=\u001b[39m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 2519\u001b[0m \u001b[39m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2520\u001b[0m \u001b[39m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2521\u001b[0m \u001b[39m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2522\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(fn, magic\u001b[39m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[39mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:795\u001b[0m, in \u001b[0;36mRMagics.R\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 793\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m e\u001b[39m.\u001b[39mstdout\u001b[39m.\u001b[39mendswith(e\u001b[39m.\u001b[39merr):\n\u001b[1;32m 794\u001b[0m \u001b[39mprint\u001b[39m(e\u001b[39m.\u001b[39merr)\n\u001b[0;32m--> 795\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m 796\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 797\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdevice \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39mpng\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39msvg\u001b[39m\u001b[39m'\u001b[39m]:\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:775\u001b[0m, in \u001b[0;36mRMagics.R\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 773\u001b[0m return_output \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 774\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 775\u001b[0m text_result, result, visible \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49meval(code)\n\u001b[1;32m 776\u001b[0m text_output \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m text_result\n\u001b[1;32m 777\u001b[0m \u001b[39mif\u001b[39;00m visible:\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:284\u001b[0m, in \u001b[0;36mRMagics.eval\u001b[0;34m(self, code)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[39mexcept\u001b[39;00m (ri\u001b[39m.\u001b[39membedded\u001b[39m.\u001b[39mRRuntimeError, \u001b[39mValueError\u001b[39;00m) \u001b[39mas\u001b[39;00m exception:\n\u001b[1;32m 282\u001b[0m \u001b[39m# Otherwise next return seems to have copy of error.\u001b[39;00m\n\u001b[1;32m 283\u001b[0m warning_or_other_msg \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mflush()\n\u001b[0;32m--> 284\u001b[0m \u001b[39mraise\u001b[39;00m RInterpreterError(code, \u001b[39mstr\u001b[39m(exception),\n\u001b[1;32m 285\u001b[0m warning_or_other_msg)\n\u001b[1;32m 286\u001b[0m text_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mflush()\n\u001b[1;32m 287\u001b[0m \u001b[39mreturn\u001b[39;00m text_output, value, visible[\u001b[39m0\u001b[39m]\n", + "\u001b[0;31mRInterpreterError\u001b[0m: Failed to parse and evaluate line 'methobj_R_tibble <- as_tibble(data.frame(methobj_R))\\nmethobj_R_tibble\\n'.\nR error message: 'Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, : \\n arguments imply differing number of rows: 1556, 516'" + ] + } + ], + "source": [ + "%%R \n", + "methobj_R_tibble <- as_tibble(data.frame(methobj_R))\n", + "methobj_R_tibble\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# A tibble: 218 × 7\n", + " chr start end strand coverage numCs numTs\n", + " \n", + " 1 phage_lambda 1851 1851 + 12 0 12\n", + " 2 phage_lambda 1870 1870 + 13 0 13\n", + " 3 phage_lambda 1874 1874 + 12 0 12\n", + " 4 phage_lambda 1884 1884 + 12 0 12\n", + " 5 phage_lambda 1901 1901 + 11 0 11\n", + " 6 phage_lambda 1904 1904 + 12 0 12\n", + " 7 phage_lambda 1910 1910 + 12 0 12\n", + " 8 phage_lambda 1926 1926 + 11 0 11\n", + " 9 phage_lambda 1931 1931 + 11 0 11\n", + "10 phage_lambda 1938 1938 + 11 0 11\n", + "# ℹ 208 more rows\n", + "# ℹ Use `print(n = ...)` to see more rows\n" + ] + } + ], + "source": [ + "%%R \n", + "methobj_R_tibble %>% \n", + "\tfilter(chr == \"phage_lambda\")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# A tibble: 298 × 7\n", + " chr start end strand coverage numCs numTs\n", + " \n", + " 1 plasmid_puc19c 2 2 + 14 14 0\n", + " 2 plasmid_puc19c 3 3 - 14 12 2\n", + " 3 plasmid_puc19c 4 4 + 14 14 0\n", + " 4 plasmid_puc19c 5 5 - 16 16 0\n", + " 5 plasmid_puc19c 6 6 + 14 14 0\n", + " 6 plasmid_puc19c 7 7 - 15 15 0\n", + " 7 plasmid_puc19c 11 11 + 16 16 0\n", + " 8 plasmid_puc19c 12 12 - 21 19 2\n", + " 9 plasmid_puc19c 20 20 + 26 26 0\n", + "10 plasmid_puc19c 21 21 - 18 18 0\n", + "# ℹ 288 more rows\n", + "# ℹ Use `print(n = ...)` to see more rows\n" + ] + } + ], + "source": [ + "%%R \n", + "methobj_R_tibble %>% \n", + "\tfilter(chr == \"plasmid_puc19c\")" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# A tibble: 2 × 2\n", + " chr mean_pct_methylated\n", + " \n", + "1 phage_lambda 0.0417\n", + "2 plasmid_puc19c 97.7 \n" + ] + } + ], + "source": [ + "%%R \n", + "methobj_R_tibble %>% \n", + "\tgroup_by(chr) %>% \n", + "\tsummarize(mean_pct_methylated = mean(100 * (numCs / coverage)))" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import rpy2\n", + "from rpy2.robjects.packages import importr\n", + "methobj = rpy2.robjects.r.readRDS(\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methylqc/methobj.rds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " StrVector with 3 elements.\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " '1'\n", + " \n", + " 'character'\n", + " \n", + " 'character'\n", + "
\n", + " " + ], + "text/plain": [ + " [RTYPES.STRSXP]\n", + "R classes: ('summaryDefault', 'table')\n", + "['1', 'character', 'character']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rpy2.robjects.r.summary(\"H\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Healthy_1_200k', 'Healthy_1_100k']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "methylKit = importr('methylKit')\n", + "list(methylKit.getSampleID(methobj))" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " ListVector with 2 elements.\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " 'phage_la...\n", + " \n", + " 'phage_la...\n", + " \n", + " 'phage_la...\n", + " \n", + " ...\n", + " \n", + " 'plasmid_...\n", + " \n", + " 'plasmid_...\n", + " \n", + " 'plasmid_...\n", + "
\n", + " " + ], + "text/plain": [ + " [RTYPES.STRSXP]\n", + "R classes: ('character',)\n", + "['phage_la..., 'phage_la..., 'phage_la..., 'phage_la..., ..., 'plasmid_..., 'plasmid_..., 'plasmid_..., 'plasmid_...]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "methobj[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def output_control_stats(methobj, rep_list:list, run_id:str, base_data_directory:str, spike_in_controls:list = ['phage_lambda', 'plasmid_puc19c']) -> None:\n", + " import os\n", + " import pandas as pd\n", + "\n", + " print('\\n')\n", + " print('Percentage methylation in spike-in controls.')\n", + "\n", + " control_df_fn = base_data_directory+'/'+run_id+'/control_df.csv'\n", + "\n", + " if os.path.isfile(control_df_fn):\n", + " control_df = pd.read_csv(control_df_fn)\n", + " else:\n", + " control_df = create_control_stats_table(methobj, rep_list, spike_in_controls)\n", + " control_df.to_csv(control_df_fn)\n", + "\n", + " print(control_df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "def create_control_stats_table(methobj, spike_in_controls:list = ['phage_lambda', 'plasmid_puc19c']) -> pd.DataFrame:\n", + "\tchr_index = 0\n", + "\tcoverage_index = 4\n", + "\tnumCs_index = 5\n", + "\tpct_methylatedL = []\n", + "\trep_list = list(rpy2.robjects.r.getSampleID(methobj))\n", + "\tfor i,this_rep in enumerate(rep_list):\n", + "\t\tpct_methylatedL_aux = []\n", + "\t\tfor this_control in spike_in_controls:\n", + "\t\t\tarray_selection = np.array(methobj[i][chr_index]) == this_control\n", + "\t\t\tif np.any(array_selection):\n", + "\t\t\t\tpct_methylated = 100*np.array(methobj[i][numCs_index])[array_selection] / np.array(methobj[i][coverage_index])[array_selection]\n", + "\t\t\t\tpct_methylated = np.mean(pct_methylated)\n", + "\t\t\t\tpct_methylatedL_aux.append(pct_methylated)\n", + "\t\t\telse:\n", + "\t\t\t\tpct_methylatedL_aux.append(0)\n", + "\t\tpct_methylatedL.append(pct_methylatedL_aux)\n", + "\treturn pd.DataFrame(pct_methylatedL, index=rep_list, columns=spike_in_controls)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
phage_lambdaplasmid_puc19c
Healthy_1_200k0.26772797.647241
Healthy_1_100k0.04170197.728094
\n", + "
" + ], + "text/plain": [ + " phage_lambda plasmid_puc19c\n", + "Healthy_1_200k 0.267727 97.647241\n", + "Healthy_1_100k 0.041701 97.728094" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "create_control_stats_table(methobj)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well.. I'm not really sure if that makss sense or not.. I'll have to think on this a bit more.\n", + "\n", + "I'll try to break this down a bit. and see if I can make sense of it." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "rep_list = [\"Healthy_1-1\"]\n", + "spike_in_controls = ['phage_lambda', 'plasmid_puc19c']\n", + "\n", + "chr_index = 0\n", + "coverage_index = 4\n", + "numCs_index = 5\n", + "pct_methylatedL = []\n", + "\n", + "for i,this_rep in enumerate(rep_list):\n", + "\tpct_methylatedL_aux = []\n", + "\tfor this_control in spike_in_controls:\n", + "\t\tarray_selection = np.array(methobj[i][chr_index]) == this_control\n", + "\t\t# print(i)\n", + "\t\t# print(np.array(methobj[i][chr_index]))\n", + "\t\tif np.any(array_selection):\n", + "\t\t\tprint(array_selection, this_control)\n", + "\t\t\tprint(np.array(methobj[i][numCs_index]))\n", + "\t\t\tprint(np.array(methobj[i][numCs_index])[array_selection])\n", + "\t\n", + "\t# \t\tpct_methylated = 100*np.array(methobj[i][numCs_index])[array_selection] / np.array(methobj[i][coverage_index])[array_selection]\n", + "\t# \t\tpct_methylated = np.mean(pct_methylated)\n", + "\t# \t\tpct_methylatedL_aux.append(pct_methylated)\n", + "\t# \telse:\n", + "\t# \t\tpct_methylatedL_aux.append(0)\n", + "\t# pct_methylatedL.append(pct_methylatedL_aux)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "015455a9c6be9e7274ccecdbf26c5e74b1ea1b6d228cb963172aa34f90a4e9f8" + }, + "kernelspec": { + "display_name": "Python 3.10.2 ('methylseq')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/analysis/notebooks/input_methylkit_meta.ipynb b/analysis/notebooks/input_methylkit_meta.ipynb new file mode 100644 index 000000000..0fabd4222 --- /dev/null +++ b/analysis/notebooks/input_methylkit_meta.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext rpy2.ipython" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"[healthy_1.methylkit, healthy_2.methylkit, disease_1.methylkit, disease_2.methylkit]\"\n" + ] + } + ], + "source": [ + "%%R \n", + "methylkit_files <- \"[healthy_1.methylkit, healthy_2.methylkit, disease_1.methylkit, disease_2.methylkit]\"\n", + "methylkit_files\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"healthy_1.methylkit\" \"healthy_2.methylkit\" \"disease_1.methylkit\"\n", + "[4] \"disease_2.methylkit\"\n" + ] + } + ], + "source": [ + "%%R\n", + "# Removing square brackets from the string\n", + "cleaned_string <- gsub(\"^\\\\[|\\\\]$\", \"\", methylkit_files)\n", + "\n", + "cleaned_string\n", + "\n", + "# Splitting the string into elements\n", + "split_elements <- unlist(strsplit(cleaned_string, \",\\\\s*\"))\n", + "\n", + "# Displaying the vector\n", + "result_vector <- split_elements\n", + "result_vector" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"healthy_1.methylkit\" \"healthy_2.methylkit\" \"disease_1.methylkit\"\n", + "[4] \"disease_2.methylkit\"\n" + ] + } + ], + "source": [ + "%%R\n", + "\n", + "get_file_list <- function(file_string){\n", + "\n", + "\t# Removing square brackets from the string\n", + "\tcleaned_string = gsub(\"^\\\\[|\\\\]$\", \"\", file_string)\t\n", + "\n", + "\t# Splitting the string into elements\n", + "\tfile_vector = unlist(strsplit(cleaned_string, \",\\\\s*\"))\n", + "\t\n", + "\treturn(file_vector)\n", + "}\n", + "# Removing square brackets from the string\n", + "cleaned_string <- gsub(\"^\\\\[|\\\\]$\", \"\", methylkit_files)\n", + "\n", + "cleaned_string\n", + "\n", + "# Splitting the string into elements\n", + "split_elements <- unlist(strsplit(cleaned_string, \",\\\\s*\"))\n", + "\n", + "# Displaying the vector\n", + "result_vector <- split_elements\n", + "result_vector" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Healthy_1-1.markdup.sorted_CpG.methylKit\"\n" + ] + } + ], + "source": [ + "%%R\n", + "get_file_list(\"Healthy_1-1.markdup.sorted_CpG.methylKit\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"healthy\" \"healthy\" \"disease\" \"disease\"\n" + ] + } + ], + "source": [ + "%%R\n", + "input_string <- \"[[id:healthy_1, group:healthy], [id:healthy_2, group:healthy], [id:disease_1, group:disease], [id:disease_2, group:disease]]\"\n", + "\n", + "# Extracting id values\n", + "id_values <- regmatches(input_string, gregexpr(\"(?<=id:)[^,\\\\]]+\", input_string, perl = TRUE))[[1]]\n", + "\n", + "# Extracting group values\n", + "group_values <- regmatches(input_string, gregexpr(\"(?<=group:)[^,\\\\]]+\", input_string, perl = TRUE))[[1]]\n", + "group_values" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "%%R \n", + "get_ids_from_meta <- function(meta_string){\n", + "\treturn(regmatches(meta_string, gregexpr(\"(?<=id:)[^,\\\\]]+\", meta_string, perl = TRUE))[[1]])\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] 1 1 0 0\n" + ] + } + ], + "source": [ + "%%R\n", + "# Your input vector\n", + "input_vector <- c(\"healthy\", \"healthy\", \"disease\", \"disease\")\n", + "\n", + "# Find unique values and sort them alphabetically\n", + "unique_values <- sort(unique(input_vector))\n", + "\n", + "# Assign numbers based on alphabetical rank\n", + "converted_vector <- match(input_vector, unique_values) - 1\n", + "\n", + "# Display the converted vector\n", + "converted_vector\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "%%R\n", + "char2intVec <- function(charVec){\n", + "\t# Find unique values and sort them alphabetically\n", + "\tunique_values = sort(unique(charVec))\n", + "\n", + "\t# Assign numbers based on alphabetical rank\n", + "\tconverted_vector = match(charVec, unique_values) - 1\n", + "\n", + "\treturn(converted_vector)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] 0\n" + ] + } + ], + "source": [ + "%%R\n", + "char2intVec(c(\"Healthy\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "015455a9c6be9e7274ccecdbf26c5e74b1ea1b6d228cb963172aa34f90a4e9f8" + }, + "kernelspec": { + "display_name": "Python 3.10.2 ('methylseq')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/analysis/notebooks/make_combined_methobj.ipynb b/analysis/notebooks/make_combined_methobj.ipynb new file mode 100644 index 000000000..1eabaf907 --- /dev/null +++ b/analysis/notebooks/make_combined_methobj.ipynb @@ -0,0 +1,572 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext rpy2.ipython" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──\n", + "\n", + "R[write to console]: ✔ ggplot2 3.4.2 ✔ purrr 1.0.1\n", + "✔ tibble 3.2.1 ✔ dplyr 1.1.2\n", + "✔ tidyr 1.3.0 ✔ stringr 1.5.0\n", + "✔ readr 2.1.4 ✔ forcats 1.0.0\n", + "\n", + "R[write to console]: ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n", + "✖ dplyr::collapse() masks IRanges::collapse()\n", + "✖ dplyr::combine() masks BiocGenerics::combine()\n", + "✖ dplyr::desc() masks IRanges::desc()\n", + "✖ tidyr::expand() masks S4Vectors::expand()\n", + "✖ dplyr::filter() masks stats::filter()\n", + "✖ dplyr::first() masks S4Vectors::first()\n", + "✖ dplyr::lag() masks stats::lag()\n", + "✖ ggplot2::Position() masks BiocGenerics::Position(), base::Position()\n", + "✖ purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()\n", + "✖ dplyr::rename() masks S4Vectors::rename()\n", + "✖ dplyr::select() masks methylKit::select()\n", + "✖ dplyr::slice() masks IRanges::slice()\n", + "✖ tidyr::unite() masks methylKit::unite()\n", + "\n" + ] + } + ], + "source": [ + "%%R \n", + "library(methylKit)\n", + "library(tidyverse)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "import re \n", + "from natsort import natsorted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# make methobj.rds" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] 0 1 2 3 4 0 1 2\n" + ] + } + ], + "source": [ + "%%R\n", + "treatment_list_string = '0,1,2,3,4,0,1,2'\n", + "treatment_list = c(mapply(strtoi, strsplit(treatment_list_string,',')))\n", + "treatment_list" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_20k/methyldackel/Healthy_1-1.markdup.sorted_CpG.methylKit'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# base_data_directory = \"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results\"\n", + "# run_id = \"test_NSQCAM529_subsample_20k/methyldackel\"\n", + "# bedGraph_ls = subprocess.run(\n", + "# 'ls {base_data_directory}{run_id}/*.bedGraph'.format(\n", + "# base_data_directory=base_data_directory, run_id=run_id),\n", + "# shell=True, text=True, capture_output=True).stdout.split('\\n')[:-1]\n", + "bedGraph_ls = [\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_20k/methyldackel/Healthy_1-1.markdup.sorted_CpG.bedGraph\"]\n", + "bedGraph_ls_natsorted = natsorted(bedGraph_ls)\n", + "methylKit_ls_natsorted = [re.sub('bedGraph$', 'methylKit', q) for q in bedGraph_ls_natsorted]\n", + "methylKit_fn_string = ' '.join(methylKit_ls_natsorted)\n", + "methylKit_fn_string" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_20k/methyldackel/Healthy_1-1.markdup.sorted_CpG.bedGraph']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subprocess.run(\"ls /efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_20k/methyldackel/*.bedGraph\", shell = True, text=True, capture_output=True).stdout.split('\\n')[:-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Healthy_1-1.markdup.sorted_CpG.bedGraph \u001b[0m\u001b[01;34mmbias\u001b[0m/\n", + "Healthy_1-1.markdup.sorted_CpG.methylKit\n" + ] + } + ], + "source": [ + "ls /efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_20k/methyldackel*" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Error in \"\\\"\" + methylKit_fn_string : \n", + " non-numeric argument to binary operator\n", + "\n", + "R[write to console]: In addition: \n", + "R[write to console]: Warning messages:\n", + "\n", + "R[write to console]: 1: package ‘methylKit’ was built under R version 4.1.3 \n", + "\n", + "R[write to console]: 2: package ‘GenomicRanges’ was built under R version 4.1.3 \n", + "\n", + "R[write to console]: 3: package ‘S4Vectors’ was built under R version 4.1.3 \n", + "\n", + "R[write to console]: 4: package ‘IRanges’ was built under R version 4.1.3 \n", + "\n", + "R[write to console]: 5: package ‘GenomeInfoDb’ was built under R version 4.1.3 \n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Error in \"\\\"\" + methylKit_fn_string : \n", + " non-numeric argument to binary operator\n" + ] + }, + { + "ename": "RInterpreterError", + "evalue": "Failed to parse and evaluate line 'file_paths_string = \\'\\\\\"\\'+methylKit_fn_string+\\'\\\\\"\\'\\n'.\nR error message: 'Error in \"\\\\\"\" + methylKit_fn_string : \\n non-numeric argument to binary operator'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRRuntimeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:280\u001b[0m, in \u001b[0;36mRMagics.eval\u001b[0;34m(self, code)\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 279\u001b[0m \u001b[39m# Need the newline in case the last line in code is a comment.\u001b[39;00m\n\u001b[0;32m--> 280\u001b[0m value, visible \u001b[39m=\u001b[39m ro\u001b[39m.\u001b[39;49mr(\u001b[39m\"\u001b[39;49m\u001b[39mwithVisible(\u001b[39;49m\u001b[39m{\u001b[39;49m\u001b[39m%s\u001b[39;49;00m\u001b[39m\\n\u001b[39;49;00m\u001b[39m})\u001b[39;49m\u001b[39m\"\u001b[39;49m \u001b[39m%\u001b[39;49m code)\n\u001b[1;32m 281\u001b[0m \u001b[39mexcept\u001b[39;00m (ri\u001b[39m.\u001b[39membedded\u001b[39m.\u001b[39mRRuntimeError, \u001b[39mValueError\u001b[39;00m) \u001b[39mas\u001b[39;00m exception:\n\u001b[1;32m 282\u001b[0m \u001b[39m# Otherwise next return seems to have copy of error.\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/__init__.py:451\u001b[0m, in \u001b[0;36mR.__call__\u001b[0;34m(self, string)\u001b[0m\n\u001b[1;32m 450\u001b[0m p \u001b[39m=\u001b[39m rinterface\u001b[39m.\u001b[39mparse(string)\n\u001b[0;32m--> 451\u001b[0m res \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49meval(p)\n\u001b[1;32m 452\u001b[0m \u001b[39mreturn\u001b[39;00m conversion\u001b[39m.\u001b[39mrpy2py(res)\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/functions.py:201\u001b[0m, in \u001b[0;36mSignatureTranslatedFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 200\u001b[0m kwargs[r_k] \u001b[39m=\u001b[39m v\n\u001b[0;32m--> 201\u001b[0m \u001b[39mreturn\u001b[39;00m (\u001b[39msuper\u001b[39;49m(SignatureTranslatedFunction, \u001b[39mself\u001b[39;49m)\n\u001b[1;32m 202\u001b[0m \u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs))\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/functions.py:124\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 123\u001b[0m new_kwargs[k] \u001b[39m=\u001b[39m conversion\u001b[39m.\u001b[39mpy2rpy(v)\n\u001b[0;32m--> 124\u001b[0m res \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m(Function, \u001b[39mself\u001b[39;49m)\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(\u001b[39m*\u001b[39;49mnew_args, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mnew_kwargs)\n\u001b[1;32m 125\u001b[0m res \u001b[39m=\u001b[39m conversion\u001b[39m.\u001b[39mrpy2py(res)\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/rinterface_lib/conversion.py:45\u001b[0m, in \u001b[0;36m_cdata_res_to_rinterface.._\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m---> 45\u001b[0m cdata \u001b[39m=\u001b[39m function(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 46\u001b[0m \u001b[39m# TODO: test cdata is of the expected CType\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/rinterface.py:810\u001b[0m, in \u001b[0;36mSexpClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[39mif\u001b[39;00m error_occured[\u001b[39m0\u001b[39m]:\n\u001b[0;32m--> 810\u001b[0m \u001b[39mraise\u001b[39;00m embedded\u001b[39m.\u001b[39mRRuntimeError(_rinterface\u001b[39m.\u001b[39m_geterrmessage())\n\u001b[1;32m 811\u001b[0m \u001b[39mreturn\u001b[39;00m res\n", + "\u001b[0;31mRRuntimeError\u001b[0m: Error in \"\\\"\" + methylKit_fn_string : \n non-numeric argument to binary operator\n", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mRInterpreterError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mR\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m-i methylKit_fn_string\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfile_paths_string = \u001b[39;49m\u001b[38;5;130;43;01m\\'\u001b[39;49;00m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\'\u001b[39;49;00m\u001b[38;5;124;43m+methylKit_fn_string+\u001b[39;49m\u001b[38;5;130;43;01m\\'\u001b[39;49;00m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\'\u001b[39;49;00m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/IPython/core/interactiveshell.py:2517\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2515\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuiltin_trap:\n\u001b[1;32m 2516\u001b[0m args \u001b[39m=\u001b[39m (magic_arg_s, cell)\n\u001b[0;32m-> 2517\u001b[0m result \u001b[39m=\u001b[39m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 2519\u001b[0m \u001b[39m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2520\u001b[0m \u001b[39m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2521\u001b[0m \u001b[39m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2522\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(fn, magic\u001b[39m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[39mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:795\u001b[0m, in \u001b[0;36mRMagics.R\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 793\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m e\u001b[39m.\u001b[39mstdout\u001b[39m.\u001b[39mendswith(e\u001b[39m.\u001b[39merr):\n\u001b[1;32m 794\u001b[0m \u001b[39mprint\u001b[39m(e\u001b[39m.\u001b[39merr)\n\u001b[0;32m--> 795\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m 796\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 797\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdevice \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39mpng\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39msvg\u001b[39m\u001b[39m'\u001b[39m]:\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:775\u001b[0m, in \u001b[0;36mRMagics.R\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 773\u001b[0m return_output \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 774\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 775\u001b[0m text_result, result, visible \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49meval(code)\n\u001b[1;32m 776\u001b[0m text_output \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m text_result\n\u001b[1;32m 777\u001b[0m \u001b[39mif\u001b[39;00m visible:\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:284\u001b[0m, in \u001b[0;36mRMagics.eval\u001b[0;34m(self, code)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[39mexcept\u001b[39;00m (ri\u001b[39m.\u001b[39membedded\u001b[39m.\u001b[39mRRuntimeError, \u001b[39mValueError\u001b[39;00m) \u001b[39mas\u001b[39;00m exception:\n\u001b[1;32m 282\u001b[0m \u001b[39m# Otherwise next return seems to have copy of error.\u001b[39;00m\n\u001b[1;32m 283\u001b[0m warning_or_other_msg \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mflush()\n\u001b[0;32m--> 284\u001b[0m \u001b[39mraise\u001b[39;00m RInterpreterError(code, \u001b[39mstr\u001b[39m(exception),\n\u001b[1;32m 285\u001b[0m warning_or_other_msg)\n\u001b[1;32m 286\u001b[0m text_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mflush()\n\u001b[1;32m 287\u001b[0m \u001b[39mreturn\u001b[39;00m text_output, value, visible[\u001b[39m0\u001b[39m]\n", + "\u001b[0;31mRInterpreterError\u001b[0m: Failed to parse and evaluate line 'file_paths_string = \\'\\\\\"\\'+methylKit_fn_string+\\'\\\\\"\\'\\n'.\nR error message: 'Error in \"\\\\\"\" + methylKit_fn_string : \\n non-numeric argument to binary operator'" + ] + } + ], + "source": [ + "%%R -i methylKit_fn_string\n", + "file_paths_string = '\\\"'+methylKit_fn_string+'\\\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After a bit of double checking, it seems that `file_paths_string` is just a space delimited string of .methylKit files! " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_20k/methyldackel/Healthy_1-1.markdup.sorted_CpG.bedGraph\"\n" + ] + } + ], + "source": [ + "%%R\n", + "# This script cannot handle spaces in filenames because they are used as a separator\n", + "file_paths = strsplit(\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_20k/methyldackel/Healthy_1-1.markdup.sorted_CpG.bedGraph\",' ')[[1]]\n", + "file_paths\n", + "# file_paths_split = strsplit(file_paths,'/')\n", + "# file_vector = c()\n", + "# for (fps in file_paths_split){\n", + "# # file_vector = c(file_vector,fps[6])\n", + "# \t\tfile_vector = c(file_vector,fps[11])\n", + "# }\n", + " \n", + "# file_list = file_vector\n", + "# file_list" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Error in eval(quote(list(...)), env) : object 'file_list' not found\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Error in eval(quote(list(...)), env) : object 'file_list' not found\n" + ] + }, + { + "ename": "RInterpreterError", + "evalue": "Failed to parse and evaluate line \"split_filenames = mapply(strsplit, file_list, MoreArgs=list('.', fixed=TRUE))\\nrep_list = as.list(sapply(split_filenames, '[[', 1))\\nrep_list\\n\".\nR error message: \"Error in eval(quote(list(...)), env) : object 'file_list' not found\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRRuntimeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:280\u001b[0m, in \u001b[0;36mRMagics.eval\u001b[0;34m(self, code)\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 279\u001b[0m \u001b[39m# Need the newline in case the last line in code is a comment.\u001b[39;00m\n\u001b[0;32m--> 280\u001b[0m value, visible \u001b[39m=\u001b[39m ro\u001b[39m.\u001b[39;49mr(\u001b[39m\"\u001b[39;49m\u001b[39mwithVisible(\u001b[39;49m\u001b[39m{\u001b[39;49m\u001b[39m%s\u001b[39;49;00m\u001b[39m\\n\u001b[39;49;00m\u001b[39m})\u001b[39;49m\u001b[39m\"\u001b[39;49m \u001b[39m%\u001b[39;49m code)\n\u001b[1;32m 281\u001b[0m \u001b[39mexcept\u001b[39;00m (ri\u001b[39m.\u001b[39membedded\u001b[39m.\u001b[39mRRuntimeError, \u001b[39mValueError\u001b[39;00m) \u001b[39mas\u001b[39;00m exception:\n\u001b[1;32m 282\u001b[0m \u001b[39m# Otherwise next return seems to have copy of error.\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/__init__.py:451\u001b[0m, in \u001b[0;36mR.__call__\u001b[0;34m(self, string)\u001b[0m\n\u001b[1;32m 450\u001b[0m p \u001b[39m=\u001b[39m rinterface\u001b[39m.\u001b[39mparse(string)\n\u001b[0;32m--> 451\u001b[0m res \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49meval(p)\n\u001b[1;32m 452\u001b[0m \u001b[39mreturn\u001b[39;00m conversion\u001b[39m.\u001b[39mrpy2py(res)\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/functions.py:201\u001b[0m, in \u001b[0;36mSignatureTranslatedFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 200\u001b[0m kwargs[r_k] \u001b[39m=\u001b[39m v\n\u001b[0;32m--> 201\u001b[0m \u001b[39mreturn\u001b[39;00m (\u001b[39msuper\u001b[39;49m(SignatureTranslatedFunction, \u001b[39mself\u001b[39;49m)\n\u001b[1;32m 202\u001b[0m \u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs))\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/functions.py:124\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 123\u001b[0m new_kwargs[k] \u001b[39m=\u001b[39m conversion\u001b[39m.\u001b[39mpy2rpy(v)\n\u001b[0;32m--> 124\u001b[0m res \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m(Function, \u001b[39mself\u001b[39;49m)\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(\u001b[39m*\u001b[39;49mnew_args, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mnew_kwargs)\n\u001b[1;32m 125\u001b[0m res \u001b[39m=\u001b[39m conversion\u001b[39m.\u001b[39mrpy2py(res)\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/rinterface_lib/conversion.py:45\u001b[0m, in \u001b[0;36m_cdata_res_to_rinterface.._\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m---> 45\u001b[0m cdata \u001b[39m=\u001b[39m function(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 46\u001b[0m \u001b[39m# TODO: test cdata is of the expected CType\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/rinterface.py:810\u001b[0m, in \u001b[0;36mSexpClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[39mif\u001b[39;00m error_occured[\u001b[39m0\u001b[39m]:\n\u001b[0;32m--> 810\u001b[0m \u001b[39mraise\u001b[39;00m embedded\u001b[39m.\u001b[39mRRuntimeError(_rinterface\u001b[39m.\u001b[39m_geterrmessage())\n\u001b[1;32m 811\u001b[0m \u001b[39mreturn\u001b[39;00m res\n", + "\u001b[0;31mRRuntimeError\u001b[0m: Error in eval(quote(list(...)), env) : object 'file_list' not found\n", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mRInterpreterError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mR\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msplit_filenames = mapply(strsplit, file_list, MoreArgs=list(\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m, fixed=TRUE))\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43mrep_list = as.list(sapply(split_filenames, \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m[[\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m, 1))\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43mrep_list\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/IPython/core/interactiveshell.py:2517\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2515\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuiltin_trap:\n\u001b[1;32m 2516\u001b[0m args \u001b[39m=\u001b[39m (magic_arg_s, cell)\n\u001b[0;32m-> 2517\u001b[0m result \u001b[39m=\u001b[39m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 2519\u001b[0m \u001b[39m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2520\u001b[0m \u001b[39m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2521\u001b[0m \u001b[39m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2522\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(fn, magic\u001b[39m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[39mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:795\u001b[0m, in \u001b[0;36mRMagics.R\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 793\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m e\u001b[39m.\u001b[39mstdout\u001b[39m.\u001b[39mendswith(e\u001b[39m.\u001b[39merr):\n\u001b[1;32m 794\u001b[0m \u001b[39mprint\u001b[39m(e\u001b[39m.\u001b[39merr)\n\u001b[0;32m--> 795\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m 796\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 797\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdevice \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39mpng\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39msvg\u001b[39m\u001b[39m'\u001b[39m]:\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:775\u001b[0m, in \u001b[0;36mRMagics.R\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 773\u001b[0m return_output \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 774\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 775\u001b[0m text_result, result, visible \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49meval(code)\n\u001b[1;32m 776\u001b[0m text_output \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m text_result\n\u001b[1;32m 777\u001b[0m \u001b[39mif\u001b[39;00m visible:\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:284\u001b[0m, in \u001b[0;36mRMagics.eval\u001b[0;34m(self, code)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[39mexcept\u001b[39;00m (ri\u001b[39m.\u001b[39membedded\u001b[39m.\u001b[39mRRuntimeError, \u001b[39mValueError\u001b[39;00m) \u001b[39mas\u001b[39;00m exception:\n\u001b[1;32m 282\u001b[0m \u001b[39m# Otherwise next return seems to have copy of error.\u001b[39;00m\n\u001b[1;32m 283\u001b[0m warning_or_other_msg \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mflush()\n\u001b[0;32m--> 284\u001b[0m \u001b[39mraise\u001b[39;00m RInterpreterError(code, \u001b[39mstr\u001b[39m(exception),\n\u001b[1;32m 285\u001b[0m warning_or_other_msg)\n\u001b[1;32m 286\u001b[0m text_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mflush()\n\u001b[1;32m 287\u001b[0m \u001b[39mreturn\u001b[39;00m text_output, value, visible[\u001b[39m0\u001b[39m]\n", + "\u001b[0;31mRInterpreterError\u001b[0m: Failed to parse and evaluate line \"split_filenames = mapply(strsplit, file_list, MoreArgs=list('.', fixed=TRUE))\\nrep_list = as.list(sapply(split_filenames, '[[', 1))\\nrep_list\\n\".\nR error message: \"Error in eval(quote(list(...)), env) : object 'file_list' not found\"" + ] + } + ], + "source": [ + "%%R\n", + "split_filenames = mapply(strsplit, file_list, MoreArgs=list('.', fixed=TRUE))\n", + "rep_list = as.list(sapply(split_filenames, '[[', 1))\n", + "rep_list" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_20k/methyldackel/Healthy_1-1.markdup.sorted_CpG.methylKit\n" + ] + } + ], + "source": [ + "ls /efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_20k/methyldackel/Healthy_1-1.markdup.sorted_CpG.methylKit" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Received list of locations.\n", + "\n", + "R[write to console]: Reading file.\n", + "\n", + "R[write to console]: |--------------------------------------------------|\n", + "|\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: =\n", + "R[write to console]: |\n", + "\n" + ] + } + ], + "source": [ + "%%R\n", + "methobj = methRead(list(\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/NSQCAM529_test/methyldackel/Healthy_1-1.markdup.sorted_CpG.methylKit\"),\n", + " sample.id = list(\"NSQCAM529_test\"),\n", + " assembly=\"hg38\",\n", + " treatment=c(3),\n", + " context=\"CpG\",\n", + " mincov = 10\n", + ")\n", + "# saveRDS(methobj, file=this_file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "%%R \n", + "\n", + "get_file_list <- function(file_string){\n", + "\n", + "\t# Removing square brackets from the string\n", + "\tcleaned_string = gsub(\"^\\\\[|\\\\]$\", \"\", file_string)\t\n", + "\n", + "\t# Splitting the string into elements\n", + "\tfile_vector = unlist(strsplit(cleaned_string, \" \\\\s*\"))\n", + "\t\n", + "\treturn(as.list(file_vector))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_100k.markdup.sorted_CpG.methylKit\"\n", + "[2] \"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_200k.markdup.sorted_CpG.methylKit\"\n" + ] + } + ], + "source": [ + "%%R\n", + "get_file_list('/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_100k.markdup.sorted_CpG.methylKit /efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_200k.markdup.sorted_CpG.methylKit')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# make methobj_lowcov.rds" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Received list of locations.\n", + "\n", + "R[write to console]: Reading file.\n", + "\n", + "R[write to console]: Reading file.\n", + "\n" + ] + } + ], + "source": [ + "%%R\n", + "methobj_lowcov = methRead(\n", + "\t\t\tas.list(c('/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_100k.markdup.sorted_CpG.methylKit','/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_100k.markdup.sorted_CpG.methylKit')),\n", + " sample.id=as.list(c(\"Healthy_1_100k\",\"Healthy_1_200k\")),\n", + " assembly='hg38',\n", + " treatment=c(0,0),\n", + " context='CpG',\n", + " mincov = 3\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Healthy_1_200k\" \"Healthy_1_100k\"\n" + ] + } + ], + "source": [ + "%%R\n", + "meta_string = '[[id:Healthy_1_200k, single_end:false, group:Healthy], [id:Healthy_1_100k, single_end:false, group:Healthy]]'\n", + "id_values <- regmatches(meta_string, gregexpr(\"(?<=id:)[^,\\\\]]+\", meta_string, perl = TRUE))[[1]]\n", + "id_values" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Received list of locations.\n", + "\n", + "R[write to console]: Reading file.\n", + "\n", + "R[write to console]: Reading file.\n", + "\n" + ] + } + ], + "source": [ + "%%R\n", + "methobj_lowcov = methRead(\n", + "\t\t\tas.list(c('/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_100k.markdup.sorted_CpG.methylKit','/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_200k.markdup.sorted_CpG.methylKit')),\n", + " sample.id=as.list(id_values),\n", + " assembly='hg38',\n", + " treatment=c(0,0),\n", + " context='CpG',\n", + " mincov = 3\n", + " )\n", + "# saveRDS(methobj_lowcov, file=this_file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "015455a9c6be9e7274ccecdbf26c5e74b1ea1b6d228cb963172aa34f90a4e9f8" + }, + "kernelspec": { + "display_name": "Python 3.10.2 ('methylseq')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/analysis/notebooks/merge_annotation_stats.ipynb b/analysis/notebooks/merge_annotation_stats.ipynb new file mode 100644 index 000000000..11affcd11 --- /dev/null +++ b/analysis/notebooks/merge_annotation_stats.ipynb @@ -0,0 +1,1054 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There's something weird with these processes that requires `rep_list` and I'm trying to figure out how to get that information directly from the input objects themselves, much like I did with the methylkit objects using `getSampleID`. i think I just need to noodle around and figure out how best to go about getting that information.\n", + "\n", + "\n", + "I've been having some weird issues with getSampleID on the annotated objects, so I think I need to start with the annotation step here and see how I can retreive/maintain the sample IDs. I very much want to avoid continually passing on a list of samplIDs and hoping that the order is preserved across processes.\n", + "\n", + "You know what, for now I'm starting to feel like this may be more trouble than it's worth. I think I can just pass along the methobj just for the sake of extracting the rep_list. That should be good enough." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext rpy2.ipython" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Loading required package: GenomicRanges\n", + "\n", + "R[write to console]: Loading required package: stats4\n", + "\n", + "R[write to console]: Loading required package: BiocGenerics\n", + "\n", + "R[write to console]: \n", + "Attaching package: ‘BiocGenerics’\n", + "\n", + "\n", + "R[write to console]: The following objects are masked from ‘package:stats’:\n", + "\n", + " IQR, mad, sd, var, xtabs\n", + "\n", + "\n", + "R[write to console]: The following objects are masked from ‘package:base’:\n", + "\n", + " anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n", + " dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n", + " grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n", + " order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n", + " rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n", + " union, unique, unsplit, which.max, which.min\n", + "\n", + "\n", + "R[write to console]: Loading required package: S4Vectors\n", + "\n", + "R[write to console]: \n", + "Attaching package: ‘S4Vectors’\n", + "\n", + "\n", + "R[write to console]: The following objects are masked from ‘package:base’:\n", + "\n", + " expand.grid, I, unname\n", + "\n", + "\n", + "R[write to console]: Loading required package: IRanges\n", + "\n", + "R[write to console]: Loading required package: GenomeInfoDb\n", + "\n", + "R[write to console]: Loading required package: grid\n", + "\n", + "R[write to console]: \n", + "Attaching package: ‘genomation’\n", + "\n", + "\n", + "R[write to console]: The following objects are masked from ‘package:methylKit’:\n", + "\n", + " getFeatsWithTargetsStats, getFlanks, getMembers,\n", + " getTargetAnnotationStats, plotTargetAnnotation\n", + "\n", + "\n", + "R[write to console]: Loading required package: GenomicFeatures\n", + "\n", + "R[write to console]: Loading required package: AnnotationDbi\n", + "\n", + "R[write to console]: Loading required package: Biobase\n", + "\n", + "R[write to console]: Welcome to Bioconductor\n", + "\n", + " Vignettes contain introductory material; view with\n", + " 'browseVignettes()'. To cite Bioconductor, see\n", + " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n", + "\n", + "\n", + "R[write to console]: \n", + "Attaching package: 'AnnotationDbi'\n", + "\n", + "\n", + "R[write to console]: The following object is masked from 'package:methylKit':\n", + "\n", + " select\n", + "\n", + "\n" + ] + } + ], + "source": [ + "%%R\n", + "invisible( lapply(c(\n", + "\t\"methylKit\",\n", + "\t\"genomation\",\n", + "\t\"ChIPpeakAnno\",\n", + "\t\"TxDb.Hsapiens.UCSC.hg38.knownGene\",\n", + "\t\"rtracklayer\"\n", + "), library, character.only=T))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "%%R\n", + "methobj_lowcov <- readRDS(\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methylqc/methobj_lowcov.rds\")\n", + "repeat_anno <- import.bb(\"/efs/scratch/methyl_qc_data/reference_data/chm13v2.0_rmsk.bb\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "methylRawList object with 2 methylRaw objects\n", + "\n", + "methylRaw object with 11036 rows\n", + "--------------\n", + " chr start end strand coverage numCs numTs\n", + "1 chr1 923082 923082 + 3 2 1\n", + "2 chr1 1108570 1108570 - 4 4 0\n", + "3 chr1 1108576 1108576 - 4 4 0\n", + "4 chr1 1108599 1108599 - 3 3 0\n", + "5 chr1 1108605 1108605 - 3 3 0\n", + "6 chr1 1108637 1108637 - 3 3 0\n", + "--------------\n", + "sample.id: Healthy_1_200k \n", + "assembly: hg38 \n", + "context: CpG \n", + "resolution: base \n", + "\n", + "methylRaw object with 4258 rows\n", + "--------------\n", + " chr start end strand coverage numCs numTs\n", + "1 chr1 1041617 1041617 + 3 3 0\n", + "2 chr1 1041623 1041623 + 3 2 1\n", + "3 chr1 1041639 1041639 + 3 3 0\n", + "4 chr1 1041643 1041643 + 3 3 0\n", + "5 chr1 1041650 1041650 + 3 3 0\n", + "6 chr1 1041654 1041654 + 3 3 0\n", + "--------------\n", + "sample.id: Healthy_1_100k \n", + "assembly: hg38 \n", + "context: CpG \n", + "resolution: base \n", + "\n", + "treatment: 0 0 \n" + ] + } + ], + "source": [ + "%%R\n", + "methobj_lowcov" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Error in as(methobj_lowcov[0], \"GRanges\") : \n", + " no method or default for coercing \"list\" to \"GRanges\"\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Error in as(methobj_lowcov[0], \"GRanges\") : \n", + " no method or default for coercing \"list\" to \"GRanges\"\n" + ] + }, + { + "ename": "RInterpreterError", + "evalue": "Failed to parse and evaluate line '# annotateWithFeature(GRanges(methobj_lowcov),repeat_anno)\\nas(methobj_lowcov[0],\"GRanges\")\\n'.\nR error message: 'Error in as(methobj_lowcov[0], \"GRanges\") : \\n no method or default for coercing \"list\" to \"GRanges\"'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRRuntimeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:280\u001b[0m, in \u001b[0;36mRMagics.eval\u001b[0;34m(self, code)\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 279\u001b[0m \u001b[39m# Need the newline in case the last line in code is a comment.\u001b[39;00m\n\u001b[0;32m--> 280\u001b[0m value, visible \u001b[39m=\u001b[39m ro\u001b[39m.\u001b[39;49mr(\u001b[39m\"\u001b[39;49m\u001b[39mwithVisible(\u001b[39;49m\u001b[39m{\u001b[39;49m\u001b[39m%s\u001b[39;49;00m\u001b[39m\\n\u001b[39;49;00m\u001b[39m})\u001b[39;49m\u001b[39m\"\u001b[39;49m \u001b[39m%\u001b[39;49m code)\n\u001b[1;32m 281\u001b[0m \u001b[39mexcept\u001b[39;00m (ri\u001b[39m.\u001b[39membedded\u001b[39m.\u001b[39mRRuntimeError, \u001b[39mValueError\u001b[39;00m) \u001b[39mas\u001b[39;00m exception:\n\u001b[1;32m 282\u001b[0m \u001b[39m# Otherwise next return seems to have copy of error.\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/__init__.py:451\u001b[0m, in \u001b[0;36mR.__call__\u001b[0;34m(self, string)\u001b[0m\n\u001b[1;32m 450\u001b[0m p \u001b[39m=\u001b[39m rinterface\u001b[39m.\u001b[39mparse(string)\n\u001b[0;32m--> 451\u001b[0m res \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49meval(p)\n\u001b[1;32m 452\u001b[0m \u001b[39mreturn\u001b[39;00m conversion\u001b[39m.\u001b[39mrpy2py(res)\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/functions.py:201\u001b[0m, in \u001b[0;36mSignatureTranslatedFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 200\u001b[0m kwargs[r_k] \u001b[39m=\u001b[39m v\n\u001b[0;32m--> 201\u001b[0m \u001b[39mreturn\u001b[39;00m (\u001b[39msuper\u001b[39;49m(SignatureTranslatedFunction, \u001b[39mself\u001b[39;49m)\n\u001b[1;32m 202\u001b[0m \u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs))\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/functions.py:124\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 123\u001b[0m new_kwargs[k] \u001b[39m=\u001b[39m conversion\u001b[39m.\u001b[39mpy2rpy(v)\n\u001b[0;32m--> 124\u001b[0m res \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m(Function, \u001b[39mself\u001b[39;49m)\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(\u001b[39m*\u001b[39;49mnew_args, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mnew_kwargs)\n\u001b[1;32m 125\u001b[0m res \u001b[39m=\u001b[39m conversion\u001b[39m.\u001b[39mrpy2py(res)\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/rinterface_lib/conversion.py:45\u001b[0m, in \u001b[0;36m_cdata_res_to_rinterface.._\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m---> 45\u001b[0m cdata \u001b[39m=\u001b[39m function(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 46\u001b[0m \u001b[39m# TODO: test cdata is of the expected CType\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/rinterface.py:810\u001b[0m, in \u001b[0;36mSexpClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[39mif\u001b[39;00m error_occured[\u001b[39m0\u001b[39m]:\n\u001b[0;32m--> 810\u001b[0m \u001b[39mraise\u001b[39;00m embedded\u001b[39m.\u001b[39mRRuntimeError(_rinterface\u001b[39m.\u001b[39m_geterrmessage())\n\u001b[1;32m 811\u001b[0m \u001b[39mreturn\u001b[39;00m res\n", + "\u001b[0;31mRRuntimeError\u001b[0m: Error in as(methobj_lowcov[0], \"GRanges\") : \n no method or default for coercing \"list\" to \"GRanges\"\n", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mRInterpreterError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mR\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m# annotateWithFeature(GRanges(methobj_lowcov),repeat_anno)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43mas(methobj_lowcov[0],\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGRanges\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/IPython/core/interactiveshell.py:2517\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2515\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuiltin_trap:\n\u001b[1;32m 2516\u001b[0m args \u001b[39m=\u001b[39m (magic_arg_s, cell)\n\u001b[0;32m-> 2517\u001b[0m result \u001b[39m=\u001b[39m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 2519\u001b[0m \u001b[39m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2520\u001b[0m \u001b[39m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2521\u001b[0m \u001b[39m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2522\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(fn, magic\u001b[39m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[39mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:795\u001b[0m, in \u001b[0;36mRMagics.R\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 793\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m e\u001b[39m.\u001b[39mstdout\u001b[39m.\u001b[39mendswith(e\u001b[39m.\u001b[39merr):\n\u001b[1;32m 794\u001b[0m \u001b[39mprint\u001b[39m(e\u001b[39m.\u001b[39merr)\n\u001b[0;32m--> 795\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m 796\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 797\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdevice \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39mpng\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39msvg\u001b[39m\u001b[39m'\u001b[39m]:\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:775\u001b[0m, in \u001b[0;36mRMagics.R\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 773\u001b[0m return_output \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 774\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 775\u001b[0m text_result, result, visible \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49meval(code)\n\u001b[1;32m 776\u001b[0m text_output \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m text_result\n\u001b[1;32m 777\u001b[0m \u001b[39mif\u001b[39;00m visible:\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/ipython/rmagic.py:284\u001b[0m, in \u001b[0;36mRMagics.eval\u001b[0;34m(self, code)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[39mexcept\u001b[39;00m (ri\u001b[39m.\u001b[39membedded\u001b[39m.\u001b[39mRRuntimeError, \u001b[39mValueError\u001b[39;00m) \u001b[39mas\u001b[39;00m exception:\n\u001b[1;32m 282\u001b[0m \u001b[39m# Otherwise next return seems to have copy of error.\u001b[39;00m\n\u001b[1;32m 283\u001b[0m warning_or_other_msg \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mflush()\n\u001b[0;32m--> 284\u001b[0m \u001b[39mraise\u001b[39;00m RInterpreterError(code, \u001b[39mstr\u001b[39m(exception),\n\u001b[1;32m 285\u001b[0m warning_or_other_msg)\n\u001b[1;32m 286\u001b[0m text_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mflush()\n\u001b[1;32m 287\u001b[0m \u001b[39mreturn\u001b[39;00m text_output, value, visible[\u001b[39m0\u001b[39m]\n", + "\u001b[0;31mRInterpreterError\u001b[0m: Failed to parse and evaluate line '# annotateWithFeature(GRanges(methobj_lowcov),repeat_anno)\\nas(methobj_lowcov[0],\"GRanges\")\\n'.\nR error message: 'Error in as(methobj_lowcov[0], \"GRanges\") : \\n no method or default for coercing \"list\" to \"GRanges\"'" + ] + } + ], + "source": [ + "%%R \n", + "# annotateWithFeature(GRanges(methobj_lowcov),repeat_anno)\n", + "# as(methobj_lowcov[0],\"GRanges\")\n", + "for (sample in methobj_lowcov)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import rpy2\n", + "from rpy2.robjects.packages import importr\n", + "import rpy2.rinterface as rinterface\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "as_char = rinterface.baseenv['as.character']\n", + "methylKit = importr('methylKit')\n", + "genomation = importr('genomation')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: summary of target set annotation with feature annotation:\n", + "\n", + "R[write to console]: Rows in target set: 11036\n", + "\n", + "R[write to console]: ----------------------------\n", + "\n", + "R[write to console]: percentage of target elements overlapping with features:\n", + "\n", + "R[write to console]: \n", + "\n", + "R[write to console]: percentage of feature elements overlapping with target:\n", + "\n", + "R[write to console]: \n", + "\n", + "R[write to console]: summary of target set annotation with feature annotation:\n", + "\n", + "R[write to console]: Rows in target set: 11036\n", + "\n", + "R[write to console]: ----------------------------\n", + "\n", + "R[write to console]: percentage of target elements overlapping with features:\n", + "\n", + "R[write to console]: \n", + "\n", + "R[write to console]: percentage of feature elements overlapping with target:\n", + "\n", + "R[write to 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"metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " '1',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'AnnotationByFeature',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4',\n", + " 'S4']" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# len([i for i in methobj_lowcov_twist_annotation])\n", + "list(rpy2.robjects.r.summary(methobj_lowcov_twist_annotation))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Error in (function (classes, fdef, mtable) : \n", + " unable to find an inherited method for function ‘getSampleID’ for signature ‘\"list\"’\n", + "\n" + ] + }, + { + "ename": "RRuntimeError", + "evalue": "Error in (function (classes, fdef, mtable) : \n unable to find an inherited method for function ‘getSampleID’ for signature ‘\"list\"’\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmethylKit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetSampleID\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethobj_lowcov_twist_annotation\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/functions.py:201\u001b[0m, in \u001b[0;36mSignatureTranslatedFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 199\u001b[0m v \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mpop(k)\n\u001b[1;32m 200\u001b[0m kwargs[r_k] \u001b[39m=\u001b[39m v\n\u001b[0;32m--> 201\u001b[0m \u001b[39mreturn\u001b[39;00m (\u001b[39msuper\u001b[39;49m(SignatureTranslatedFunction, \u001b[39mself\u001b[39;49m)\n\u001b[1;32m 202\u001b[0m \u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs))\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/functions.py:124\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 123\u001b[0m new_kwargs[k] \u001b[39m=\u001b[39m conversion\u001b[39m.\u001b[39mpy2rpy(v)\n\u001b[0;32m--> 124\u001b[0m res \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m(Function, \u001b[39mself\u001b[39;49m)\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(\u001b[39m*\u001b[39;49mnew_args, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mnew_kwargs)\n\u001b[1;32m 125\u001b[0m res \u001b[39m=\u001b[39m conversion\u001b[39m.\u001b[39mrpy2py(res)\n\u001b[1;32m 126\u001b[0m \u001b[39mreturn\u001b[39;00m res\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/rinterface_lib/conversion.py:45\u001b[0m, in \u001b[0;36m_cdata_res_to_rinterface.._\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m---> 45\u001b[0m cdata \u001b[39m=\u001b[39m function(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 46\u001b[0m \u001b[39m# TODO: test cdata is of the expected CType\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[39mreturn\u001b[39;00m _cdata_to_rinterface(cdata)\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/rinterface.py:810\u001b[0m, in \u001b[0;36mSexpClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 803\u001b[0m res \u001b[39m=\u001b[39m rmemory\u001b[39m.\u001b[39mprotect(\n\u001b[1;32m 804\u001b[0m openrlib\u001b[39m.\u001b[39mrlib\u001b[39m.\u001b[39mR_tryEval(\n\u001b[1;32m 805\u001b[0m call_r,\n\u001b[1;32m 806\u001b[0m call_context\u001b[39m.\u001b[39m__sexp__\u001b[39m.\u001b[39m_cdata,\n\u001b[1;32m 807\u001b[0m error_occured)\n\u001b[1;32m 808\u001b[0m )\n\u001b[1;32m 809\u001b[0m \u001b[39mif\u001b[39;00m error_occured[\u001b[39m0\u001b[39m]:\n\u001b[0;32m--> 810\u001b[0m \u001b[39mraise\u001b[39;00m embedded\u001b[39m.\u001b[39mRRuntimeError(_rinterface\u001b[39m.\u001b[39m_geterrmessage())\n\u001b[1;32m 811\u001b[0m \u001b[39mreturn\u001b[39;00m res\n", + "\u001b[0;31mRRuntimeError\u001b[0m: Error in (function (classes, fdef, mtable) : \n unable to find an inherited method for function ‘getSampleID’ for signature ‘\"list\"’\n" + ] + } + ], + "source": [ + "methylKit.getSampleID(methobj_lowcov_twist_annotation)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Error in (function (classes, fdef, mtable) : \n", + " unable to find an inherited method for function ‘getSampleID’ for signature ‘\"list\"’\n", + "\n" + ] + }, + { + "ename": "RRuntimeError", + "evalue": "Error in (function (classes, fdef, mtable) : \n unable to find an inherited method for function ‘getSampleID’ for signature ‘\"list\"’\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmethylKit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetSampleID\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethobj_lowcov_repeat_annotation\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/functions.py:201\u001b[0m, in \u001b[0;36mSignatureTranslatedFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 199\u001b[0m v \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mpop(k)\n\u001b[1;32m 200\u001b[0m kwargs[r_k] \u001b[39m=\u001b[39m v\n\u001b[0;32m--> 201\u001b[0m \u001b[39mreturn\u001b[39;00m (\u001b[39msuper\u001b[39;49m(SignatureTranslatedFunction, \u001b[39mself\u001b[39;49m)\n\u001b[1;32m 202\u001b[0m \u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs))\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/robjects/functions.py:124\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 123\u001b[0m new_kwargs[k] \u001b[39m=\u001b[39m conversion\u001b[39m.\u001b[39mpy2rpy(v)\n\u001b[0;32m--> 124\u001b[0m res \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m(Function, \u001b[39mself\u001b[39;49m)\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(\u001b[39m*\u001b[39;49mnew_args, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mnew_kwargs)\n\u001b[1;32m 125\u001b[0m res \u001b[39m=\u001b[39m conversion\u001b[39m.\u001b[39mrpy2py(res)\n\u001b[1;32m 126\u001b[0m \u001b[39mreturn\u001b[39;00m res\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/rinterface_lib/conversion.py:45\u001b[0m, in \u001b[0;36m_cdata_res_to_rinterface.._\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m---> 45\u001b[0m cdata \u001b[39m=\u001b[39m function(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 46\u001b[0m \u001b[39m# TODO: test cdata is of the expected CType\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[39mreturn\u001b[39;00m _cdata_to_rinterface(cdata)\n", + "File \u001b[0;32m~/miniconda3/envs/methylseq/lib/python3.10/site-packages/rpy2/rinterface.py:810\u001b[0m, in \u001b[0;36mSexpClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 803\u001b[0m res \u001b[39m=\u001b[39m rmemory\u001b[39m.\u001b[39mprotect(\n\u001b[1;32m 804\u001b[0m openrlib\u001b[39m.\u001b[39mrlib\u001b[39m.\u001b[39mR_tryEval(\n\u001b[1;32m 805\u001b[0m call_r,\n\u001b[1;32m 806\u001b[0m call_context\u001b[39m.\u001b[39m__sexp__\u001b[39m.\u001b[39m_cdata,\n\u001b[1;32m 807\u001b[0m error_occured)\n\u001b[1;32m 808\u001b[0m )\n\u001b[1;32m 809\u001b[0m \u001b[39mif\u001b[39;00m error_occured[\u001b[39m0\u001b[39m]:\n\u001b[0;32m--> 810\u001b[0m \u001b[39mraise\u001b[39;00m embedded\u001b[39m.\u001b[39mRRuntimeError(_rinterface\u001b[39m.\u001b[39m_geterrmessage())\n\u001b[1;32m 811\u001b[0m \u001b[39mreturn\u001b[39;00m res\n", + "\u001b[0;31mRRuntimeError\u001b[0m: Error in (function (classes, fdef, mtable) : \n unable to find an inherited method for function ‘getSampleID’ for signature ‘\"list\"’\n" + ] + } + ], + "source": [ + "methylKit.getSampleID(methobj_lowcov_repeat_annotation)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.07, 0.01]\n", + "[array([0.07]), array([0.01])]\n", + "[ [RTYPES.S4SXP]\n", + "R classes: ('AnnotationByFeature',), [RTYPES.S4SXP]\n", + "R classes: ('AnnotationByFeature',)]\n" + ] + } + ], + "source": [ + "print([np.array(genomation.getFeatsWithTargetsStats(q, percentage=True))[0] for q in methobj_lowcov_repeat_annotation])\n", + "print([np.array(genomation.getFeatsWithTargetsStats(q, percentage=True)) for q in methobj_lowcov_repeat_annotation])\n", + "print([q for q in methobj_lowcov_repeat_annotation])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%%R\n", + "methobj_twist_annotation <- readRDS(\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methylqc/methobj_lowcov_twist_annotation.rds\")\n", + "cpg_obj <- readRDS(\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methylqc/cpg_obj_twist_out.rds\")\n", + "methobj_repeat_annotation <- readRDS(\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methylqc/methobj_lowcov_repeat_annotation.rds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Loading required package: genomation\n", + "\n", + "R[write to console]: Loading required package: grid\n", + "\n" + ] + } + ], + "source": [ + "%%R \n", + "methobj_repeat_annotation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "015455a9c6be9e7274ccecdbf26c5e74b1ea1b6d228cb963172aa34f90a4e9f8" + }, + "kernelspec": { + "display_name": "Python 3.10.2 ('methylseq')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/analysis/notebooks/meth_histogram_by_chromosome.ipynb b/analysis/notebooks/meth_histogram_by_chromosome.ipynb new file mode 100644 index 000000000..dc3f82aef --- /dev/null +++ b/analysis/notebooks/meth_histogram_by_chromosome.ipynb @@ -0,0 +1,408 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import collections\n", + "import bz2\n", + "import gzip\n", + "from urllib.request import urlopen\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0Healthy_1_100k/efs/scratch/jeffrey/code/github.com/exosomedx...
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" + ], + "text/plain": [ + " id file\n", + "0 Healthy_1_100k /efs/scratch/jeffrey/code/github.com/exosomedx...\n", + "1 Healthy_1_200k /efs/scratch/jeffrey/code/github.com/exosomedx..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file_list = [\n", + "\t\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_100k.markdup.sorted_CpG.bedGraph\",\n", + "\t\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methyldackel/Healthy_1_200k.markdup.sorted_CpG.bedGraph\"\n", + "]\n", + "id_list = [\"Healthy_1_100k\",\"Healthy_1_200k\"]\n", + "file_id_df = pd.DataFrame.from_dict({\n", + "\t\t\"id\": id_list,\n", + "\t\t\"file\": file_list\n", + "\t})\n", + "file_id_df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def read_bedfile(fname:str):\n", + " '''\n", + " read compressed (.gz .bz) BED files\n", + " From https://github.com/liguowang/cpgtools/blob/master/lib/cpgmodule/ireader.py\n", + " cpgtools version 1.10.0\n", + " Used with chrom_count_from_bedfile function\n", + " '''\n", + "\n", + " def nopen(f, mode=\"rb\"):\n", + " if not isinstance(f, str):\n", + " return f\n", + " if f.startswith(\"|\"):\n", + " p = Popen(f[1:], stdout=PIPE, stdin=PIPE, shell=True)\n", + " if mode[0] == \"r\": return p.stdout\n", + " return p\n", + " return {\"r\": sys.stdin, \"w\": sys.stdout}[mode[0]] if f == \"-\" \\\n", + " else gzip.open(f, mode) if f.endswith((\".gz\", \".Z\", \".z\")) \\\n", + " else bz2.BZ2File(f, mode) if f.endswith((\".bz\", \".bz2\", \".bzip2\")) \\\n", + " else urlopen(f) if f.startswith((\"http://\", \"https://\",\"ftp://\")) \\\n", + " else open(f, mode)\n", + "\n", + " for l in nopen(fname):\n", + " yield l.decode('utf8').strip().replace(\"\\r\", \"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def cpg_count_from_bedfile(infile:str) -> dict:\n", + " '''\n", + " count chrom frequencies from BED file\n", + " From https://github.com/liguowang/cpgtools/blob/master/lib/cpgmodule/utils.py\n", + " cpgtools version 1.10.0\n", + " '''\n", + " \n", + " \n", + " chrom_count = collections.defaultdict(int)\n", + " \n", + " for l in read_bedfile(infile):\n", + " if l.startswith('#'):\n", + " continue\n", + " if l.startswith('track'):\n", + " continue\n", + " if l.startswith('browser'):\n", + " continue\n", + " f = l.split()\n", + " if len(f)< 3:\n", + " print (\"BED has at least 3 columns. Skip: \" + l, file=sys.stderr)\n", + " continue\n", + " try:\n", + " start = int(f[1])\n", + " end = int(f[2])\n", + " if start > end:\n", + " print (\"'Start' cannot be larger than 'End'. Skip: \" + l, file=sys.stderr)\n", + " continue\n", + " except:\n", + " print (\"Not in valid BED format. Skip:\" + l, file=sys.stderr)\n", + " continue\n", + "\n", + " chrom_count[f[0]] += 1\n", + " return chrom_count\n", + "\n", + "def meth_count_from_bedfile(infile:str) -> dict:\n", + " '''\n", + " count methylation on each chromosome from BED file\n", + " Modified from https://github.com/liguowang/cpgtools/blob/master/lib/cpgmodule/utils.py\n", + " cpgtools version 1.10.0\n", + " '''\n", + " \n", + " \n", + " chrom_count = collections.defaultdict(int)\n", + " \n", + " for l in read_bedfile(infile):\n", + " if l.startswith('#'):\n", + " continue\n", + " if l.startswith('track'):\n", + " continue\n", + " if l.startswith('browser'):\n", + " continue\n", + " f = l.split()\n", + " if len(f)< 3:\n", + " print(\"BED has at least 3 columns. Skip: \" + l, file=sys.stderr)\n", + " continue\n", + " try:\n", + " start = int(f[1])\n", + " end = int(f[2])\n", + " if start > end:\n", + " print(\"'Start' cannot be larger than 'End'. Skip: \" + l, file=sys.stderr)\n", + " continue\n", + " except:\n", + " print(\"Not in valid BED format. Skip:\" + l, file=sys.stderr)\n", + " continue\n", + "\n", + " # Counts any partially or fully methylated CpG.\n", + " if float(f[3]) > 0:\n", + " chrom_count[f[0]] += 1\n", + "\n", + " return chrom_count\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def meth_histogram_by_chromosome(hg38_chrom_sizes_file:str,\n", + "\tfile_id_df:pd.DataFrame) -> (pd.DataFrame, pd.DataFrame):\n", + "\t'''\n", + " Calculate chromosome-level CpG methylation statistics. Returns one Pandas DataFrame that is raw methylation counts and one that is percentage of CpGs.\n", + "\n", + " '''\n", + "\t\n", + "\tkeep_chr_names = ['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19', 'chr20', 'chr21', 'chr22', 'chrX', 'chrY', 'chrM']\n", + "\tcontrol_chr_names = ['phage_lambda', 'plasmid_puc19c']\n", + "\tchr_size_df = pd.read_csv(hg38_chrom_sizes_file, sep='\\t', index_col=0, header=None)\n", + "\tchr_size_df.columns = ['chr_length']\n", + "\tchr_size_df.index.name = 'chromosome'\n", + "\n", + "\tcpg_countsL = []\n", + "\tmeth_countsL = []\n", + "\n", + "\tcontrol_cpg_countsL = []\n", + "\tcontrol_meth_countsL = []\n", + "\n", + "\tfor index, row in file_id_df.iterrows():\n", + "\t\trep = row[\"id\"]\n", + "\n", + "\t\tcpg_count = cpg_count_from_bedfile(row[\"file\"])\n", + "\t\tcpg_count_df = pd.DataFrame(data={rep+'_cpg':[cpg_count[q] for q in keep_chr_names]}, index=keep_chr_names)\n", + "\t\tmeth_count = meth_count_from_bedfile(row[\"file\"])\n", + "\t\tmeth_count_df = pd.DataFrame(data={rep+'_meth':[meth_count[q] for q in keep_chr_names]}, index=keep_chr_names)\n", + "\t\tcpg_countsL.append(cpg_count_df)\n", + "\t\tmeth_countsL.append(meth_count_df)\n", + "\n", + "\t\tcontrol_cpg_count_df = pd.DataFrame(data={rep+'_cpg':[cpg_count[q] for q in control_chr_names]}, index=control_chr_names)\n", + "\t\tcontrol_meth_count_df = pd.DataFrame(data={rep+'_meth':[meth_count[q] for q in control_chr_names]}, index=control_chr_names)\n", + "\t\tcontrol_cpg_countsL.append(control_cpg_count_df)\n", + "\t\tcontrol_meth_countsL.append(control_meth_count_df)\n", + "\t\n", + "\tcpg_counts_df = pd.concat(cpg_countsL, axis=1)\n", + "\tmeth_counts_df = pd.concat(meth_countsL, axis=1)\n", + "\tcounts_df = pd.concat([meth_counts_df, cpg_counts_df], axis=1)\n", + " # counts_df.to_csv(mfreq_path_fn)\n", + "\n", + "\tcontrol_cpg_counts_df = pd.concat(control_cpg_countsL, axis=1)\n", + "\tcontrol_meth_counts_df = pd.concat(control_meth_countsL, axis=1)\n", + "\tcontrol_counts_df = pd.concat([control_meth_counts_df, control_cpg_counts_df], axis=1)\n", + " # control_counts_df.to_csv(mfreq_control_path_fn)\n", + "\n", + "\tcounts_df_meth = counts_df.filter(like='_meth', axis=1)\n", + "\tcount_col_names = [q[:-5] for q in counts_df_meth.columns]\n", + "\tcounts_df_meth.columns = count_col_names\n", + "\n", + "\tcounts_df_cpg = counts_df.filter(like='_cpg', axis=1)\n", + "\tcount_col_names = [q[:-4] for q in counts_df_cpg.columns]\n", + "\tcounts_df_cpg.columns = count_col_names\n", + "\n", + "\tcounts_df_long = counts_df_meth.reset_index().melt(id_vars='index', var_name = \"replicate\", value_name='count')\n", + "\tcpg_df_long = counts_df_cpg.reset_index().melt(id_vars='index', var_name = \"replicate\", value_name='count')\n", + "\n", + "\tcontrol_counts_df_meth = control_counts_df.filter(like='_meth', axis=1)\n", + "\tcount_col_names = [q[:-5] for q in control_counts_df_meth.columns]\n", + "\tcontrol_counts_df_meth.columns = count_col_names\n", + " \n", + "\tcontrol_counts_df_cpg = control_counts_df.filter(like='_cpg', axis=1)\n", + "\tcount_col_names = [q[:-4] for q in control_counts_df_cpg.columns]\n", + "\tcontrol_counts_df_cpg.columns = count_col_names\n", + " \n", + "\tcontrol_counts_df_long = control_counts_df_meth.reset_index().melt(id_vars='index', var_name = \"replicate\", value_name='count')\n", + "\tcontrol_cpg_df_long = control_counts_df_cpg.reset_index().melt(id_vars='index', var_name = \"replicate\", value_name='count')\n", + " \n", + "\tcounts_df_long = pd.concat([counts_df_long, control_counts_df_long])\n", + "\tcpg_df_long = pd.concat([cpg_df_long, control_cpg_df_long])\n", + "\n", + "\tchr_iteration_list = keep_chr_names + control_chr_names\n", + "\t# Calculate the percentage of methylation and return a DF with methylated CpG counts, all CpG counts, and percent methylated.\n", + "\tchr_percentL = []\n", + "\tfor this_chr_name in chr_iteration_list:\n", + "\t\tthis_cpg_counts = cpg_df_long.where(counts_df_long.loc[:,'index'].eq(this_chr_name)).dropna()\n", + "\t\tthis_meth_counts = counts_df_long.where(counts_df_long.loc[:,'index'].eq(this_chr_name)).dropna()\n", + "\n", + "\t\tthis_chr_pct = this_meth_counts.loc[:,'count'].div(this_cpg_counts.loc[:,'count']).mul(100).reset_index(drop=True)\n", + "\t\t# If a zero is in the denominator it means there was zero methylation percent.\n", + "\t\tthis_chr_pct = this_chr_pct.fillna(0)\n", + "\t\tthis_chr_pct.name = 'Percent CpGs Methylated'\n", + "\n", + "\t\tthis_chr_name_df = this_cpg_counts.loc[:,'index'].reset_index(drop=True)\n", + "\t\tthis_chr_name_df.name = 'Chromosome'\n", + "\n", + "\t\tthis_chr_sample = this_cpg_counts.loc[:,'replicate'].reset_index(drop=True)\n", + "\t\tthis_chr_sample.name = 'Sample'\n", + "\n", + "\t\tthis_chr_cpg_counts = this_cpg_counts.loc[:,'count'].reset_index(drop=True)\n", + "\t\tthis_chr_cpg_counts.name = 'CpG Count'\n", + "\n", + "\t\tthis_chr_meth_counts = this_cpg_counts.loc[:,'count'].reset_index(drop=True)\n", + "\t\tthis_chr_meth_counts.name = 'Methylated CpG Count'\n", + "\n", + "\t\tthis_chr_df = pd.concat([this_chr_sample, this_chr_name_df, this_chr_meth_counts, this_chr_cpg_counts, this_chr_pct], axis=1)\n", + "\t\tchr_percentL.append(this_chr_df)\n", + "\tchr_percent_df = pd.concat(chr_percentL)\n", + "\treturn counts_df_long, chr_percent_df\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def output_chromosome_histograms(counts_df_long:pd.DataFrame, chr_percent_df:pd.DataFrame, histogram_dims:(float,float) = (8,4), histogram_xaxis_label_rotation:float = 45) -> None:\n", + "\t'''\n", + "\tcounts_df_long: Pandas DataFrame containing columns named index, count, and replicate. index is chromosome.\n", + "\tchr_percent_df: Same as above except it is percent methylated CpGs.\n", + "\tThese objects are created by the function meth_histogram_by_chromosome()\n", + "\t'''\n", + "\tf,ax = plt.subplots(figsize=histogram_dims)\n", + "\t# sns.barplot(data=counts_df_long, y='count', x='index', hue='replicate', palette='Set2');\n", + "\tsns.barplot(data=chr_percent_df, y='CpG Count', x='Chromosome', hue='Sample', palette='Set2');\n", + "\tsns.despine()\n", + "\tplt.xticks(rotation=histogram_xaxis_label_rotation, ha='right')\n", + "\tplt.legend(bbox_to_anchor=(1,1));\n", + "\tplt.savefig(\"meth_histogram_by_chromosome_CpG_count.png\")\n", + "\n", + "\tf,ax = plt.subplots(figsize=histogram_dims)\n", + "\tsns.barplot(data=chr_percent_df, y='Percent CpGs Methylated', x='Chromosome', hue='Sample', palette='Set2');\n", + "\tsns.despine()\n", + "\tplt.xticks(rotation=histogram_xaxis_label_rotation, ha='right')\n", + "\tplt.legend(bbox_to_anchor=(1,1));\n", + "\tplt.savefig(\"meth_histogram_by_chromosome_Percent_CpGs.png\")\n", + "# Chromosome-Level Histogram Plotting ##########" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "counts_df_long, chr_percent_df = meth_histogram_by_chromosome(\"s3://exosomedx/pipeline_resources/references/methylation/hg38.chrom.sizes\",file_id_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "output_chromosome_histograms(counts_df_long, chr_percent_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "015455a9c6be9e7274ccecdbf26c5e74b1ea1b6d228cb963172aa34f90a4e9f8" + }, + "kernelspec": { + "display_name": "Python 3.10.2 ('methylseq')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/analysis/notebooks/tiled_stats.ipynb b/analysis/notebooks/tiled_stats.ipynb new file mode 100644 index 000000000..ebe435430 --- /dev/null +++ b/analysis/notebooks/tiled_stats.ipynb @@ -0,0 +1,960 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def make_tiled_stats(run_id:str,\n", + " rep_list:str,\n", + " base_data_directory:str = '/efs/scratch/methyl_qc_data/',\n", + " keep_chr_names:list = default_chr_list,\n", + " tile_win_size:int = 1000,\n", + " tile_step_size:int = 1000,\n", + " tile_cov_bases:int = 0,\n", + " verbose:bool = False) -> (pd.DataFrame, pd.DataFrame):\n", + "\n", + " import rpy2\n", + " from rpy2.robjects.packages import importr\n", + " import os\n", + " import rpy2.rinterface as rinterface\n", + " as_char = rinterface.baseenv['as.character']\n", + " \n", + " # Indices for (tiled) methylation R object\n", + " chromosome_column_index = 0\n", + " start_column_index = 1\n", + " end_column_index = 2\n", + " strand_column_index = 3\n", + " coverage_column_index = 4\n", + " meth_column_index = 5\n", + " unmeth_column_index = 6\n", + " \n", + " methylKit = importr('methylKit')\n", + " rutils = importr('utils')\n", + " genomation = importr('genomation')\n", + " \n", + " methobj_fn = base_data_directory+run_id+'/methobj.rds'\n", + "\n", + " # We define an R function that is a reformulated version of getCorrelation from MethylKit\n", + " # to clean up the output format\n", + " rpy2.robjects.r('''\n", + " getCorrelation_local <- function(object, method = c(\"pearson\",\"kendall\",\"spearman\")){\n", + " meth.mat = getData(object)[, object@numCs.index]/\n", + " (getData(object)[,object@numCs.index] + \n", + " getData(object)[,object@numTs.index]) \n", + "\n", + " names(meth.mat)=object@sample.ids\n", + " method <- match.arg(method)\n", + " return_data = cor(meth.mat,method=method, use = \"na.or.complete\")\n", + " return(return_data)\n", + " }''')\n", + "\n", + " methobj = rpy2.robjects.r('methobj = unite(readRDS(\\\"'+methobj_fn+'\\\"),min.per.group=1L)')\n", + " meth_correlations = rpy2.robjects.r('getCorrelation_local(methobj)')\n", + "\n", + " meth_correlations_df = pd.DataFrame(np.array(meth_correlations), index=rep_list, columns=rep_list)\n", + "\n", + " methobj_lowCov_fn = base_data_directory+run_id+'/methobj_lowcov.rds'\n", + " methobj_tiles_fn = base_data_directory+run_id+'/methobj_lowcov_tiles.rds'\n", + "\n", + " if os.path.isfile(methobj_tiles_fn):\n", + " methobj_tiles = rpy2.robjects.r.readRDS(methobj_tiles_fn)\n", + " else:\n", + " methobj_lowCov = rpy2.robjects.r.readRDS(methobj_lowCov_fn)\n", + " methobj_tiles = methylKit.tileMethylCounts(methobj_lowCov, win_size = tile_win_size, step_size = tile_step_size, cov_bases = tile_cov_bases)\n", + " rpy2.robjects.r.saveRDS(methobj_tiles,file=methobj_tiles_fn)\n", + "\n", + " tile_beta_df_fn = base_data_directory+run_id+'/tile_beta_df.csv'\n", + "\n", + " if os.path.isfile(tile_beta_df_fn):\n", + " tile_beta_df = pd.read_csv(tile_beta_df_fn, index_col=0)\n", + " else:\n", + " if verbose:\n", + " print('tile_beta_df.csv does not exist. Creating...')\n", + " tile_beta_df = tile_beta_to_csv(rep_list, methobj_tiles, keep_chr_names, verbose, tile_beta_df_fn = tile_beta_df_fn)\n", + "\n", + " return (tile_beta_df, meth_correlations_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def tile_beta_to_csv(rep_list:list,\n", + " methobj_tiles, #:rpy2.robjects.vectors.ListVector,\n", + " keep_chr_names:list = default_chr_list,\n", + " verbose:bool = False,\n", + " save_file:bool = True,\n", + " tile_beta_df_fn:str = 'tile_beta_df.csv') -> pd.DataFrame:\n", + " '''\n", + " Function taking rpy2 object methobj_tiles and saving the tiles beta values to a csv named tile_beta_df_fn.\n", + " '''\n", + "\n", + " import numpy as np\n", + " import pandas as pd\n", + " from methylqc.utils import beta\n", + " import rpy2\n", + " from rpy2.robjects.packages import importr\n", + " import rpy2.rinterface as rinterface\n", + " as_char = rinterface.baseenv['as.character']\n", + " rutils = importr('utils')\n", + "\n", + " # Indices for (tiled) methylation R object\n", + " chromosome_column_index = 0\n", + " start_column_index = 1\n", + " end_column_index = 2\n", + " strand_column_index = 3\n", + " coverage_column_index = 4\n", + " meth_column_index = 5\n", + " unmeth_column_index = 6\n", + "\n", + " tile_betaL = []\n", + " for i,tiled_counts_obj in enumerate(methobj_tiles):\n", + " if verbose:\n", + " print('Tiling beta values for '+rep_list[i])\n", + "\n", + " # We must be careful to index the R object with 1-based indexing, so the range of indices is shifted by 1.\n", + " chr_list = [as_char(tiled_counts_obj[chromosome_column_index].rx[int(i)])[0] for i in np.arange(1, len(tiled_counts_obj[chromosome_column_index])+1)]\n", + "\n", + " coord_matrix = pd.DataFrame({\n", + " 'chr': chr_list,\n", + " 'start': list(map(str,np.array(tiled_counts_obj[start_column_index]))),\n", + " 'end': list(map(str,np.array(tiled_counts_obj[end_column_index]))),\n", + " 'strand': list(map(str,np.array(tiled_counts_obj[strand_column_index])))})\n", + "\n", + " is_in_keep_chr = coord_matrix.loc[:,'chr'].isin(keep_chr_names)\n", + " meth_column = pd.Series(tiled_counts_obj[meth_column_index]).where(is_in_keep_chr).dropna().values\n", + " unmeth_column = pd.Series(tiled_counts_obj[unmeth_column_index]).where(is_in_keep_chr).dropna().values\n", + " coord_matrix = coord_matrix.where(is_in_keep_chr).dropna().values\n", + " tile_index = list(map(lambda x: '_'.join(x), coord_matrix))\n", + "\n", + " tile_beta_aux = pd.Series(beta(meth_column, unmeth_column, 0), index=tile_index, name = rep_list[i])\n", + "\n", + " tile_betaL.append(tile_beta_aux)\n", + "\n", + " dups = [q.index.where(q.index.duplicated()).dropna().values for q in tile_betaL]\n", + " tile_betaL_filt = [tile_betaL[i].mask(tile_betaL[i].index.isin(q)).dropna() for i,q in enumerate(dups)]\n", + " tile_beta_df = pd.concat(tile_betaL_filt, axis=1).fillna(0)\n", + "\n", + " if save_file:\n", + " tile_beta_df.to_csv(tile_beta_df_fn)\n", + " \n", + " return tile_beta_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# tileMethylCounts\n", + "\n", + "In python from rpy2, this ends up being \n", + "\n", + "```python\n", + "methobj_lowCov = rpy2.robjects.r.readRDS(methobj_lowCov_fn)\n", + "\n", + "methobj_tiles = methylKit.tileMethylCounts(methobj_lowCov, win_size = tile_win_size, step_size = tile_step_size, cov_bases = tile_cov_bases)\n", + "```\n", + "\n", + "I think it would be best to just put this in a simple R script? Now just to figure out how to get that to work.." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext rpy2.ipython" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Loading required package: GenomicRanges\n", + "\n", + "R[write to console]: Loading required package: stats4\n", + "\n", + "R[write to console]: Loading required package: BiocGenerics\n", + "\n", + "R[write to console]: \n", + "Attaching package: ‘BiocGenerics’\n", + "\n", + "\n", + "R[write to console]: The following objects are masked from ‘package:stats’:\n", + "\n", + " IQR, mad, sd, var, xtabs\n", + "\n", + "\n", + "R[write to console]: The following objects are masked from ‘package:base’:\n", + "\n", + " anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n", + " dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n", + " grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n", + " order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n", + " rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n", + " union, unique, unsplit, which.max, which.min\n", + "\n", + "\n", + "R[write to console]: Loading required package: S4Vectors\n", + "\n", + "R[write to console]: \n", + "Attaching package: ‘S4Vectors’\n", + "\n", + "\n", + "R[write to console]: The following objects are masked from ‘package:base’:\n", + "\n", + " expand.grid, I, unname\n", + "\n", + "\n", + "R[write to console]: Loading required package: IRanges\n", + "\n", + "R[write to console]: Loading required package: GenomeInfoDb\n", + "\n" + ] + } + ], + "source": [ + "%%R \n", + "library(methylKit)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%%R\n", + "methobj_lowCov <- readRDS(\"/efs/scratch/jeffrey/code/github.com/exosomedx/methylseq/tests/results/test_NSQCAM529_subsample_100k_200k/methylqc/methobj_lowcov.rds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──\n", + "\n", + "R[write to console]: ✔ ggplot2 3.4.2 ✔ purrr 1.0.1\n", + "✔ tibble 3.2.1 ✔ dplyr 1.1.2\n", + "✔ tidyr 1.3.0 ✔ stringr 1.5.0\n", + "✔ readr 2.1.4 ✔ forcats 1.0.0\n", + "\n", + "R[write to console]: ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n", + "✖ dplyr::collapse() masks IRanges::collapse()\n", + "✖ dplyr::combine() masks BiocGenerics::combine()\n", + "✖ dplyr::desc() masks IRanges::desc()\n", + "✖ tidyr::expand() masks S4Vectors::expand()\n", + "✖ dplyr::filter() masks stats::filter()\n", + "✖ dplyr::first() masks S4Vectors::first()\n", + "✖ dplyr::lag() masks stats::lag()\n", + "✖ ggplot2::Position() masks BiocGenerics::Position(), base::Position()\n", + "✖ purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()\n", + "✖ dplyr::rename() masks S4Vectors::rename()\n", + "✖ dplyr::select() masks methylKit::select()\n", + "✖ dplyr::slice() masks IRanges::slice()\n", + "✖ tidyr::unite() masks methylKit::unite()\n", + "\n" + ] + } + ], + "source": [ + "%%R \n", + "library(tidyverse)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "%%R \n", + "methobj_tiles <- tileMethylCounts(\n", + "\tmethobj_lowCov,\n", + "\twin_size = 1000, \n", + "\tstep_size = 1000, \n", + "\tcov_bases = 0\n", + ") " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "methylRawList object with 2 methylRaw objects\n", + "\n", + "methylRaw object with 1700 rows\n", + "--------------\n", + " chr start end strand coverage numCs numTs\n", + "1 chr1 923001 924000 * 3 2 1\n", + "2 chr1 1108001 1109000 * 23 23 0\n", + "3 chr1 1321001 1322000 * 3 3 0\n", + "4 chr1 1425001 1426000 * 10 5 5\n", + "5 chr1 1439001 1440000 * 9 6 3\n", + "6 chr1 1554001 1555000 * 6 6 0\n", + "--------------\n", + "sample.id: Healthy_1_200k \n", + "assembly: hg38 \n", + "context: CpG \n", + "resolution: region \n", + "\n", + "methylRaw object with 331 rows\n", + "--------------\n", + " chr start end strand coverage numCs numTs\n", + "1 chr1 1041001 1042000 * 18 17 1\n", + "2 chr1 2931001 2932000 * 21 21 0\n", + "3 chr1 4655001 4656000 * 3 0 3\n", + "4 chr1 4724001 4725000 * 6 4 2\n", + "5 chr1 6094001 6095000 * 18 16 2\n", + "6 chr1 9179001 9180000 * 6 6 0\n", + "--------------\n", + "sample.id: Healthy_1_100k \n", + "assembly: hg38 \n", + "context: CpG \n", + "resolution: region \n", + "\n", + "treatment: 0 0 \n" + ] + } + ], + "source": [ + "%%R \n", + "methobj_tiles " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interesting.. I definitely don't understanf how the tiling works here.. but it looks like the deeper sequenced one has more rows? Which intuitively makes sense, but yeah I guess some other stuff has to be done downstream to merge this stuff together." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It seems to just make a somewhat straightforward dataframe?? It shouldn't be too crazy to just plug that into pandas as a csv file?" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chrstartendstrandcoveragenumCsnumTs
0chr110410011042000*18171
1chr129310012932000*21210
2chr146550014656000*303
3chr147240014725000*642
4chr160940016095000*18162
........................
326phage_lambda4700148000*24024
327phage_lambda4800149000*12012
328plasmid_puc19c11000*39913890101
329plasmid_puc19c10012000*2241219249
330plasmid_puc19c20013000*70469311
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331 rows × 7 columns

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" + ], + "text/plain": [ + " chr start end strand coverage numCs numTs\n", + "0 chr1 1041001 1042000 * 18 17 1\n", + "1 chr1 2931001 2932000 * 21 21 0\n", + "2 chr1 4655001 4656000 * 3 0 3\n", + "3 chr1 4724001 4725000 * 6 4 2\n", + "4 chr1 6094001 6095000 * 18 16 2\n", + ".. ... ... ... ... ... ... ...\n", + "326 phage_lambda 47001 48000 * 24 0 24\n", + "327 phage_lambda 48001 49000 * 12 0 12\n", + "328 plasmid_puc19c 1 1000 * 3991 3890 101\n", + "329 plasmid_puc19c 1001 2000 * 2241 2192 49\n", + "330 plasmid_puc19c 2001 3000 * 704 693 11\n", + "\n", + "[331 rows x 7 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "methobj_tiles_df = pd.read_csv(\"methobj_tiles.csv\")\n", + "methobj_tiles_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well there you go? Pretty sure those are all the columns needed by the downstream stuff??" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 chr\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'as_char' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[19], line 11\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i,tiled_counts_obj \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(methobj_tiles_df):\n\u001b[1;32m 10\u001b[0m \t\u001b[38;5;28mprint\u001b[39m(i,tiled_counts_obj)\n\u001b[0;32m---> 11\u001b[0m \tchr_list \u001b[38;5;241m=\u001b[39m [as_char(tiled_counts_obj[chromosome_column_index]\u001b[38;5;241m.\u001b[39mrx[\u001b[38;5;28mint\u001b[39m(i)])[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mlen\u001b[39m(tiled_counts_obj[chromosome_column_index])\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m)]\n", + "Cell \u001b[0;32mIn[19], line 11\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i,tiled_counts_obj \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(methobj_tiles_df):\n\u001b[1;32m 10\u001b[0m \t\u001b[38;5;28mprint\u001b[39m(i,tiled_counts_obj)\n\u001b[0;32m---> 11\u001b[0m \tchr_list \u001b[38;5;241m=\u001b[39m [\u001b[43mas_char\u001b[49m(tiled_counts_obj[chromosome_column_index]\u001b[38;5;241m.\u001b[39mrx[\u001b[38;5;28mint\u001b[39m(i)])[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mlen\u001b[39m(tiled_counts_obj[chromosome_column_index])\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m)]\n", + "\u001b[0;31mNameError\u001b[0m: name 'as_char' is not defined" + ] + } + ], + "source": [ + "import numpy as np\n", + "chromosome_column_index = 0\n", + "start_column_index = 1\n", + "end_column_index = 2\n", + "strand_column_index = 3\n", + "coverage_column_index = 4\n", + "meth_column_index = 5\n", + "unmeth_column_index = 6\n", + "for i,tiled_counts_obj in enumerate(methobj_tiles_df):\n", + "\tprint(i,tiled_counts_obj)\n", + "\tchr_list = [as_char(tiled_counts_obj[chromosome_column_index].rx[int(i)])[0] for i in np.arange(1, len(tiled_counts_obj[chromosome_column_index])+1)]\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hmm.. maybe I'm actually missing something here??" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from rpy2.robjects.packages import importr\n", + "methylKit = importr('methylKit')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " ListVector with 7 elements.\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " " + ], + "text/plain": [ + " [RTYPES.INTSXP]\n", + "R classes: ('integer',)\n", + "[1, 0, 3, 2, ..., 12, 101, 49, 11]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "015455a9c6be9e7274ccecdbf26c5e74b1ea1b6d228cb963172aa34f90a4e9f8" + }, + "kernelspec": { + "display_name": "Python 3.10.2 ('methylseq')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assets/multiqc/other_picard_hs_metrics_multiqc_header.yml b/assets/multiqc/other_picard_hs_metrics_multiqc_header.yml new file mode 100644 index 000000000..bb7ba893d --- /dev/null +++ b/assets/multiqc/other_picard_hs_metrics_multiqc_header.yml @@ -0,0 +1,18 @@ +headers: + FOLD_80_BASE_PENALTY: + description: Fold 80 base penalty would appear as ? if median target coverage + is 0 + title: Fold 80 base penalty + FOLD_ENRICHMENT: + format: '{:,.2f}' + title: Fold enrichment + MEAN_TARGET_COVERAGE: + format: '{:,.2f}' + title: Mean target coverage +plot_type: table +id: Other Picard Hs metrics +parent_id: Picard Hs metrics +parent_name: Picard Hs metrics +config: + sortRows: true + table_title: Other Picard Hs metrics diff --git a/assets/multiqc/picard_on_off_rate_multiqc_header.txt b/assets/multiqc/picard_on_off_rate_multiqc_header.txt new file mode 100644 index 000000000..2b592c94b --- /dev/null +++ b/assets/multiqc/picard_on_off_rate_multiqc_header.txt @@ -0,0 +1,12 @@ +# parent_id: 'Picard Hs metrics' +# parent_name: "Picard Hs metrics" +# parent_description: "This section contains results collected from Picard Hs metrics" +# id: 'Hybcap on off target rate' +# section_name: 'Hybcap on off target rate' +# plot_type: 'bargraph' +# anchor: 'picard Hs' +# pconfig: +# id: "On off target rate" +# title: "On off target rate" +# xlab: "# Bases" +# cpswitch_counts_label: "Number of Bases" diff --git a/assets/multiqc/picard_uniformity_multiqc_header.yml b/assets/multiqc/picard_uniformity_multiqc_header.yml new file mode 100644 index 000000000..d53935056 --- /dev/null +++ b/assets/multiqc/picard_uniformity_multiqc_header.yml @@ -0,0 +1,14 @@ +id: Hybcap uniformity +anchor: picard Hs +parent_description: This section contains results collected from Picard Hs metrics +parent_id: Picard Hs metrics +parent_name: Picard Hs metrics +pconfig: + cpswitch_counts_label: Number of Bases + id: Uniformity + title: Uniformity + xlab: Coverage + ylab: Base covered at >X (%) + logswitch: True +plot_type: linegraph +section_name: Hybcap uniformity diff --git a/assets/schema_input.json b/assets/schema_input.json index ffbdcf3c1..3baa320e5 100644 --- a/assets/schema_input.json +++ b/assets/schema_input.json @@ -33,6 +33,12 @@ "exists": true, "pattern": "^[a-zA-Z0-9._-]+$", "errorMessage": "Invalid reference genome name" + }, + "group": { + "type": "string", + "pattern": "^\\S*$", + "errorMessage": "Group cannot contain spaces", + "meta": ["group"] } }, "required": ["sample", "fastq_1"] diff --git a/bin/basic_read_statistics.py b/bin/basic_read_statistics.py new file mode 100755 index 000000000..d0ff4aad3 --- /dev/null +++ b/bin/basic_read_statistics.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python + +""" +Per-sample read statistics from the merged MethylDackel bedGraph HDF5 and M-bias files. + +Outputs (current directory): + merged_mbias.csv MethylDackel M-bias tables for all samples, with a 'replicate' column + tab_id_value_counts_df.csv Distribution of per-CpG methylation (%) per sample + rnf_counts_id_df.csv Distribution of per-CpG read depth per sample + metrics_table.csv One row of summary metrics per sample + *.png Static plots of the above +""" + +import argparse +import re + +import h5py +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns + +__version__ = "0.0.2" + +# CpGs with fewer reads than this are counted as low depth in metrics_table.csv +LOW_DEPTH_THRESHOLD = 10 + + +def main(): + parser = argparse.ArgumentParser(description="Per-sample read statistics from bedGraph HDF5 and M-bias files") + parser.add_argument("--bedGraph_hdf5", required=True, help="Single HDF5 file with bedGraph data from all samples") + parser.add_argument("--mbias_files", required=True, help="Space-separated MethylDackel M-bias files, in the same order as --meta") + parser.add_argument("--meta", required=True, help="Sample metadata, e.g. [[id:healthy_1, group:healthy], [id:disease_1, group:disease]]") + parser.add_argument("--id_file", required=True, help="Output CSV mapping sample id to M-bias file") + parser.add_argument("--merged_mbias", required=True, help="Output merged M-bias CSV file") + parser.add_argument("--version", action="version", version=__version__) + args = parser.parse_args() + + mbias_files = args.mbias_files.split() + sample_ids = re.findall(r"id:([^,\]]+)", args.meta) + if len(sample_ids) != len(mbias_files): + raise SystemExit(f"Got {len(mbias_files)} M-bias files but {len(sample_ids)} sample ids") + + file_id_df = pd.DataFrame({"id": sample_ids, "file": mbias_files}) + file_id_df.to_csv(args.id_file, index=False) + + mbias_df = pd.concat( + [pd.read_csv(f, sep="\t").assign(replicate=sample) for sample, f in zip(sample_ids, mbias_files)], + ignore_index=True, + ) + mbias_df.to_csv(args.merged_mbias, index=False) + + meth_distL = [] + depth_distL = [] + metricsL = [] + with h5py.File(args.bedGraph_hdf5, "r") as hdf5: + for sample in sample_ids: + # data columns: start, end, percent methylation, methylated reads, unmethylated reads + data = hdf5[sample + "/data"][:] + meth_reads = data[:, 3].astype(float) + depth = meth_reads + data[:, 4] + covered = depth > 0 + pct_meth = np.divide(100 * meth_reads, depth, out=np.zeros_like(depth), where=covered) + + values, counts = np.unique(np.rint(pct_meth[covered]).astype(int), return_counts=True) + meth_distL.append(pd.DataFrame({"percent_methylation": values, "count": counts, "replicate": sample})) + + values, counts = np.unique(depth[covered].astype(int), return_counts=True) + depth_distL.append(pd.DataFrame({"read count": values, "number of read count occurrences": counts, "replicate": sample})) + + n_cpg = int(covered.sum()) + metricsL.append({ + "sample": sample, + "CpGs covered": n_cpg, + "median read #": float(np.median(depth[covered])) if n_cpg else 0.0, + "mean read #": float(depth[covered].mean()) if n_cpg else 0.0, + f"% CpGs with < {LOW_DEPTH_THRESHOLD} reads": pct(np.sum(depth[covered] < LOW_DEPTH_THRESHOLD), n_cpg), + "mean CpG methylation (%)": float(pct_meth[covered].mean()) if n_cpg else 0.0, + "% CpGs partially methylated": pct(np.sum((pct_meth > 0) & (pct_meth < 100) & covered), n_cpg), + }) + + meth_dist_df = pd.concat(meth_distL, ignore_index=True) + meth_dist_df.to_csv("tab_id_value_counts_df.csv", index=False) + + depth_dist_df = pd.concat(depth_distL, ignore_index=True) + depth_dist_df.to_csv("rnf_counts_id_df.csv", index=False) + + metrics_table = pd.DataFrame(metricsL).set_index("sample").round(3) + metrics_table.to_csv("metrics_table.csv") + + plot_basic_read_statistics(depth_dist_df, mbias_df, meth_dist_df) + + +def pct(numerator, denominator) -> float: + return float(100 * numerator / denominator) if denominator else 0.0 + + +def plot_basic_read_statistics(depth_dist_df: pd.DataFrame, mbias_df: pd.DataFrame, meth_dist_df: pd.DataFrame) -> None: + f, ax = plt.subplots() + sns.lineplot(data=meth_dist_df, x="percent_methylation", y="count", hue="replicate", palette="Set2", ax=ax) + ax.set_yscale("log") + ax.set_xlabel("CpG methylation (%)") + ax.set_ylabel("Number of CpGs") + sns.despine(left=True, bottom=True) + f.savefig("combined_coverage_distributions.png", bbox_inches="tight") + plt.close(f) + + g = sns.FacetGrid(depth_dist_df, col="replicate", height=4, aspect=2, col_wrap=2) + g.map(sns.lineplot, "read count", "number of read count occurrences") + g.map(sns.scatterplot, "read count", "number of read count occurrences", s=20, color="gray") + g.set(xscale="log") + g.savefig("read_count_occurence_frequencies.png") + plt.close(g.figure) + + for strand in ["OT", "OB"]: + for column, label in [("nMethylated", "methylated"), ("nUnmethylated", "unmethylated")]: + g = sns.FacetGrid( + mbias_df[mbias_df["Strand"] == strand], + col="replicate", height=4, aspect=2, col_wrap=2, hue="Read", palette="Set1", sharey=False, + ) + g.map(sns.lineplot, "Position", column) + g.set(ylim=(0, None)) + g.add_legend() + g.savefig(f"read_bias_in_{label}_{strand}_strand.png") + plt.close(g.figure) + + +if __name__ == "__main__": + main() diff --git a/bin/collect_picardHs.py b/bin/collect_picardHs.py new file mode 100755 index 000000000..598f19857 --- /dev/null +++ b/bin/collect_picardHs.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python +import argparse +import pandas as pd +import numpy as np +import pathlib +import seaborn as sns +import os +import yaml + +__version__ = '0.0.1' + +# Picard on off target barplot +def generate_on_off_target_tsv(picard_path, sample_name, header): + + # Format picard result data + try: + picard_sample = pd.read_csv(f'{picard_path}/{sample_name}.CollectHsMetrics.coverage_metrics', sep='\t', + skiprows=6, nrows=1) + except: + return + barplot_data = picard_sample[['ON_BAIT_BASES', 'NEAR_BAIT_BASES', 'OFF_BAIT_BASES']] + barplot_data = barplot_data.melt(var_name='type', value_name='bases').set_index('type') + barplot_data.index.name = None + + + # Add header + with open(header, "r") as input: + with open(f"{sample_name}_on_off_target_mqc.tsv", "w") as output: + for line in input: + output.write(line) + for i, row in barplot_data.iterrows(): + output.write('\n') + output.write(f'{row.name}\t{row[0]}') + +def wrapper_tsv(picard_path, sample_sheet, header): + samplesheet = pd.read_csv(sample_sheet, sep=',') + for sample in samplesheet['sample']: + generate_on_off_target_tsv(picard_path, sample, header) + + +# Picard uniformity lineplot +def picard_uniformity(picard_path): + column_header = None + for file in os.listdir(picard_path): + if file.endswith(".coverage_metrics"): + sample_name = file.split('.')[0] + file_path = os.path.join(picard_path, file) + df = pd.read_csv(file_path, sep='\t',skiprows=6, nrows=1) + + # Get data for uniformity line plot + lineplot_col = list(df.columns[df.columns.to_series().str.contains('PCT_TARGET_BASES')]) + lineplot_col_filtered = [] + for col in lineplot_col: + if int(col.replace('PCT_TARGET_BASES_', '').replace('X', '')) < 1000: + lineplot_col_filtered.append(col) + lineplot_data = df[lineplot_col_filtered] + + # Format data for lineplot + if column_header is None: + column_header = [float(x.replace('PCT_TARGET_BASES_', '').replace('X', '')) for x in lineplot_col_filtered] + lineplot_df = pd.DataFrame(columns = column_header) + lineplot_data.columns = column_header + lineplot_data.index = [sample_name] + lineplot_df = pd.concat([lineplot_df, lineplot_data]) + + # Build dictionary for lineplot data: + lineplot_df = lineplot_df*100 + lineplot_dict = dict() + for i, row in lineplot_df.iterrows(): + row_dict = dict() + for indices in row.index: + row_dict[indices] = row[indices] + lineplot_dict[row.name] = row_dict + lineplot_data_dict = {'data': lineplot_dict} + + + with open('uniformity.yml', 'w') as outfile: + yaml.dump(lineplot_data_dict, outfile, default_flow_style=False) + +# Picard other metrics in table +def picard_other_metrics(picard_path, table_config): + table_df = pd.DataFrame() + probe_set = None + for file in os.listdir(picard_path): + if file.endswith(".coverage_metrics"): + sample_name = file.split('.')[0] + file_path = os.path.join(picard_path, file) + df = pd.read_csv(file_path, sep='\t',skiprows=6, nrows=1) + + # Get data for table output + table_data = df[['FOLD_ENRICHMENT','FOLD_80_BASE_PENALTY','MEAN_TARGET_COVERAGE']] + table_data.index = [sample_name] + try: + table_data['FOLD_80_BASE_PENALTY'] = round(table_data['FOLD_80_BASE_PENALTY'],2) + except: + pass + table_df = pd.concat([table_df, table_data]) + + # Get probe set name + if probe_set is None: + probe_set = str(df['BAIT_SET'].unique()[0]) + + # Build dictionary for table data: + table_dict = dict() + for i, row in table_df.iterrows(): + row_dict = dict() + for indices in row.index: + try: + row_dict[indices] = row[indices].item() + except: + row_dict[indices] = row[indices] + table_dict[row.name] = row_dict + + # Convert table config to a dictionary to be edited: + with open(table_config, 'r') as stream: + table = yaml.safe_load(stream) + + # Add probe set name: + table['description'] = f'Bait set used to generate all picard result is: {probe_set}' + + # Add data: + table['data'] = table_dict + + with open('picard_other_metrics_mqc.yml', 'w') as outfile: + yaml.dump(table, outfile, default_flow_style=False) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="""Picard Hs visuals""" + ) + parser.add_argument("picard_path", type=pathlib.Path) + parser.add_argument("sample_sheet", type=pathlib.Path) + parser.add_argument("bargraph_header", type=pathlib.Path) + parser.add_argument("table_config", type=pathlib.Path) + parser.add_argument('--version', action='version', version=__version__) + + args = parser.parse_args() + wrapper_tsv(args.picard_path, args.sample_sheet, args.bargraph_header) + picard_uniformity(args.picard_path) + picard_other_metrics(args.picard_path, args.table_config) + diff --git a/bin/control_stats.py b/bin/control_stats.py new file mode 100755 index 000000000..b49b21b85 --- /dev/null +++ b/bin/control_stats.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python + +import argparse +import rpy2 +from rpy2.robjects.packages import importr +import pandas as pd +import numpy as np + +methylKit = importr('methylKit') + + + +def main(): + parser = argparse.ArgumentParser(description='Create stats for methylation controls') + parser.add_argument('--methobj', required=True, help='Path to methobj file') + parser.add_argument('--stats_out', required=True, help='Path to output control stats CSV file') + args = parser.parse_args() + + methobj = rpy2.robjects.r.readRDS(args.methobj) + control_stats_df = create_control_stats_table(methobj) + control_stats_df.to_csv(args.stats_out) + + + +def create_control_stats_table(methobj, spike_in_controls:list = ['phage_lambda', 'plasmid_puc19c']) -> pd.DataFrame: + chr_index = 0 + coverage_index = 4 + numCs_index = 5 + pct_methylatedL = [] + rep_list = list(methylKit.getSampleID(methobj)) + for i,this_rep in enumerate(rep_list): + pct_methylatedL_aux = [] + for this_control in spike_in_controls: + array_selection = np.array(methobj[i][chr_index]) == this_control + if np.any(array_selection): + pct_methylated = 100*np.array(methobj[i][numCs_index])[array_selection] / np.array(methobj[i][coverage_index])[array_selection] + pct_methylated = np.mean(pct_methylated) + pct_methylatedL_aux.append(pct_methylated) + else: + pct_methylatedL_aux.append(0) + pct_methylatedL.append(pct_methylatedL_aux) + return pd.DataFrame(pct_methylatedL, index=rep_list, columns=spike_in_controls) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/bin/make_combined_hdf5.py b/bin/make_combined_hdf5.py new file mode 100755 index 000000000..a5847e0d6 --- /dev/null +++ b/bin/make_combined_hdf5.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python + +""" +Gather per-sample MethylDackel bedGraph files into a single HDF5 file. + +Each sample is stored as a group named by its sample id with two datasets: + /chr chromosome name of each CpG + /data integer matrix: start, end, percent methylation, methylated reads, unmethylated reads +Only the primary human chromosomes (chr1-22, X, Y, M) are kept. +""" + +import argparse +import re + +import h5py +import pandas as pd + +KEEP_CHROMOSOMES = [f"chr{i}" for i in range(1, 23)] + ["chrX", "chrY", "chrM"] + + +def main(): + parser = argparse.ArgumentParser(description="Gather bedGraph files into a single HDF5 file") + parser.add_argument("--input_files", required=True, help="Space-separated bedGraph files, in the same order as --meta") + parser.add_argument("--meta", required=True, help="Sample metadata, e.g. [[id:healthy_1, group:healthy], [id:disease_1, group:disease]]") + parser.add_argument("--id_file", required=True, help="Output CSV mapping sample id to bedGraph file") + parser.add_argument("--hdf5", required=True, help="Output HDF5 file") + args = parser.parse_args() + + input_files = args.input_files.split() + sample_ids = re.findall(r"id:([^,\]]+)", args.meta) + if len(sample_ids) != len(input_files): + raise SystemExit(f"Got {len(input_files)} bedGraph files but {len(sample_ids)} sample ids") + + pd.DataFrame({"id": sample_ids, "file": input_files}).to_csv(args.id_file, index=False) + + with h5py.File(args.hdf5, "w") as hdf5: + for sample, bedgraph in zip(sample_ids, input_files): + insert_bedgraph_into_hdf5(bedgraph, sample, hdf5) + + +def insert_bedgraph_into_hdf5(bedgraph_file: str, sample: str, hdf5: h5py.File) -> None: + columns = ["chr", "start", "end", "percent_methylation", "meth_reads", "unmeth_reads"] + try: + tab = pd.read_table(bedgraph_file, header=None, skiprows=1, names=columns) + except pd.errors.EmptyDataError: + tab = pd.DataFrame(columns=columns) + tab = tab[tab["chr"].isin(KEEP_CHROMOSOMES)] + + hdf5.create_group(sample) + hdf5.create_dataset( + f"{sample}/chr", + data=tab["chr"].to_numpy().astype(object), + dtype=h5py.string_dtype(encoding="utf-8"), + compression="gzip", + ) + hdf5.create_dataset(f"{sample}/data", data=tab.iloc[:, 1:].to_numpy(dtype="i"), compression="gzip") + + +if __name__ == "__main__": + main() diff --git a/bin/make_combined_methobj.R b/bin/make_combined_methobj.R new file mode 100755 index 000000000..54083801d --- /dev/null +++ b/bin/make_combined_methobj.R @@ -0,0 +1,86 @@ +#!/usr/bin/env Rscript + +invisible( lapply(c( + "optparse", + "methylKit" +), library, character.only=T)) + +option_list = list( + make_option(c("-i", "--input_files"), type="character", default=NULL, help="List of input methylkit files (i.e. 'healthy_1.methylkit healthy_2.methylkit disease_1.methylkit disease_2.methylkit'", metavar="character"), + make_option(c("-m", "--meta"), type="character", default=NULL, help="Sample ID and group information metadata (i.e. [[id:healthy_1, group:healthy], [id:healthy_2, group:healthy], [id:disease_1, group:disease], [id:disease_2, group:disease]]", metavar="character") +) +opt_parser = OptionParser(option_list=option_list) +opt = parse_args(opt_parser) + +# Validate and read input +if (is.null(opt$input_files)){ + print_help(opt_parser) + stop("Input methylkit files needs to be provided!") +} else { + input_files_string = opt$input_files +} + +if (is.null(opt$meta)){ + print_help(opt_parser) + stop("meta data string needs to be provided!") +} else { + meta_string = opt$meta +} + +get_file_list <- function(file_string){ + + # Removing square brackets from the string + cleaned_string = gsub("^\\[|\\]$", "", file_string) + + # Splitting the string into element[s + file_vector = unlist(strsplit(cleaned_string, " \\s*")) # delimit by space + + return(as.list(file_vector)) +} + +char2intVec <- function(charVec){ + # Find unique values and sort them alphabetically + unique_values = sort(unique(charVec)) + + # Assign numbers based on alphabetical rank + converted_vector = match(charVec, unique_values) - 1 + + return(converted_vector) +} + + +methylkit_files <- get_file_list(input_files_string) + +# Extracting id values +id_values <- regmatches(meta_string, gregexpr("(?<=id:)[^,\\]]+", meta_string, perl = TRUE))[[1]] + +# Extracting group values +group_values <- regmatches(meta_string, gregexpr("(?<=group:)[^,\\]]+", meta_string, perl = TRUE))[[1]] + +# Convert group character names to integers (i.e. c("healthy", "healthy", "disease", "disease") --> c(1,1,0,0)) +group_values_int <- char2intVec(group_values) + +# Samplesheets without a group column get a single treatment group +if (length(group_values_int) == 0) { + group_values_int <- rep(0, length(id_values)) +} + +methobj <- methRead(methylkit_files, + sample.id = as.list(id_values), + assembly="hg38", + treatment=group_values_int, + context="CpG", + mincov = 10 +) + +saveRDS(methobj, file="methobj.rds") + +methobj_lowcov <- methRead(methylkit_files, + sample.id = as.list(id_values), + assembly="hg38", + treatment=group_values_int, + context="CpG", + mincov = 3 +) + +saveRDS(methobj_lowcov, file="methobj_lowcov.rds") diff --git a/bin/merge_annotation_stats.py b/bin/merge_annotation_stats.py new file mode 100755 index 000000000..3a6f69bd2 --- /dev/null +++ b/bin/merge_annotation_stats.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python + +import argparse +import rpy2 +from rpy2.robjects.packages import importr +import rpy2.rinterface as rinterface +import pandas as pd +import numpy as np + +as_char = rinterface.baseenv['as.character'] +methylKit = importr('methylKit') +genomation = importr('genomation') + + + +def main(): + parser = argparse.ArgumentParser(description='Create flat csv files from twist and repeat annotation RDS files') + parser.add_argument('--methobj', required=True, help='Path to methobj file') + parser.add_argument('--methobj_twist_annotation', required=True, help='Path to methobj twist annotation file') + parser.add_argument('--cpg_obj', required=True, help='Path to methobj file') + parser.add_argument('--methobj_repeat_annotation', required=True, help='Path to output control stats CSV file') + parser.add_argument('--twist_annotation_csv', required=True, help='Output CSV for twist annotation') + parser.add_argument('--repeat_annotation_csv', required=True, help='Output CSV for repeat annotation') + args = parser.parse_args() + + methobj = rpy2.robjects.r.readRDS(args.methobj) + methobj_twist_annotation = rpy2.robjects.r.readRDS(args.methobj_twist_annotation) + cpg_obj = rpy2.robjects.r.readRDS(args.cpg_obj) + methobj_repeat_annotation = rpy2.robjects.r.readRDS(args.methobj_repeat_annotation) + + twist_stats_df = output_twist_annot_stats(methobj, methobj_twist_annotation,cpg_obj) + twist_stats_df.to_csv(args.twist_annotation_csv) + + repeat_stats_df = output_repeat_annot_stats(methobj, methobj_repeat_annotation) + repeat_stats_df.to_csv(args.repeat_annotation_csv) + + +# Twist Annotation Statistics ########## +def output_twist_annot_stats(methobj,methobj_twist_annotation,cpg_obj) -> None: + rep_list = list(methylKit.getSampleID(methobj)) + cpg_obj_names = np.array(rpy2.robjects.r.names(cpg_obj)) + cpg_stats = [np.array(genomation.getFeatsWithTargetsStats(q, percentage=True))[0] for q in methobj_twist_annotation] + cpg_stats_df = pd.DataFrame(np.array(np.array_split(cpg_stats,len(cpg_obj_names))), index=cpg_obj_names, columns=rep_list) + return cpg_stats_df + + +# Repeat Annotation Statistics ########## +def output_repeat_annot_stats(methobj,methobj_repeat_annotation) -> None: + annotation_description:str = 'UCSC RepeatMasker chm13v2.0_rmsk' + rep_list = list(methylKit.getSampleID(methobj)) + cpg_stats = [np.array(genomation.getFeatsWithTargetsStats(q, percentage=True))[0] for q in methobj_repeat_annotation] + cpg_stats_df = pd.DataFrame(cpg_stats, index=rep_list, columns=['Repeats from '+annotation_description]) + return cpg_stats_df + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/bin/merge_tiled_stats.py b/bin/merge_tiled_stats.py new file mode 100755 index 000000000..fed8f4049 --- /dev/null +++ b/bin/merge_tiled_stats.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python + +""" +Merge tiled methylation counts (methylKit methylRawList from tile_methyl_counts.R) into one +table of beta values: one row per tile (chr_start_end), one column per sample. + +Tiles outside chr1-22, X, Y, M are dropped. Tiles not covered in a sample are left empty. +""" + +import argparse + +import pandas as pd +import rpy2.robjects as ro +from rpy2.robjects import pandas2ri +from rpy2.robjects.conversion import localconverter +from rpy2.robjects.packages import importr + +KEEP_CHROMOSOMES = [f"chr{i}" for i in range(1, 23)] + ["chrX", "chrY", "chrM"] + +importr("methylKit") + +TILES_TO_DATAFRAME = ro.r(""" +function(obj) { + ids <- getSampleID(obj) + do.call(rbind, lapply(seq_along(obj), function(i) { + d <- getData(obj[[i]]) + data.frame( + sample = rep(ids[i], nrow(d)), + chr = as.character(d$chr), + tile = paste(d$chr, d$start, d$end, sep = "_"), + numCs = d$numCs, + numTs = d$numTs, + stringsAsFactors = FALSE + ) + })) +} +""") + + +def main(): + parser = argparse.ArgumentParser(description="Merge tiled beta values across samples") + parser.add_argument("--tiled_methobj", required=True, help="Tiled methylRawList RDS file") + parser.add_argument("--meta", required=False, help="Unused; sample ids are read from the RDS file") + parser.add_argument("--output", required=True, help="Output CSV of tile beta values") + args = parser.parse_args() + + tiled_methobj = ro.r["readRDS"](args.tiled_methobj) + with localconverter(ro.default_converter + pandas2ri.converter): + tiles = ro.conversion.rpy2py(TILES_TO_DATAFRAME(tiled_methobj)) + + tile_beta_df = tile_beta_table(tiles) + tile_beta_df.to_csv(args.output) + + +def tile_beta_table(tiles: pd.DataFrame) -> pd.DataFrame: + tiles = tiles[tiles["chr"].isin(KEEP_CHROMOSOMES)].drop_duplicates(subset=["sample", "tile"]) + depth = tiles["numCs"] + tiles["numTs"] + tiles = tiles.assign(beta=tiles["numCs"] / depth.where(depth > 0)) + sample_order = list(dict.fromkeys(tiles["sample"])) + table = tiles.pivot(index="tile", columns="sample", values="beta") + return table.reindex(columns=sample_order) + + +if __name__ == "__main__": + main() diff --git a/bin/meth_histogram_by_chromosome.py b/bin/meth_histogram_by_chromosome.py new file mode 100755 index 000000000..c013aed69 --- /dev/null +++ b/bin/meth_histogram_by_chromosome.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python + +""" +Per-chromosome CpG counts and methylation from MethylDackel bedGraph files. + +A CpG counts as methylated when its methylation level is above 0 (partially or fully methylated). + +Outputs: + --counts_df_long long table of methylated CpG counts: index (chromosome), replicate, count + --chr_percent_df long table: Sample, Chromosome, Methylated CpG Count, CpG Count, Percent CpGs Methylated + meth_histogram_by_chromosome_CpG_count.png, meth_histogram_by_chromosome_Percent_CpGs.png +""" + +import argparse +import re + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns + +KEEP_CHROMOSOMES = [f"chr{i}" for i in range(1, 23)] + ["chrX", "chrY", "chrM"] +CONTROL_CHROMOSOMES = ["phage_lambda", "plasmid_puc19c"] + + +def main(): + parser = argparse.ArgumentParser(description="Per-chromosome CpG methylation from bedGraph files") + parser.add_argument("--input_files", required=True, help="Space-separated bedGraph files, in the same order as --meta") + parser.add_argument("--meta", required=True, help="Sample metadata, e.g. [[id:healthy_1, group:healthy], [id:disease_1, group:disease]]") + parser.add_argument("--hg38_chrom_sizes", required=True, help="Chromosome sizes file (two columns: name, length)") + parser.add_argument("--counts_df_long", required=True, help="Output CSV of methylated CpG counts per chromosome") + parser.add_argument("--chr_percent_df", required=True, help="Output CSV of CpG counts and percent methylated per chromosome") + args = parser.parse_args() + + input_files = args.input_files.split() + sample_ids = re.findall(r"id:([^,\]]+)", args.meta) + if len(sample_ids) != len(input_files): + raise SystemExit(f"Got {len(input_files)} bedGraph files but {len(sample_ids)} sample ids") + + # Only chromosomes present in the sizes file are reported (plus the spike-in controls) + chrom_sizes = pd.read_csv(args.hg38_chrom_sizes, sep="\t", header=None, usecols=[0], names=["chromosome"]) + chromosomes = [c for c in KEEP_CHROMOSOMES if c in set(chrom_sizes["chromosome"])] + CONTROL_CHROMOSOMES + + chr_percent_df = pd.concat( + [chromosome_stats(f, sample, chromosomes) for sample, f in zip(sample_ids, input_files)], + ignore_index=True, + ) + chr_percent_df.to_csv(args.chr_percent_df) + + counts_df_long = chr_percent_df.rename( + columns={"Chromosome": "index", "Sample": "replicate", "Methylated CpG Count": "count"} + )[["index", "replicate", "count"]] + counts_df_long.to_csv(args.counts_df_long) + + plot_chromosome_histograms(chr_percent_df) + + +def chromosome_stats(bedgraph_file: str, sample: str, chromosomes: list) -> pd.DataFrame: + try: + tab = pd.read_table(bedgraph_file, header=None, skiprows=1, usecols=[0, 3], names=["chr", "percent_methylation"]) + except pd.errors.EmptyDataError: + tab = pd.DataFrame({"chr": pd.Series(dtype=str), "percent_methylation": pd.Series(dtype=float)}) + cpg_count = tab.groupby("chr").size().reindex(chromosomes, fill_value=0) + meth_count = (tab["percent_methylation"] > 0).groupby(tab["chr"]).sum().reindex(chromosomes, fill_value=0).astype(int) + percent = (100 * meth_count / cpg_count.where(cpg_count > 0)).fillna(0) + return pd.DataFrame({ + "Sample": sample, + "Chromosome": chromosomes, + "Methylated CpG Count": meth_count.to_numpy(), + "CpG Count": cpg_count.to_numpy(), + "Percent CpGs Methylated": percent.to_numpy(), + }) + + +def plot_chromosome_histograms(chr_percent_df: pd.DataFrame, dims=(8, 4), rotation=45) -> None: + for column, filename in [ + ("CpG Count", "meth_histogram_by_chromosome_CpG_count.png"), + ("Percent CpGs Methylated", "meth_histogram_by_chromosome_Percent_CpGs.png"), + ]: + f, ax = plt.subplots(figsize=dims) + sns.barplot(data=chr_percent_df, y=column, x="Chromosome", hue="Sample", palette="Set2", ax=ax) + sns.despine() + plt.xticks(rotation=rotation, ha="right") + ax.legend(bbox_to_anchor=(1, 1)) + f.savefig(filename, bbox_inches="tight") + plt.close(f) + + +if __name__ == "__main__": + main() diff --git a/bin/meth_stats.py b/bin/meth_stats.py new file mode 100755 index 000000000..43809ca6d --- /dev/null +++ b/bin/meth_stats.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python +import argparse +from collections import defaultdict as dd +import pandas as pd +import pathlib + +__version__ = '0.0.1' + +def get_controls(file: pd.DataFrame): + df = pd.read_csv(file, sep='\t', skiprows=1, names=['contig', 'start', 'stop', 'percent','C', 'T']) + df = df[(df['contig'] == 'phage_lambda') | (df['contig'] == 'plasmid_puc19c')] + df['cov'] = df['C'] + df['T'] + return df + +def sn_sv_metrics(frac_c, con_type, thresh=0.99): + if con_type == 'plasmid_puc19c': + if frac_c >= thresh: + return 'TP' + return 'FN' + if con_type == 'phage_lambda': + if frac_c <= thresh: + return 'TN' + return 'FP' + +def process_controls(methyl_frame, min_cov=10, thresh=0.99): + this_frame = methyl_frame[(methyl_frame['contig'] == 'phage_lambda') | (methyl_frame['contig'] == 'plasmid_puc19c')] + this_frame['cov'] = this_frame['C'] + this_frame['T'] + this_frame = this_frame[this_frame['cov'] >= min_cov] + this_frame['frac_c'] = this_frame['C']/this_frame['cov'] + this_frame['frac_t'] = this_frame['T']/this_frame['cov'] + if this_frame.empty: + this_frame['metrics'] = None + else: + this_frame['metrics'] = this_frame.apply(lambda x: sn_sv_metrics(x.frac_c, x.contig, thresh), axis=1) + return this_frame + + +class ReportSN_SV: + def __init__(self, methylframe): + self.methyl_frame = methylframe + + self.mdict = dd(int) + + self.tp, self.tn, self.fn, self.fn = 0, 0, 0, 0 + + self.sn = 0 + self.sv = 0 + self.tpn = 0 + + self.__processed = False + + def __repr__(self): + if self.__processed: + return (f"Sensitivity: {self.sn}\n" + f"Specificity: {self.sv}\n" + f"Negative Predictive Value: {self.tpn}\n" + f"TP: {self.tp}\n" + f"FP: {self.fp}\n" + f"TN: {self.tn}\n" + f"FN: {self.fn}") + return "Not Processed Yet" + + def get_sn_sv(self): + mdict = dd(int) + recs = self.methyl_frame.groupby('metrics')['cov'].count().to_dict() + for k, v in recs.items(): + mdict[k] = v + + self.tp, self.fp, self.fn, self.tn = mdict['TP'], mdict['FP'], mdict['FN'], mdict['TN'] + + try: + self.sn = self.tp / (self.tp + self.fn) + except ZeroDivisionError: + self.sn = 'div0' + try: + self.sv = self.tp / (self.tp + self.fp) + except ZeroDivisionError: + self.sv = 'div0' + + try: + self.tpn = self.tn / (self.tn + self.fn) + except ZeroDivisionError: + self.tpn = 'div0' + self.__processed = True + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Method for finding sensitivity and specificity of contols for any given sample") + parser.add_argument('bedgraph', type=pathlib.Path) + parser.add_argument('out', type=str) + parser.add_argument('--threshold', type=float, default=0.99) + parser.add_argument('--mincov', type=int, default=10) + + args = parser.parse_args() + + df = get_controls(args.bedgraph) + pc_df = process_controls(df, min_cov=args.mincov, thresh=args.threshold) + snsv = ReportSN_SV(pc_df) + snsv.get_sn_sv() + with open(args.out, 'w') as fo: + fo.write(snsv.__repr__()) diff --git a/bin/methylqc_multiqc.py b/bin/methylqc_multiqc.py new file mode 100755 index 000000000..ed02c86a6 --- /dev/null +++ b/bin/methylqc_multiqc.py @@ -0,0 +1,252 @@ +#!/usr/bin/env python + +""" +Convert EvolvDx methyl_qc outputs into MultiQC custom content (*_mqc.json). + +Every input is optional; a section is written only for the inputs that are given. +All sections are grouped under one "EvolvDx methylation QC" heading in the report. +""" + +import argparse +import json + +import numpy as np +import pandas as pd + +__version__ = "0.0.1" + +PARENT = { + "parent_id": "evolvdx_methylqc", + "parent_name": "EvolvDx methylation QC", + "parent_description": "Cohort-level methylation QC from the EvolvDx methyl_qc subworkflow (--run_methylqc).", +} + +SPIKE_IN_NAMES = {"phage_lambda": "Lambda", "plasmid_puc19c": "pUC19"} + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--control_stats", help="control_stats.csv from CONTROL_STATS") + parser.add_argument("--read_metrics", help="metrics_table.csv from BASIC_READ_STATISTICS") + parser.add_argument("--meth_distribution", help="tab_id_value_counts_df.csv from BASIC_READ_STATISTICS") + parser.add_argument("--depth_distribution", help="rnf_counts_id_df.csv from BASIC_READ_STATISTICS") + parser.add_argument("--mbias", help="merged_mbias.csv from BASIC_READ_STATISTICS") + parser.add_argument("--chr_percent", help="chr_percent_df.csv from METH_HISTOGRAM_BY_CHROMOSOME") + parser.add_argument("--twist_annotation", help="twist_annotation.csv from MERGE_ANNOTATION_STATS") + parser.add_argument("--repeat_annotation", help="repeat_annotation.csv from MERGE_ANNOTATION_STATS") + parser.add_argument("--tiled_beta", help="merge_tiled_stats.csv from MERGE_TILED_STATS") + parser.add_argument("--version", action="version", version=__version__) + args = parser.parse_args() + + general_stats = {} + general_headers = {} + + if args.control_stats: + df = pd.read_csv(args.control_stats, index_col=0) + for column in df.columns: + label = SPIKE_IN_NAMES.get(column, column) + key = f"{column}_meth" + add_general_stat(general_stats, df[column], key) + general_headers[key] = { + "title": f"{label} meth", + "description": f"Mean CpG methylation of the {label} spike-in control (%)", + "min": 0, "max": 100, "suffix": "%", "scale": "OrRd", "format": "{:,.1f}", + } + + if args.read_metrics: + df = pd.read_csv(args.read_metrics, index_col=0) + add_general_stat(general_stats, df["mean CpG methylation (%)"], "mean_cpg_meth") + general_headers["mean_cpg_meth"] = { + "title": "CpG meth", + "description": "Mean methylation across covered CpGs (%)", + "min": 0, "max": 100, "suffix": "%", "scale": "RdYlBu-rev", "format": "{:,.1f}", + } + add_general_stat(general_stats, df["median read #"], "median_cpg_depth") + general_headers["median_cpg_depth"] = { + "title": "CpG depth", + "description": "Median read depth across covered CpGs", + "min": 0, "suffix": "X", "scale": "BuPu", "format": "{:,.0f}", + } + write_section("methylqc_read_metrics", { + "id": "methylqc_read_metrics", + "section_name": "CpG read metrics", + "description": "Per-sample CpG coverage and methylation summary from the MethylDackel bedGraphs (primary chromosomes only).", + "plot_type": "table", + "pconfig": {"id": "methylqc_read_metrics_table", "title": "methyl_qc: CpG read metrics", "namespace": "methyl_qc"}, + "headers": { + "CpGs covered": {"format": "{:,.0f}", "scale": "Greens"}, + "median read #": {"title": "Median depth", "format": "{:,.1f}", "scale": "BuPu"}, + "mean read #": {"title": "Mean depth", "format": "{:,.1f}", "scale": "BuPu"}, + "% CpGs with < 10 reads": {"min": 0, "max": 100, "suffix": "%", "scale": "OrRd"}, + "mean CpG methylation (%)": {"min": 0, "max": 100, "suffix": "%", "scale": "RdYlBu-rev"}, + "% CpGs partially methylated": {"min": 0, "max": 100, "suffix": "%", "scale": "Purples"}, + }, + "data": table_data(df), + }) + + if args.repeat_annotation: + df = pd.read_csv(args.repeat_annotation, index_col=0) + add_general_stat(general_stats, df.iloc[:, 0], "repeat_cpg_pct") + general_headers["repeat_cpg_pct"] = { + "title": "% CpG in repeats", + "description": f"Percent of covered CpGs overlapping {df.columns[0].replace('Repeats from ', '')}", + "min": 0, "max": 100, "suffix": "%", "scale": "Blues", "format": "{:,.1f}", + } + + if general_stats: + write_section("methylqc_general_stats", { + "id": "methylqc_general_stats", + "plot_type": "generalstats", + "pconfig": [{key: header} for key, header in general_headers.items()], + "data": general_stats, + }) + + if args.meth_distribution: + df = pd.read_csv(args.meth_distribution) + data = {} + for sample, d in df.groupby("replicate", sort=False): + total = d["count"].sum() + data[str(sample)] = [[int(x), round(100 * c / total, 4)] for x, c in zip(d["percent_methylation"], d["count"])] + write_section("methylqc_meth_distribution", { + "id": "methylqc_meth_distribution", + "section_name": "CpG methylation distribution", + "description": "Distribution of per-CpG methylation levels. Healthy cfDNA typically shows a large peak near 100% and a smaller one near 0%.", + "plot_type": "linegraph", + "pconfig": { + "id": "methylqc_meth_distribution_plot", + "title": "methyl_qc: CpG methylation distribution", + "xlab": "CpG methylation (%)", "ylab": "% of covered CpGs", + "xmin": 0, "xmax": 100, "ymin": 0, + }, + "data": data, + }) + + if args.depth_distribution: + df = pd.read_csv(args.depth_distribution) + data = {} + for sample, d in df.groupby("replicate", sort=False): + data[str(sample)] = [[int(x), int(c)] for x, c in zip(d["read count"], d["number of read count occurrences"])] + write_section("methylqc_depth_distribution", { + "id": "methylqc_depth_distribution", + "section_name": "CpG read depth distribution", + "description": "Number of CpGs at each read depth (methylated + unmethylated reads).", + "plot_type": "linegraph", + "pconfig": { + "id": "methylqc_depth_distribution_plot", + "title": "methyl_qc: CpG read depth", + "xlab": "Read depth", "ylab": "Number of CpGs", + "xlog": True, "ymin": 0, + }, + "data": data, + }) + + if args.mbias: + df = pd.read_csv(args.mbias) + df["pct"] = 100 * df["nMethylated"] / (df["nMethylated"] + df["nUnmethylated"]).replace(0, np.nan) + datasets, labels = [], [] + for strand in ["OT", "OB"]: + for read in sorted(df["Read"].unique()): + d = df[(df["Strand"] == strand) & (df["Read"] == read)].sort_values("Position") + if d.empty: + continue + datasets.append({ + str(sample): [[int(p), round(float(v), 3)] for p, v in zip(g["Position"], g["pct"]) if pd.notna(v)] + for sample, g in d.groupby("replicate", sort=False) + }) + labels.append({"name": f"{strand} read {read}", "ylab": "% CpG methylated"}) + write_section("methylqc_mbias", { + "id": "methylqc_mbias", + "section_name": "M-bias", + "description": "CpG methylation by position in the read (MethylDackel mbias). Lines should be flat; slopes at the read ends suggest trimming.", + "plot_type": "linegraph", + "pconfig": { + "id": "methylqc_mbias_plot", + "title": "methyl_qc: M-bias", + "xlab": "Position in read (bp)", "ylab": "% CpG methylated", + "ymin": 0, "ymax": 100, + "data_labels": labels, + }, + "data": datasets, + }) + + if args.chr_percent: + df = pd.read_csv(args.chr_percent, index_col=0) + pivot = df.pivot_table(index="Sample", columns="Chromosome", values="Percent CpGs Methylated", sort=False) + cpgs = df.pivot_table(index="Sample", columns="Chromosome", values="CpG Count", aggfunc="sum", sort=False) + # Drop chromosomes with no CpGs in any sample (e.g. absent spike-ins) + pivot = pivot.loc[:, cpgs.sum(axis=0) > 0] + write_section("methylqc_chr_methylation", { + "id": "methylqc_chr_methylation", + "section_name": "Methylation by chromosome", + "description": "Percent of covered CpGs with any methylation, per chromosome and spike-in control.", + "plot_type": "heatmap", + "pconfig": { + "id": "methylqc_chr_methylation_heatmap", + "title": "methyl_qc: % CpGs methylated by chromosome", + "xlab": "Chromosome", "ylab": "Sample", + "min": 0, "max": 100, "square": False, + }, + "xcats": [str(c) for c in pivot.columns], + "ycats": [str(s) for s in pivot.index], + "data": [[round(float(v), 2) if pd.notna(v) else None for v in row] for row in pivot.to_numpy()], + }) + + if args.twist_annotation: + df = pd.read_csv(args.twist_annotation, index_col=0).T + write_section("methylqc_twist_annotation", { + "id": "methylqc_twist_annotation", + "section_name": "Twist target annotation", + "description": "Percent of covered CpGs overlapping each Twist methylome target annotation.", + "plot_type": "table", + "pconfig": {"id": "methylqc_twist_annotation_table", "title": "methyl_qc: Twist annotation", "namespace": "methyl_qc"}, + "headers": {str(c): {"min": 0, "max": 100, "suffix": "%", "scale": "Blues", "format": "{:,.1f}"} for c in df.columns}, + "data": table_data(df), + }) + + if args.tiled_beta: + df = pd.read_csv(args.tiled_beta, index_col=0) + bins = np.linspace(0, 1, 21) + centres = (bins[:-1] + bins[1:]) / 2 + data = {} + for sample in df.columns: + values = df[sample].dropna().to_numpy() + if values.size == 0: + continue + counts, _ = np.histogram(values, bins=bins) + data[str(sample)] = [[round(float(x), 3), round(100 * c / values.size, 3)] for x, c in zip(centres, counts)] + write_section("methylqc_tiled_beta", { + "id": "methylqc_tiled_beta", + "section_name": "Tiled beta values", + "description": "Distribution of methylation beta values across 1 kb tiles.", + "plot_type": "linegraph", + "pconfig": { + "id": "methylqc_tiled_beta_plot", + "title": "methyl_qc: tile beta distribution", + "xlab": "Beta value", "ylab": "% of tiles", + "xmin": 0, "xmax": 1, "ymin": 0, + }, + "data": data, + }) + + +def add_general_stat(general_stats: dict, series: pd.Series, key: str) -> None: + for sample, value in series.items(): + if pd.notna(value): + general_stats.setdefault(str(sample), {})[key] = float(value) + + +def table_data(df: pd.DataFrame) -> dict: + return { + str(sample): {str(k): (float(v) if pd.notna(v) else None) for k, v in row.items()} + for sample, row in df.iterrows() + } + + +# Line graph data is written as [x, y] pairs: MultiQC sorts dict keys as strings (1, 10, 100, 2, ...) +def write_section(name: str, content: dict) -> None: + with open(f"{name}_mqc.json", "w") as fh: + json.dump({**PARENT, **content}, fh, indent=2) + + +if __name__ == "__main__": + main() diff --git a/bin/repeat_annotation.R b/bin/repeat_annotation.R new file mode 100755 index 000000000..e8842fad0 --- /dev/null +++ b/bin/repeat_annotation.R @@ -0,0 +1,55 @@ +#!/usr/bin/env Rscript + +invisible( lapply(c( + "optparse", + "methylKit", + "genomation", + "ChIPpeakAnno", + "TxDb.Hsapiens.UCSC.hg38.knownGene", + "rtracklayer" +), library, character.only=T)) + + + + + + +option_list = list( + make_option(c("-i", "--methobj"), type="character", default=NULL, help="Methobj RDS file", metavar="character"), + make_option(c("-b", "--repeat_annot"), type="character", default=NULL, help="Repeat annotation file", metavar="character"), + make_option(c("-o", "--methobj_repeat_out"), type="character", default=NULL, help="Methobj RDS file with TWIST methylome annotation", metavar="character") +) +opt_parser = OptionParser(option_list=option_list) +opt = parse_args(opt_parser) + +# Validate and read input +if (is.null(opt$methobj)){ + print_help(opt_parser) + stop("Input methobjs files needs to be provided!") +} else { + methobj = readRDS(opt$methobj) +} + +if (is.null(opt$repeat_annot)){ + print_help(opt_parser) + stop("Repeat annotation file needs to be provided!") +} else { + repeat_annot = import.bb(opt$repeat_annot) +} + +if (is.null(opt$methobj_repeat_out)){ + print_help(opt_parser) + stop("Output name for Methobj RDS file with TWIST methylome annotation needs to be provided!") +} else { + methobj_repeat_out = opt$methobj_repeat_out +} + + +feat_annot_reps = c() +for (sample in methobj){ + methobj_gr = as(sample,'GRanges') + feat_annot = annotateWithFeature(methobj_gr, repeat_annot) + feat_annot_reps = c(feat_annot_reps, feat_annot) +} +saveRDS(feat_annot_reps, file=methobj_repeat_out) + diff --git a/bin/tile_methyl_counts.R b/bin/tile_methyl_counts.R new file mode 100755 index 000000000..09ca769b5 --- /dev/null +++ b/bin/tile_methyl_counts.R @@ -0,0 +1,62 @@ +#!/usr/bin/env Rscript + +invisible( lapply(c( + "optparse", + "methylKit" +), library, character.only=T)) + + +option_list = list( + make_option(c("-m", "--methobj"), type="character", default=NULL, help="Methylkit object RDS file", metavar="character"), + make_option(c("-w", "--window_size"), type="integer", default=1000, help="Window size for tiling methylkit counts", metavar="character"), + make_option(c("-s", "--step_size"), type="integer", default=1000, help="Step size for tiling methylkit counts", metavar="character"), + make_option(c("-c", "--cov_bases"), type="integer", default=0, help="Cov_bases for tiling methylkit counts", metavar="character"), + make_option(c("-o", "--output"), type="character", default=NULL, help="Output tiled Methylkit object RDS file", metavar="character") +) +opt_parser = OptionParser(option_list=option_list) +opt = parse_args(opt_parser) + +# Validate and read input +if (is.null(opt$methobj)){ + print_help(opt_parser) + stop("Input methylkit object needs to be provided!") +} else { + methobj = readRDS(opt$methobj) +} +if (is.null(opt$output)){ + print_help(opt_parser) + stop("Input methylkit object needs to be provided!") +} else { + outputRDS = opt$output +} + +if (is.null(opt$window_size)){ + print_help(opt_parser) + stop("Window size needs to be provided!") +} else { + window_size = opt$window_size +} + +if (is.null(opt$step_size)){ + print_help(opt_parser) + stop("Window size needs to be provided!") +} else { + step_size = opt$step_size +} + +if (is.null(opt$cov_bases)){ + print_help(opt_parser) + stop("Window size needs to be provided!") +} else { + cov_bases = opt$cov_bases +} + + +methobj_tiles <- tileMethylCounts( + methobj, + win_size = window_size, + step_size = step_size, + cov_bases = cov_bases +) + +saveRDS(methobj_tiles,outputRDS) \ No newline at end of file diff --git a/bin/twist_annotation.R b/bin/twist_annotation.R new file mode 100755 index 000000000..261df568a --- /dev/null +++ b/bin/twist_annotation.R @@ -0,0 +1,90 @@ +#!/usr/bin/env Rscript + +invisible( lapply(c( + "optparse", + "methylKit", + "genomation", + "ChIPpeakAnno", + "TxDb.Hsapiens.UCSC.hg38.knownGene", + "rtracklayer" +), library, character.only=T)) + + + + + + +option_list = list( + make_option(c("-i", "--methobj"), type="character", default=NULL, help="Methobj RDS file", metavar="character"), + make_option(c("-b", "--twist_bed_file"), type="character", default=NULL, help="TWIST methylome bed file", metavar="character"), + make_option(c("-c", "--cpg_obj_twist_out"), type="character", default=NULL, help="RDS file with TWIST methylome CpG annotation", metavar="character"), + make_option(c("-o", "--methobj_twist_out"), type="character", default=NULL, help="Methobj RDS file with TWIST methylome annotation", metavar="character") +) +opt_parser = OptionParser(option_list=option_list) +opt = parse_args(opt_parser) + +# Validate and read input +if (is.null(opt$methobj)){ + print_help(opt_parser) + stop("Input methobjs files needs to be provided!") +} else { + methobj = readRDS(opt$methobj) +} + +if (is.null(opt$twist_bed_file)){ + print_help(opt_parser) + stop("TWIST bed file needs to be provided!") +} else { + twist_bed_file = opt$twist_bed_file +} + +if (is.null(opt$cpg_obj_twist_out)){ + print_help(opt_parser) + stop("Output name for twist RDS file with TWIST methylome annotation needs to be provided!") +} else { + cpg_obj_twist_out = opt$cpg_obj_twist_out +} + +if (is.null(opt$methobj_twist_out)){ + print_help(opt_parser) + stop("Output name for Methobj RDS file with TWIST methylome annotation needs to be provided!") +} else { + methobj_twist_out = opt$methobj_twist_out +} + +twist_bed_rds <- function(bed_file_twist){ + methylome_annot_bed = readGeneric(bed_file_twist, meta.cols=4) + + # We capture all "generic" annotations in the file with regex filtering. + all_annot = unique(unlist(lapply(elementMetadata(methylome_annot_bed)[[1]],strsplit,','))) + annot_filt = all_annot[!grepl('^cg', all_annot)] + annot_filt = annot_filt[!grepl('^ch', annot_filt)] + annot_filt = annot_filt[!grepl('^rs[0-9]+', annot_filt)] + + # Create a GRangesList object whose elements are the granges annotated according to each element in annot_filt. + cpg_obj_twist = c() + for (annotation_case in annot_filt){ + this_subset = methylome_annot_bed[grepl(annotation_case, elementMetadata(methylome_annot_bed)[[1]])] + cpg_obj_twist = c(cpg_obj_twist, this_subset) + } + cpg_obj_twist = GRangesList(cpg_obj_twist) + names(cpg_obj_twist@partitioning) <- annot_filt + return(cpg_obj_twist) +} + + +# Create TWIST methylome object +cpg_obj_twist <- twist_bed_rds(twist_bed_file) +saveRDS(cpg_obj_twist, file=cpg_obj_twist_out) + +feat_annot_reps = c() +for (sample in methobj){ + methobj_gr = as(sample,'GRanges') + feat_annot = c() + for (annotation_case_index in 1:length(cpg_obj_twist)){ + feat_annot = c(feat_annot, annotateWithFeature(methobj_gr, cpg_obj_twist[annotation_case_index][[1]])) + } + # Note: Is this overwriting feat_annot_reps each time in the loop?? + feat_annot_reps = c(feat_annot_reps, feat_annot) +} +saveRDS(feat_annot_reps, file=methobj_twist_out) diff --git a/conf/evolvdx/aws.config b/conf/evolvdx/aws.config new file mode 100644 index 000000000..a162c86cb --- /dev/null +++ b/conf/evolvdx/aws.config @@ -0,0 +1,32 @@ +plugins { + id 'nf-amazon@2.15.0-patch2' +} + +docker.enabled = true + +aws { + batch { + cliPath = '/home/ec2-user/miniconda/bin/aws' + } + region = 'us-east-1' +} + +process { + executor = 'awsbatch' + queue = 'demand-nextflowbatch-ami-queue' +} + + +timeline { + overwrite = true +} +report { + overwrite = true +} +trace { + overwrite = true +} +dag { + overwrite = true +} + diff --git a/conf/evolvdx/aws.params.json b/conf/evolvdx/aws.params.json new file mode 100644 index 000000000..25f70c65f --- /dev/null +++ b/conf/evolvdx/aws.params.json @@ -0,0 +1,11 @@ +{ + "input": "s3://your-bucket/samplesheets/experiment.csv", + "outdir": "s3://your-bucket/results/experiment/", + "genome": "GRCh38", + "aligner": "bwameth", + "run_targeted_sequencing": true, + "target_regions_file": "s3://your-bucket/references/panel.bed", + "save_reference": true, + "save_align_intermeds": false, + "email": "your.email@evolvdx.com" +} diff --git a/conf/evolvdx/aws_500gb.config b/conf/evolvdx/aws_500gb.config new file mode 100644 index 000000000..f0beddb60 --- /dev/null +++ b/conf/evolvdx/aws_500gb.config @@ -0,0 +1,32 @@ +plugins { + id 'nf-amazon@2.15.0-patch2' +} + +docker.enabled = true + +aws { + batch { + cliPath = '/home/ec2-user/miniconda/bin/aws' + } + region = 'us-east-1' +} + +process { + executor = 'awsbatch' + queue = 'demand-nextflowbatch-ami-500Gb_multisubnets_v2' +} + + +timeline { + overwrite = true +} +report { + overwrite = true +} +trace { + overwrite = true +} +dag { + overwrite = true +} + diff --git a/conf/evolvdx/custom_genomes.config b/conf/evolvdx/custom_genomes.config new file mode 100755 index 000000000..d9c16c531 --- /dev/null +++ b/conf/evolvdx/custom_genomes.config @@ -0,0 +1,26 @@ +/* +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Nextflow config file for Custom Genomes paths +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Defines reference genomes using AWS S3 paths. + Can be used by any config that customises the base path using: + $params.exogenomes_base / --exogenomes_base +---------------------------------------------------------------------------------------- +*/ + +params { + // Custom Methylation specific Genomes reference file paths + genomes { + 'grch38_core_bs_controls' { + fasta = "${params.exogenomes_base}/grch38_core+bs_controls/genome.fa" + fasta_index = "${params.exogenomes_base}/grch38_core+bs_controls/bwameth/genome.fa.fai" + bwameth = "${params.exogenomes_base}/grch38_core+bs_controls/bwameth/" + bismark = "${params.exogenomes_base}/grch38_core+bs_controls/BismarkIndex/" + gtf = "${params.exogenomes_base}/Homo_sapiens.GRCh38.106.gtf" + twist_methylome_bed = "${params.exogenomes_base}/covered_targets_Twist_Methylome_hg38_annotated_collapsed.bed" + repeat_annot = "${params.exogenomes_base}/chm13v2.0_rmsk.bb" + hg38_chrom_sizes = "${params.exogenomes_base}/hg38.chrom.sizes" + } + } +} + diff --git a/conf/evolvdx/evolvdx.config b/conf/evolvdx/evolvdx.config new file mode 100644 index 000000000..498520495 --- /dev/null +++ b/conf/evolvdx/evolvdx.config @@ -0,0 +1,32 @@ +/* +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + EvolvDx profile: production defaults layered on top of nf-core/methylseq +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Use with `-profile evolvdx,`. Keeping these defaults in a profile + (instead of editing nextflow.config) keeps upstream merges clean. +---------------------------------------------------------------------------------------- +*/ + +params { + config_profile_name = 'EvolvDx profile' + config_profile_description = 'EvolvDx bwameth/methylKit defaults with EvolvDx QC steps enabled' + + // References + genome = 'grch38_core_bs_controls' + igenomes_ignore = true + cpg_island_bed = 's3://exosomedx/pipeline_resources/bedfiles/cpgIslandExt.hg38.bed' + target_regions_file = 's3://exosomedx/pipeline_resources/bedfiles/covered_targets_Twist_Methylome_hg38_annotated_collapsed.bed' + + // Alignment (MethylDackel bedGraph is the default extraction; methylKit files are + // produced by an extra extraction when --run_methylqc is set) + aligner = 'bwameth' + + // Trimming + clip_r1 = 20 + clip_r2 = 15 + + // EvolvDx QC steps + run_methylqc = true + run_cpg_coverage = true + run_methsnsv = true +} diff --git a/conf/evolvdx/exo_test.config b/conf/evolvdx/exo_test.config new file mode 100644 index 000000000..2eeaf258f --- /dev/null +++ b/conf/evolvdx/exo_test.config @@ -0,0 +1,30 @@ +/* +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + EvolvDx test profile: small NSQCAM529 subsample, EvolvDx QC steps enabled +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Use with `-profile evolvdx_test,`. Pulls its inputs from S3. +---------------------------------------------------------------------------------------- +*/ + +// Start from the EvolvDx production defaults +includeConfig 'evolvdx.config' + +process { + resourceLimits = [ + cpus: 2, + memory: '6.GB', + time: '6.h' + ] +} + +params { + config_profile_name = 'EvolvDx test profile' + config_profile_description = 'Minimal EvolvDx test dataset to check pipeline function' + + input = 's3://exosomedx/pipeline_resources/methylseq/test/test_NSQCAM529_subsample_20k.csv' + + // Targeted sequencing metrics against the Twist methylome panel + run_targeted_sequencing = true + collecthsmetrics = true + run_picardhs_report = true +} diff --git a/conf/modules/evolvdx.config b/conf/modules/evolvdx.config new file mode 100644 index 000000000..bfcd5ff0d --- /dev/null +++ b/conf/modules/evolvdx.config @@ -0,0 +1,57 @@ +process { + + withName: 'METHYLDACKEL_EXTRACT_METHYLKIT' { + ext.args = [ + params.all_contexts ? ' --CHG --CHH' : '', + params.merge_context ? ' --mergeContext' : '', + params.ignore_flags ? " --ignoreFlags" : '', + " --methylKit", + params.min_depth > 0 ? " --minDepth ${params.min_depth}" : '' + ].join(" ").trim() + publishDir = [ + path: { "${params.outdir}/methyldackel" }, + mode: params.publish_dir_mode, + pattern: "*.methylKit" + ] + } + + withName: 'COMBINED_METHOBJ|COMBINED_HDF5|CONTROL_STATS|BASIC_READ_STATISTICS|TILE_METHYL_COUNTS|MERGE_TILED_STATS|TWIST_ANNOTATION|REPEAT_ANNOTATION|MERGE_ANNOTATION_STATS|METH_HISTOGRAM_BY_CHROMOSOME' { + publishDir = [ + path: { "${params.outdir}/methqc" }, + mode: params.publish_dir_mode, + saveAs: { filename -> filename.equals('versions.yml') ? null : filename } + ] + } + + withName: 'METHYLQC_MULTIQC' { + publishDir = [ + path: { "${params.outdir}/methqc/multiqc" }, + mode: params.publish_dir_mode, + pattern: "*_mqc.json" + ] + } + + withName: 'METHSNSV' { + publishDir = [ + path: { "${params.outdir}/methqc/sn_sv" }, + mode: params.publish_dir_mode, + pattern: "*.txt" + ] + } + + withName: 'BEDTOOLS_INTERSECT_CPG|BEDTOOLS_PERBASE_GENOMECOV' { + publishDir = [ + path: { "${params.outdir}/cpg_coverage" }, + mode: params.publish_dir_mode, + saveAs: { filename -> filename.equals('versions.yml') ? null : filename } + ] + } + + withName: 'CUSTOM_MULTIQC_PICARDHS' { + publishDir = [ + path: { "${params.outdir}/multiqc/picard_hs" }, + mode: params.publish_dir_mode, + saveAs: { filename -> filename.equals('versions.yml') ? null : filename } + ] + } +} diff --git a/env/README.md b/env/README.md new file mode 100644 index 000000000..baa1bacbf --- /dev/null +++ b/env/README.md @@ -0,0 +1,105 @@ +# Building and Pushing EvolvDX Exome Container Images + +This guide explains how to build, tag, and push the custom EvolvDX exome container images to Amazon ECR for use with the pipeline on AWS Batch. + +## Prerequisites + +- Docker installed and running +- AWS CLI configured with appropriate credentials +- Access to the EvolvDX ECR registry (account ID: 062689759574, region: us-east-1) + +## Included Container Images + +The pipeline uses the following custom container images (stored in `env/exo/*/`): +- `exosomedx/methylkit`: R environment for methylKit QC reporting +- `exosomedx/basic_read_statistics`: Custom read statistics tools +- `exosomedx/methsnsv`: SNV calling from methylation data +- `exosomedx/custom_multiqc_picardhs`: MultiQC with Picard HS metrics support + +## Build Workflow + +### 1. Build the Docker image +> [!IMPORTANT] +> AWS Batch uses x86_64 (amd64) architecture. If you are building on a Mac with Apple Silicon (arm64), you must specify the `--platform linux/amd64` flag to ensure compatibility. + +```bash +# Navigate to the directory containing the Dockerfile +cd env/exo// + +# Build the image (replace and with appropriate values) +docker build --no-cache --platform linux/amd64 -t exosomedx/: . +``` + +### 2. Verify the image was built +```bash +docker image ls | grep exosomedx/ +``` + +### 3. Tag the image for ECR +```bash +# Get the IMAGE ID from the previous step, or use the tag +docker tag exosomedx/: 062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/: +``` + +### 4. Authenticate with ECR +```bash +aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 062689759574.dkr.ecr.us-east-1.amazonaws.com +``` + +### 5. Create the ECR repository (if it doesn't exist) +```bash +aws ecr create-repository --repository-name exosomedx/ --region us-east-1 +``` + +### 6. Push the image to ECR +```bash +docker push 062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/: +``` + +### 7. Update the pipeline config +After pushing the new image version, update the container path in the corresponding module file (`modules/local/exo/.nf`) to reference the new image tag: +```nextflow +container "062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/:" +``` + +## Example: Building the methylKit Image + +```bash +# Build +cd env/exo/methylkit/ +docker build --no-cache --platform linux/amd64 -t exosomedx/methylkit:1.36.0 . + +# Tag +docker tag exosomedx/methylkit:1.36.0 062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/methylkit:1.36.0 + +# Push +aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 062689759574.dkr.ecr.us-east-1.amazonaws.com +docker push 062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/methylkit:1.36.0 + +# Update module config +sed -i '' 's|exosomedx/methylkit:.*|exosomedx/methylkit:1.36.0|' modules/local/exo/methylkit.nf +``` + +## Versioning Convention + +Use semantic versioning for container images: +- Major version (X.0.0): Breaking changes to the container environment +- Minor version (x.Y.0): New tools or features added, backward compatible +- Patch version (x.y.Z): Bug fixes, tool updates, backward compatible + +## Troubleshooting + +### "No space left on device" during build +Clean up unused Docker images and containers: +```bash +docker system prune -a +``` + +### "Denied: Your authorization token has expired" when pushing +Re-authenticate with ECR: +```bash +aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 062689759574.dkr.ecr.us-east-1.amazonaws.com +``` + +### Image works locally but fails on AWS Batch +Ensure you built the image with `--platform linux/amd64` if you are on an ARM-based Mac. diff --git a/env/dev/Dockerfile b/env/dev/Dockerfile new file mode 100644 index 000000000..6ce714085 --- /dev/null +++ b/env/dev/Dockerfile @@ -0,0 +1,11 @@ +FROM mambaorg/micromamba:0.25.1 +COPY --chown=$MAMBA_USER:$MAMBA_USER conda_environment.yml /tmp/conda_environment.yml +RUN micromamba install -y -n base -f /tmp/conda_environment.yml && \ + micromamba clean --all --yes +USER root +RUN apt-get update && apt-get -y install sudo +RUN sudo apt-get update -qq && \ + sudo apt-get install -y zip procps ghostscript + +WORKDIR /work +# COPY --chown=$MAMBA_USER:$MAMBA_USER .aws/ /home/mambauser/.aws/ \ No newline at end of file diff --git a/env/dev/build_conda_environment.sh b/env/dev/build_conda_environment.sh new file mode 100644 index 000000000..a23a6dfcb --- /dev/null +++ b/env/dev/build_conda_environment.sh @@ -0,0 +1,2 @@ +mamba create -n methylseq +mamba env update -n methylseq --file env/dev/conda_environment.yml \ No newline at end of file diff --git a/env/dev/conda_environment.yml b/env/dev/conda_environment.yml new file mode 100644 index 000000000..5999c365e --- /dev/null +++ b/env/dev/conda_environment.yml @@ -0,0 +1,30 @@ +name: methylseq + +channels: + - conda-forge + - bioconda + +dependencies: + - bioconda::bioconductor-chippeakanno=3.38.0 + - bioconda::bioconductor-genomation=1.34.0 + - bioconda::bioconductor-methylkit=1.36.0 + - bioconda::bioconductor-rtracklayer=1.64.0 + - bioconda::bioconductor-txdb.hsapiens.ucsc.hg38.knowngene=3.18.0 + - conda-forge::awscli=2.15.54 + - conda-forge::boto3=1.34.149 + - conda-forge::fsspec=2024.6.0 + - conda-forge::h5py=3.11.0 + - conda-forge::nb_conda_kernels=2.5.1 + - conda-forge::notebook=7.2.1 + - conda-forge::s3fs=2024.6.0 + - conda-forge::natsort=8.4.0 + - conda-forge::numpy=1.26.4 + - conda-forge::pandas=2.2.2 + - conda-forge::pandoc=3.2.1 + - conda-forge::rpy2=3.5.15 + - conda-forge::r-base=4.4.1 + - conda-forge::r-here=1.0.1 + - conda-forge::r-irkernel=1.3.2 + - conda-forge::r-svglite=2.1.3 + - conda-forge::r-tidyverse=2.0.0 + - conda-forge::seaborn=0.13.2 \ No newline at end of file diff --git a/env/exo/basic_read_statistics/Dockerfile b/env/exo/basic_read_statistics/Dockerfile new file mode 100644 index 000000000..934a6c8e6 --- /dev/null +++ b/env/exo/basic_read_statistics/Dockerfile @@ -0,0 +1,11 @@ +FROM mambaorg/micromamba:1.5.10 +COPY --chown=$MAMBA_USER:$MAMBA_USER conda_environment.yml /tmp/conda_environment.yml +RUN micromamba install -y -n base -f /tmp/conda_environment.yml && \ + micromamba clean --all --yes +USER root +RUN apt-get update && apt-get -y install sudo +RUN sudo apt-get update -qq && \ + sudo apt-get install -y zip procps ghostscript + +WORKDIR /work +# COPY --chown=$MAMBA_USER:$MAMBA_USER .aws/ /home/mambauser/.aws/ \ No newline at end of file diff --git a/env/exo/basic_read_statistics/conda_environment.yml b/env/exo/basic_read_statistics/conda_environment.yml new file mode 100644 index 000000000..b70ebaa4b --- /dev/null +++ b/env/exo/basic_read_statistics/conda_environment.yml @@ -0,0 +1,14 @@ +name: basic_read_statistics + +channels: + - conda-forge + - defaults + - anaconda + - bioconda + - r + +dependencies: + - conda-forge::h5py=3.8.0 + - conda-forge::numpy=1.23.3 + - conda-forge::pandas=1.5.0 + - conda-forge::seaborn=0.12.2 \ No newline at end of file diff --git a/env/exo/methylkit/Dockerfile b/env/exo/methylkit/Dockerfile new file mode 100644 index 000000000..934a6c8e6 --- /dev/null +++ b/env/exo/methylkit/Dockerfile @@ -0,0 +1,11 @@ +FROM mambaorg/micromamba:1.5.10 +COPY --chown=$MAMBA_USER:$MAMBA_USER conda_environment.yml /tmp/conda_environment.yml +RUN micromamba install -y -n base -f /tmp/conda_environment.yml && \ + micromamba clean --all --yes +USER root +RUN apt-get update && apt-get -y install sudo +RUN sudo apt-get update -qq && \ + sudo apt-get install -y zip procps ghostscript + +WORKDIR /work +# COPY --chown=$MAMBA_USER:$MAMBA_USER .aws/ /home/mambauser/.aws/ \ No newline at end of file diff --git a/env/exo/methylkit/conda_environment.yml b/env/exo/methylkit/conda_environment.yml new file mode 100644 index 000000000..c3d3db82f --- /dev/null +++ b/env/exo/methylkit/conda_environment.yml @@ -0,0 +1,20 @@ +name: methylkit + +channels: + - conda-forge + - bioconda + +dependencies: + - bioconda::bioconductor-chippeakanno=3.44.0 + - bioconda::bioconductor-genomation=1.42.0 + - bioconda::bioconductor-methylkit=1.36.0 + - bioconda::bioconductor-rtracklayer=1.70.1 + - bioconda::bioconductor-txdb.hsapiens.ucsc.hg38.knowngene=3.22.0 + - conda-forge::h5py=3.16.0 + - conda-forge::natsort=8.4.0 + - conda-forge::numpy=2.5.3 + - conda-forge::pandas=3.0.6 + - conda-forge::python=3.15.0 + - conda-forge::r-base=4.5.3 + - conda-forge::r-optparse=1.8.2 + - conda-forge::rpy2=3.6.8 diff --git a/modules/local/exo/basic_read_statistics.nf b/modules/local/exo/basic_read_statistics.nf new file mode 100644 index 000000000..879958361 --- /dev/null +++ b/modules/local/exo/basic_read_statistics.nf @@ -0,0 +1,41 @@ +// Per-sample CpG depth and methylation statistics and merged M-bias tables +process BASIC_READ_STATISTICS { + label 'process_medium' + + container '062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/basic_read_statistics:0.0.1' + + input: + path bedgraph_hdf5 + path mbias_files + val mbias_meta + + output: + path "merged_mbias.csv" , emit: merged_mbias + path "mbias.id_file.csv" , emit: mbias_id_file + path "tab_id_value_counts_df.csv", emit: meth_distribution + path "rnf_counts_id_df.csv" , emit: depth_distribution + path "metrics_table.csv" , emit: metrics_table + path "*.png" , emit: plots + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + """ + export MPLCONFIGDIR=\$PWD/.matplotlib + + basic_read_statistics.py \\ + --bedGraph_hdf5 ${bedgraph_hdf5} \\ + --mbias_files '${mbias_files}' \\ + --meta '${mbias_meta}' \\ + --id_file mbias.id_file.csv \\ + --merged_mbias merged_mbias.csv + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + basic_read_statistics: \$(basic_read_statistics.py --version 2>/dev/null) + pandas: \$(python -c 'import pandas; print(pandas.__version__)' 2>/dev/null) + END_VERSIONS + """ +} diff --git a/modules/local/exo/bedtools/intersect_cpg/main.nf b/modules/local/exo/bedtools/intersect_cpg/main.nf new file mode 100644 index 000000000..352b75d44 --- /dev/null +++ b/modules/local/exo/bedtools/intersect_cpg/main.nf @@ -0,0 +1,38 @@ +process BEDTOOLS_INTERSECT_CPG { + tag "$meta.id" + label 'process_medium' + + container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ? + 'https://depot.galaxyproject.org/singularity/bedtools:2.30.0--hc088bd4_0' : + 'quay.io/biocontainers/bedtools:2.30.0--hc088bd4_0' }" + + input: + tuple val(meta), path(intervals) + val extension + path cpg_bed + + output: + tuple val(meta), path("*.${extension}"), emit: intersect + path "versions.yml" , emit: version + + when: + task.ext.when == null || task.ext.when + + script: + def args = task.ext.args ?: '' + def prefix = task.ext.prefix ?: "${meta.id}" + """ + bedtools \\ + intersect \\ + -a $intervals \\ + -b $cpg_bed \\ + $args \\ + -wa \\ + > ${prefix}.${extension} + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + bedtools: \$(bedtools --version | sed -e "s/bedtools v//g") + END_VERSIONS + """ +} diff --git a/modules/local/exo/bedtools/intersect_cpg/meta.yml b/modules/local/exo/bedtools/intersect_cpg/meta.yml new file mode 100644 index 000000000..b1b73bd83 --- /dev/null +++ b/modules/local/exo/bedtools/intersect_cpg/meta.yml @@ -0,0 +1,47 @@ +name: bedtools_intersect +description: Allows one to screen for overlaps between two sets of genomic features. +keywords: + - bed + - intersect +tools: + - bedtools: + description: | + A set of tools for genomic analysis tasks, specifically enabling genome arithmetic (merge, count, complement) on various file types. + documentation: https://bedtools.readthedocs.io/en/latest/content/tools/intersect.html + licence: ["MIT"] +input: + - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - intervals1: + type: file + description: BAM + pattern: "*.{bam}" + - extension: + type: value + description: Extension of the output file. It is set by the user and corresponds to the file format which depends on arguments (e. g., ".bed", ".bam", ".txt", etc.). + - cpg_bed: + type: file + description: BED + pattern: "*.{bed}" +output: + - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - intersect: + type: file + description: File containing the description of overlaps found between the two features + pattern: "*.${extension}" + - versions: + type: file + description: File containing software versions + pattern: "versions.yml" +authors: + - "@Emiller88" + - "@sruthipsuresh" + - "@drpatelh" + - "@sidorov-si" diff --git a/modules/local/exo/bedtools/per_base_genomecov/main.nf b/modules/local/exo/bedtools/per_base_genomecov/main.nf new file mode 100644 index 000000000..5ec276053 --- /dev/null +++ b/modules/local/exo/bedtools/per_base_genomecov/main.nf @@ -0,0 +1,38 @@ +process BEDTOOLS_PERBASE_GENOMECOV { + tag "$meta.id" + label 'process_medium' + + container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ? + 'https://depot.galaxyproject.org/singularity/bedtools:2.30.0--hc088bd4_0' : + 'quay.io/biocontainers/bedtools:2.30.0--hc088bd4_0' }" + + input: + tuple val(meta), path(intervals) + val extension + + output: + tuple val(meta), path("*.${extension}"), emit: genomecov + path "versions.yml" , emit: version + + when: + task.ext.when == null || task.ext.when + + script: + def args = task.ext.args ?: '' + def args_list = args.tokenize() + + def prefix = task.ext.prefix ?: "${meta.id}" + """ + bedtools \\ + genomecov \\ + -dz \\ + -ibam $intervals \\ + $args \\ + > ${prefix}.${extension} + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + bedtools: \$(bedtools --version | sed -e "s/bedtools v//g") + END_VERSIONS + """ +} diff --git a/modules/local/exo/bedtools/per_base_genomecov/meta.yml b/modules/local/exo/bedtools/per_base_genomecov/meta.yml new file mode 100644 index 000000000..8d5d30143 --- /dev/null +++ b/modules/local/exo/bedtools/per_base_genomecov/meta.yml @@ -0,0 +1,45 @@ +name: bedtools_genomecov +description: Computes histograms (default), per-base reports (-d) and BEDGRAPH (-bg) summaries of feature coverage (e.g., aligned sequences) for a given genome. +keywords: + - bed + - bam + - genomecov +tools: + - bedtools: + description: | + A set of tools for genomic analysis tasks, specifically enabling genome arithmetic (merge, count, complement) on various file types. + documentation: https://bedtools.readthedocs.io/en/latest/content/tools/genomecov.html + licence: ["MIT"] +input: + - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - intervals: + type: file + description: BAM + pattern: "*.{bam}" + - extension: + type: string + description: Extension of the output file (".txt") It is set arbitrarily by the user and corresponds to the file format which depends on arguments. +output: + - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - genomecov: + type: file + description: Computed genome coverage file + pattern: "*.${extension}" + - versions: + type: file + description: File containing software versions + pattern: "versions.yml" +authors: + - "@Emiller88" + - "@sruthipsuresh" + - "@drpatelh" + - "@sidorov-si" + - "@chris-cheshire" diff --git a/modules/local/exo/control_stats.nf b/modules/local/exo/control_stats.nf new file mode 100644 index 000000000..a427b1eed --- /dev/null +++ b/modules/local/exo/control_stats.nf @@ -0,0 +1,29 @@ +// Mean CpG methylation of the lambda and pUC19 spike-in controls per sample +process CONTROL_STATS { + label 'process_medium' + + container '062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/methylkit:1.20.0' + + input: + path methobj + + output: + path "control_stats.csv", emit: control_stats + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + """ + control_stats.py \\ + --methobj ${methobj} \\ + --stats_out control_stats.csv + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + r-base: \$(echo \$(R --version 2>&1) | sed 's/^.*R version //; s/ .*\$//') + bioconductor-methylkit: \$(Rscript -e "library(methylKit); cat(as.character(packageVersion('methylKit')))" 2>/dev/null) + END_VERSIONS + """ +} diff --git a/modules/local/exo/custom_multiqc/custom_multiqc_picardhs.nf b/modules/local/exo/custom_multiqc/custom_multiqc_picardhs.nf new file mode 100644 index 000000000..0a4771187 --- /dev/null +++ b/modules/local/exo/custom_multiqc/custom_multiqc_picardhs.nf @@ -0,0 +1,40 @@ +process CUSTOM_MULTIQC_PICARDHS { + label 'process_medium' + + container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ? + '062689759574.dkr.ecr.us-east-1.amazonaws.com/custom_multiqc_picardhs:collect_picardHs_0.0.1' : + '062689759574.dkr.ecr.us-east-1.amazonaws.com/custom_multiqc_picardhs:collect_picardHs_0.0.1'}" + + input: + path ("picard/*") + path sample_sheet + path picard_bargraph_header + path picard_linegraph_header + path picard_table_config + + output: + path "*_mqc.yml" , emit: yml + path "*_mqc.tsv" , emit: tsv + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + def args = task.ext.args ?: '' + + """ + collect_picardHs.py \\ + picard \\ + $sample_sheet \\ + $picard_bargraph_header \\ + $picard_table_config + + cat $picard_linegraph_header <(echo) 'uniformity.yml' > 'picard_uniformity_mqc.yml' + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + custom_multiqc_picardHs: \$(echo \$(collect_picardHs.py --version 2>&1) | sed 's/^.*collect_picardHs //; s/Using.*\$//' ) + END_VERSIONS + """ +} diff --git a/modules/local/exo/make_combined_hdf5.nf b/modules/local/exo/make_combined_hdf5.nf new file mode 100644 index 000000000..ad54894d1 --- /dev/null +++ b/modules/local/exo/make_combined_hdf5.nf @@ -0,0 +1,32 @@ +// Gather per-sample MethylDackel bedGraphs into one HDF5 file +process COMBINED_HDF5 { + label 'process_medium' + + container '062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/methylkit:1.20.0' + + input: + path bedgraph_files + val bedgraph_meta + + output: + path "bedGraph.id_file.csv", emit: id_file + path "merged.bedgraph.hdf5", emit: merged_bedgraph_hdf5 + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + """ + make_combined_hdf5.py \\ + --input_files '${bedgraph_files}' \\ + --meta '${bedgraph_meta}' \\ + --id_file bedGraph.id_file.csv \\ + --hdf5 merged.bedgraph.hdf5 + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + h5py: \$(python -c 'import h5py; print(h5py.__version__)' 2>/dev/null) + END_VERSIONS + """ +} diff --git a/modules/local/exo/make_combined_methobj.nf b/modules/local/exo/make_combined_methobj.nf new file mode 100644 index 000000000..d583f63a1 --- /dev/null +++ b/modules/local/exo/make_combined_methobj.nf @@ -0,0 +1,31 @@ +// Merge per-sample methylKit files into cohort methylRawList objects (min coverage 10 and 3) +process COMBINED_METHOBJ { + label 'process_medium' + + container '062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/methylkit:1.20.0' + + input: + path methylkit_files + val methylkit_meta + + output: + path "methobj.rds" , emit: methobj + path "methobj_lowcov.rds", emit: methobj_lowcov + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + """ + make_combined_methobj.R \\ + --input_files '${methylkit_files}' \\ + --meta '${methylkit_meta}' + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + r-base: \$(echo \$(R --version 2>&1) | sed 's/^.*R version //; s/ .*\$//') + bioconductor-methylkit: \$(Rscript -e "library(methylKit); cat(as.character(packageVersion('methylKit')))" 2>/dev/null) + END_VERSIONS + """ +} diff --git a/modules/local/exo/merge_annotation_stats.nf b/modules/local/exo/merge_annotation_stats.nf new file mode 100644 index 000000000..c80bca044 --- /dev/null +++ b/modules/local/exo/merge_annotation_stats.nf @@ -0,0 +1,37 @@ +// Percent of covered CpGs in each Twist category and in repeats, per sample +process MERGE_ANNOTATION_STATS { + label 'process_medium' + + container '062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/methylkit:1.20.0' + + input: + path methobj + path methobj_twist_annotation + path cpg_obj + path methobj_repeat_annotation + + output: + path "twist_annotation.csv" , emit: twist_annotation + path "repeat_annotation.csv", emit: repeat_annotation + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + """ + merge_annotation_stats.py \\ + --methobj ${methobj} \\ + --methobj_twist_annotation ${methobj_twist_annotation} \\ + --cpg_obj ${cpg_obj} \\ + --methobj_repeat_annotation ${methobj_repeat_annotation} \\ + --twist_annotation_csv twist_annotation.csv \\ + --repeat_annotation_csv repeat_annotation.csv + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + r-base: \$(echo \$(R --version 2>&1) | sed 's/^.*R version //; s/ .*\$//') + bioconductor-methylkit: \$(Rscript -e "library(methylKit); cat(as.character(packageVersion('methylKit')))" 2>/dev/null) + END_VERSIONS + """ +} diff --git a/modules/local/exo/merge_tiled_stats.nf b/modules/local/exo/merge_tiled_stats.nf new file mode 100644 index 000000000..4ee188583 --- /dev/null +++ b/modules/local/exo/merge_tiled_stats.nf @@ -0,0 +1,29 @@ +// Merge tiled beta values across samples +process MERGE_TILED_STATS { + label 'process_medium' + + container '062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/methylkit:1.20.0' + + input: + path tiled_methobj + + output: + path "merge_tiled_stats.csv", emit: merge_tiled_stats + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + """ + merge_tiled_stats.py \\ + --tiled_methobj ${tiled_methobj} \\ + --output merge_tiled_stats.csv + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + r-base: \$(echo \$(R --version 2>&1) | sed 's/^.*R version //; s/ .*\$//') + bioconductor-methylkit: \$(Rscript -e "library(methylKit); cat(as.character(packageVersion('methylKit')))" 2>/dev/null) + END_VERSIONS + """ +} diff --git a/modules/local/exo/meth_histogram_by_chromosome.nf b/modules/local/exo/meth_histogram_by_chromosome.nf new file mode 100644 index 000000000..cd4293a3b --- /dev/null +++ b/modules/local/exo/meth_histogram_by_chromosome.nf @@ -0,0 +1,37 @@ +// Per-chromosome CpG counts and percent methylated CpGs +process METH_HISTOGRAM_BY_CHROMOSOME { + label 'process_medium' + + container '062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/basic_read_statistics:0.0.1' + + input: + path bedgraph_files + val bedgraph_meta + path chrom_sizes + + output: + path "counts_df_long.csv", emit: counts_df_long + path "chr_percent_df.csv", emit: chr_percent_df + path "*.png" , emit: plots + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + """ + export MPLCONFIGDIR=\$PWD/.matplotlib + + meth_histogram_by_chromosome.py \\ + --input_files '${bedgraph_files}' \\ + --meta '${bedgraph_meta}' \\ + --hg38_chrom_sizes ${chrom_sizes} \\ + --counts_df_long counts_df_long.csv \\ + --chr_percent_df chr_percent_df.csv + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + pandas: \$(python -c 'import pandas; print(pandas.__version__)' 2>/dev/null) + END_VERSIONS + """ +} diff --git a/modules/local/exo/methsnsv.nf b/modules/local/exo/methsnsv.nf new file mode 100644 index 000000000..12f9cef17 --- /dev/null +++ b/modules/local/exo/methsnsv.nf @@ -0,0 +1,33 @@ +process METHSNSV { + tag "$meta.id" + // label 'process_low_memscale' + label 'process_medium' + + container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ? + '062689759574.dkr.ecr.us-east-1.amazonaws.com/methsnsv:meth_stats_0.0.1' : + '062689759574.dkr.ecr.us-east-1.amazonaws.com/methsnsv:meth_stats_0.0.1'}" + + input: + tuple val(meta), path(bedgraph) + + output: + tuple val(meta), path("*.txt"), emit: txt + path "versions.yml" , emit: version + + when: + task.ext.when == null || task.ext.when + + script: + def args = task.ext.args ?: '' + def prefix = task.ext.prefix ?: "${meta.id}" + """ + meth_stats.py \\ + $bedgraph \\ + ${meta.id}_snsv.txt + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + methsnsv: meth_stats_0.0.1 + END_VERSIONS + """ +} diff --git a/modules/local/exo/methylqc_multiqc.nf b/modules/local/exo/methylqc_multiqc.nf new file mode 100644 index 000000000..170fbca30 --- /dev/null +++ b/modules/local/exo/methylqc_multiqc.nf @@ -0,0 +1,44 @@ +// Convert methyl_qc outputs into MultiQC custom content +process METHYLQC_MULTIQC { + label 'process_medium' + + container '062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/basic_read_statistics:0.0.1' + + input: + path control_stats + path read_metrics + path meth_distribution + path depth_distribution + path mbias + path chr_percent + path twist_annotation + path repeat_annotation + path tiled_beta + + output: + path "*_mqc.json" , emit: mqc + path "versions.yml", emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + """ + methylqc_multiqc.py \\ + --control_stats ${control_stats} \\ + --read_metrics ${read_metrics} \\ + --meth_distribution ${meth_distribution} \\ + --depth_distribution ${depth_distribution} \\ + --mbias ${mbias} \\ + --chr_percent ${chr_percent} \\ + --twist_annotation ${twist_annotation} \\ + --repeat_annotation ${repeat_annotation} \\ + --tiled_beta ${tiled_beta} + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + methylqc_multiqc: \$(methylqc_multiqc.py --version 2>/dev/null) + pandas: \$(python -c 'import pandas; print(pandas.__version__)' 2>/dev/null) + END_VERSIONS + """ +} diff --git a/modules/local/exo/repeat_annotation.nf b/modules/local/exo/repeat_annotation.nf new file mode 100644 index 000000000..ae05923a0 --- /dev/null +++ b/modules/local/exo/repeat_annotation.nf @@ -0,0 +1,35 @@ +// Annotate low-coverage methylation calls with RepeatMasker repeats +process REPEAT_ANNOTATION { + label 'process_medium' + + container '062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/methylkit:1.20.0' + + input: + path methobj + path repeat_annot + + output: + path "methobj_lowcov_repeat_annotation.rds", emit: methobj_lowcov_repeat_annotation_obj + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + """ + repeat_annotation.R \\ + --methobj ${methobj} \\ + --repeat_annot ${repeat_annot} \\ + --methobj_repeat_out methobj_lowcov_repeat_annotation.rds + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + r-base: \$(echo \$(R --version 2>&1) | sed 's/^.*R version //; s/ .*\$//') + bioconductor-methylkit: \$(Rscript -e "library(methylKit); cat(as.character(packageVersion('methylKit')))" 2>/dev/null) + bioconductor-genomation: \$(Rscript -e "library(genomation); cat(as.character(packageVersion('genomation')))" 2>/dev/null) + bioconductor-rtracklayer: \$(Rscript -e "library(rtracklayer); cat(as.character(packageVersion('rtracklayer')))" 2>/dev/null) + bioconductor-chippeakanno: \$(Rscript -e "library(ChIPpeakAnno); cat(as.character(packageVersion('ChIPpeakAnno')))" 2>/dev/null) + bioconductor-txdb.hsapiens.ucsc.hg38.knowngene: \$(Rscript -e "library(TxDb.Hsapiens.UCSC.hg38.knownGene); cat(as.character(packageVersion('TxDb.Hsapiens.UCSC.hg38.knownGene')))" 2>/dev/null) + END_VERSIONS + """ +} diff --git a/modules/local/exo/tile_methyl_counts.nf b/modules/local/exo/tile_methyl_counts.nf new file mode 100644 index 000000000..4f3234259 --- /dev/null +++ b/modules/local/exo/tile_methyl_counts.nf @@ -0,0 +1,32 @@ +// Summarise methylation counts in 1 kb tiles +process TILE_METHYL_COUNTS { + label 'process_medium' + + container '062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/methylkit:1.20.0' + + input: + path methobj_lowcov + + output: + path "methobj_lowcov_tiled.rds", emit: methobj_lowcov_tiled + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + """ + tile_methyl_counts.R \\ + --methobj ${methobj_lowcov} \\ + --window_size 1000 \\ + --step_size 1000 \\ + --cov_bases 0 \\ + --output methobj_lowcov_tiled.rds + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + r-base: \$(echo \$(R --version 2>&1) | sed 's/^.*R version //; s/ .*\$//') + bioconductor-methylkit: \$(Rscript -e "library(methylKit); cat(as.character(packageVersion('methylKit')))" 2>/dev/null) + END_VERSIONS + """ +} diff --git a/modules/local/exo/twist_annotation.nf b/modules/local/exo/twist_annotation.nf new file mode 100644 index 000000000..543b4a8ba --- /dev/null +++ b/modules/local/exo/twist_annotation.nf @@ -0,0 +1,37 @@ +// Annotate low-coverage methylation calls with Twist methylome target categories +process TWIST_ANNOTATION { + label 'process_medium' + + container '062689759574.dkr.ecr.us-east-1.amazonaws.com/exosomedx/methylkit:1.20.0' + + input: + path methobj + path twist_bed_file + + output: + path "cpg_obj_twist_out.rds" , emit: cpg_obj_twist_out + path "methobj_lowcov_twist_annotation.rds", emit: methobj_lowcov_twist_annotation_obj + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + """ + twist_annotation.R \\ + --methobj ${methobj} \\ + --twist_bed_file ${twist_bed_file} \\ + --cpg_obj_twist_out cpg_obj_twist_out.rds \\ + --methobj_twist_out methobj_lowcov_twist_annotation.rds + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + r-base: \$(echo \$(R --version 2>&1) | sed 's/^.*R version //; s/ .*\$//') + bioconductor-methylkit: \$(Rscript -e "library(methylKit); cat(as.character(packageVersion('methylKit')))" 2>/dev/null) + bioconductor-genomation: \$(Rscript -e "library(genomation); cat(as.character(packageVersion('genomation')))" 2>/dev/null) + bioconductor-rtracklayer: \$(Rscript -e "library(rtracklayer); cat(as.character(packageVersion('rtracklayer')))" 2>/dev/null) + bioconductor-chippeakanno: \$(Rscript -e "library(ChIPpeakAnno); cat(as.character(packageVersion('ChIPpeakAnno')))" 2>/dev/null) + bioconductor-txdb.hsapiens.ucsc.hg38.knowngene: \$(Rscript -e "library(TxDb.Hsapiens.UCSC.hg38.knownGene); cat(as.character(packageVersion('TxDb.Hsapiens.UCSC.hg38.knownGene')))" 2>/dev/null) + END_VERSIONS + """ +} diff --git a/nextflow.config b/nextflow.config index df442c8c9..5909a8135 100644 --- a/nextflow.config +++ b/nextflow.config @@ -17,6 +17,14 @@ params { igenomes_base = 's3://ngi-igenomes/igenomes/' igenomes_ignore = false + // EvolvDx references (see conf/evolvdx/custom_genomes.config) + exogenomes_base = 's3://exosomedx/pipeline_resources/references/methylation' + custom_genomes_ignore = false + cpg_island_bed = null + methylqc_target_bed = null // defaults to the genome's twist_methylome_bed + repeat_annotation = null // defaults to the genome's repeat_annot + chrom_sizes = null // defaults to the genome's hg38_chrom_sizes + // MultiQC options multiqc_config = null multiqc_title = null @@ -96,6 +104,12 @@ params { run_qualimap = false run_targeted_sequencing = false + // EvolvDx run options + run_methylqc = false + run_cpg_coverage = false + run_methsnsv = false + run_picardhs_report = false + // Qualimap options bamqc_regions_file = null @@ -249,6 +263,10 @@ profiles { test_single_gpu { includeConfig 'conf/test_single_gpu.config' } test_full { includeConfig 'conf/test_full.config' } aws_batch { includeConfig 'conf/aws/batch/nextflow.config' } + evolvdx { includeConfig 'conf/evolvdx/evolvdx.config' } + evolvdx_test { includeConfig 'conf/evolvdx/exo_test.config' } + evolvdx_aws { includeConfig 'conf/evolvdx/aws.config' } + evolvdx_aws_500gb { includeConfig 'conf/evolvdx/aws_500gb.config' } } // Load nf-core custom profiles from different institutions @@ -272,6 +290,9 @@ charliecloud.registry = 'quay.io' // Load igenomes.config if required includeConfig !params.igenomes_ignore ? 'conf/igenomes.config' : 'conf/igenomes_ignored.config' +// Load EvolvDx custom genomes (S3 references) if required +includeConfig !params.custom_genomes_ignore ? 'conf/evolvdx/custom_genomes.config' : '/dev/null' + // Export these variables to prevent local Python/R libraries from conflicting with those in the container // The JULIA depot path has been adjusted to a fixed path `/usr/local/share/julia` that needs to be used for packages in the container. // See https://apeltzer.github.io/post/03-julia-lang-nextflow/ for details on that. Once we have a common agreement on where to keep Julia packages, this is adjustable. diff --git a/nextflow_schema.json b/nextflow_schema.json index 30b5f730f..b7c29fff4 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -711,6 +711,69 @@ "description": "Display hidden parameters in the help message (only works when --help or --help_full are provided)." } } + }, + "evolvdx_options": { + "title": "EvolvDx options", + "type": "object", + "fa_icon": "fas fa-dna", + "description": "EvolvDx additions on top of nf-core/methylseq (use with `-profile evolvdx`).", + "properties": { + "exogenomes_base": { + "type": "string", + "default": "s3://exosomedx/pipeline_resources/references/methylation", + "description": "Base path of the EvolvDx reference genomes defined in conf/evolvdx/custom_genomes.config.", + "fa_icon": "fas fa-folder-open" + }, + "custom_genomes_ignore": { + "type": "boolean", + "description": "Do not load the EvolvDx custom genomes config.", + "fa_icon": "fas fa-ban" + }, + "cpg_island_bed": { + "type": "string", + "format": "file-path", + "description": "BED file of CpG islands used by --run_cpg_coverage.", + "fa_icon": "fas fa-file" + }, + "methylqc_target_bed": { + "type": "string", + "format": "file-path", + "description": "Twist methylome target BED used by --run_methylqc. Defaults to the genome's `twist_methylome_bed`.", + "fa_icon": "fas fa-file" + }, + "repeat_annotation": { + "type": "string", + "format": "file-path", + "description": "Repeat annotation (bigBed) used by --run_methylqc. Defaults to the genome's `repeat_annot`.", + "fa_icon": "fas fa-file" + }, + "chrom_sizes": { + "type": "string", + "format": "file-path", + "description": "Chromosome sizes file used by --run_methylqc. Defaults to the genome's `hg38_chrom_sizes`.", + "fa_icon": "fas fa-file" + }, + "run_methylqc": { + "type": "boolean", + "description": "Run the EvolvDx cohort-level methylation QC (requires --aligner bwameth; do not combine with --methyl_kit).", + "fa_icon": "fas fa-chart-bar" + }, + "run_cpg_coverage": { + "type": "boolean", + "description": "Run EvolvDx CpG island coverage (requires --cpg_island_bed).", + "fa_icon": "fas fa-chart-area" + }, + "run_methsnsv": { + "type": "boolean", + "description": "Run EvolvDx methylation SNV/SV statistics on MethylDackel bedGraphs (requires --aligner bwameth; do not combine with --methyl_kit).", + "fa_icon": "fas fa-chart-line" + }, + "run_picardhs_report": { + "type": "boolean", + "description": "Add the EvolvDx Picard HS metrics tables to MultiQC (requires --run_targeted_sequencing and --collecthsmetrics).", + "fa_icon": "fas fa-table" + } + } } }, "allOf": [ @@ -762,6 +825,9 @@ { "$ref": "#/$defs/generic_options" }, + { + "$ref": "#/$defs/evolvdx_options" + }, { "$ref": "#/$defs/institutional_config_options" } diff --git a/scripts/aws_run_example.sh b/scripts/aws_run_example.sh new file mode 100755 index 000000000..c57450647 --- /dev/null +++ b/scripts/aws_run_example.sh @@ -0,0 +1,26 @@ +#!/bin/bash +set -euo pipefail + +# Example script to run evolvdx/evolvdx-methylseq on AWS Batch +# Prerequisites: +# - AWS CLI configured with appropriate credentials +# - nextflow-env conda environment activated: micromamba activate nextflow-env +# - AWS Batch compute environment and job queue set up + +# Parameters +PARAMS_FILE="conf/aws/aws.params.json" +PROFILE="aws_500gb" # Use 'aws' for standard queue, 'aws_500gb' for high-memory runs +WORK_DIR="s3://your-bucket/work/nextflow/$(date +%Y%m%d_%H%M%S)" + +echo "Starting evolvdx/evolvdx-methylseq run on AWS Batch" +echo "Profile: $PROFILE" +echo "Params file: $PARAMS_FILE" +echo "Work directory: $WORK_DIR" + +nextflow run evolvdx/evolvdx-methylseq \ + -profile "$PROFILE" \ + -params-file "$PARAMS_FILE" \ + -work-dir "$WORK_DIR" \ + -resume + +echo "Run complete! Results available at $(jq -r '.outdir' "$PARAMS_FILE")" diff --git a/subworkflows/local/cpg_cov/main.nf b/subworkflows/local/cpg_cov/main.nf new file mode 100644 index 000000000..fc1e90a76 --- /dev/null +++ b/subworkflows/local/cpg_cov/main.nf @@ -0,0 +1,36 @@ +/* + * CpG island coverage subworkflow + * + * Intersects the alignments with a CpG island BED and reports per-base coverage. + */ + +include { BEDTOOLS_INTERSECT_CPG } from '../../../modules/local/exo/bedtools/intersect_cpg/main' +include { BEDTOOLS_PERBASE_GENOMECOV } from '../../../modules/local/exo/bedtools/per_base_genomecov/main' + +workflow CPG_COV { + + take: + ch_alignments // channel: [ val(meta), path(bam) ] + ch_cpg_bed // channel: path(cpg_island.bed) (value channel) + + main: + ch_versions = channel.empty() + + BEDTOOLS_INTERSECT_CPG ( + ch_alignments, + 'bam', + ch_cpg_bed + ) + ch_versions = ch_versions.mix(BEDTOOLS_INTERSECT_CPG.out.version.first()) + + BEDTOOLS_PERBASE_GENOMECOV ( + BEDTOOLS_INTERSECT_CPG.out.intersect, + 'txt' + ) + ch_versions = ch_versions.mix(BEDTOOLS_PERBASE_GENOMECOV.out.version.first()) + + emit: + intersect = BEDTOOLS_INTERSECT_CPG.out.intersect // channel: [ val(meta), path(bam) ] + genomecov = BEDTOOLS_PERBASE_GENOMECOV.out.genomecov // channel: [ val(meta), path(txt) ] + versions = ch_versions // channel: path(versions.yml) +} diff --git a/subworkflows/local/methylqc/main.nf b/subworkflows/local/methylqc/main.nf new file mode 100644 index 000000000..890b05277 --- /dev/null +++ b/subworkflows/local/methylqc/main.nf @@ -0,0 +1,133 @@ +/* + * EvolvDx methylation QC subworkflow + * + * Merges per-sample MethylDackel outputs (methylKit, bedGraph, M-bias) into cohort-level + * objects, then runs control statistics, read statistics, tiled statistics, Twist and + * repeat annotation summaries and per-chromosome methylation histograms. The tables are + * converted into MultiQC custom content (emit: multiqc_files). + */ + +include { COMBINED_METHOBJ } from '../../../modules/local/exo/make_combined_methobj' +include { COMBINED_HDF5 } from '../../../modules/local/exo/make_combined_hdf5' +include { CONTROL_STATS } from '../../../modules/local/exo/control_stats' +include { BASIC_READ_STATISTICS } from '../../../modules/local/exo/basic_read_statistics' +include { TILE_METHYL_COUNTS } from '../../../modules/local/exo/tile_methyl_counts' +include { MERGE_TILED_STATS } from '../../../modules/local/exo/merge_tiled_stats' +include { TWIST_ANNOTATION } from '../../../modules/local/exo/twist_annotation' +include { REPEAT_ANNOTATION } from '../../../modules/local/exo/repeat_annotation' +include { MERGE_ANNOTATION_STATS } from '../../../modules/local/exo/merge_annotation_stats' +include { METH_HISTOGRAM_BY_CHROMOSOME } from '../../../modules/local/exo/meth_histogram_by_chromosome' +include { METHYLQC_MULTIQC } from '../../../modules/local/exo/methylqc_multiqc' + +workflow METHYLQC { + + take: + ch_methylkit // channel: [ val(meta), path(methylkit) ] + ch_bedgraph // channel: [ val(meta), path(bedgraph) ] + ch_mbias // channel: [ val(meta), path(mbias) ] + ch_twist_bed // channel: path(twist_methylome.bed) (value channel) + ch_repeat_annot // channel: path(repeat_annotation) (value channel) + ch_chrom_sizes // channel: path(chrom.sizes) (value channel) + + main: + ch_versions = channel.empty() + + // Collect per-sample files into cohort-level lists, sorted by sample id so that the + // file list and the metadata list always line up and the task inputs are stable on -resume + ch_methylkit_cohort = cohort(ch_methylkit) + ch_bedgraph_cohort = cohort(ch_bedgraph) + ch_mbias_cohort = cohort(ch_mbias) + + COMBINED_METHOBJ ( + ch_methylkit_cohort.files, + ch_methylkit_cohort.meta + ) + ch_versions = ch_versions.mix(COMBINED_METHOBJ.out.versions) + + COMBINED_HDF5 ( + ch_bedgraph_cohort.files, + ch_bedgraph_cohort.meta + ) + ch_versions = ch_versions.mix(COMBINED_HDF5.out.versions) + + CONTROL_STATS ( + COMBINED_METHOBJ.out.methobj + ) + ch_versions = ch_versions.mix(CONTROL_STATS.out.versions) + + BASIC_READ_STATISTICS ( + COMBINED_HDF5.out.merged_bedgraph_hdf5, + ch_mbias_cohort.files, + ch_mbias_cohort.meta + ) + ch_versions = ch_versions.mix(BASIC_READ_STATISTICS.out.versions) + + TILE_METHYL_COUNTS ( + COMBINED_METHOBJ.out.methobj_lowcov + ) + ch_versions = ch_versions.mix(TILE_METHYL_COUNTS.out.versions) + + MERGE_TILED_STATS ( + TILE_METHYL_COUNTS.out.methobj_lowcov_tiled + ) + ch_versions = ch_versions.mix(MERGE_TILED_STATS.out.versions) + + TWIST_ANNOTATION ( + COMBINED_METHOBJ.out.methobj_lowcov, + ch_twist_bed + ) + ch_versions = ch_versions.mix(TWIST_ANNOTATION.out.versions) + + REPEAT_ANNOTATION ( + COMBINED_METHOBJ.out.methobj_lowcov, + ch_repeat_annot + ) + ch_versions = ch_versions.mix(REPEAT_ANNOTATION.out.versions) + + MERGE_ANNOTATION_STATS ( + COMBINED_METHOBJ.out.methobj_lowcov, + TWIST_ANNOTATION.out.methobj_lowcov_twist_annotation_obj, + TWIST_ANNOTATION.out.cpg_obj_twist_out, + REPEAT_ANNOTATION.out.methobj_lowcov_repeat_annotation_obj + ) + ch_versions = ch_versions.mix(MERGE_ANNOTATION_STATS.out.versions) + + METH_HISTOGRAM_BY_CHROMOSOME ( + ch_bedgraph_cohort.files, + ch_bedgraph_cohort.meta, + ch_chrom_sizes + ) + ch_versions = ch_versions.mix(METH_HISTOGRAM_BY_CHROMOSOME.out.versions) + + METHYLQC_MULTIQC ( + CONTROL_STATS.out.control_stats, + BASIC_READ_STATISTICS.out.metrics_table, + BASIC_READ_STATISTICS.out.meth_distribution, + BASIC_READ_STATISTICS.out.depth_distribution, + BASIC_READ_STATISTICS.out.merged_mbias, + METH_HISTOGRAM_BY_CHROMOSOME.out.chr_percent_df, + MERGE_ANNOTATION_STATS.out.twist_annotation, + MERGE_ANNOTATION_STATS.out.repeat_annotation, + MERGE_TILED_STATS.out.merge_tiled_stats + ) + ch_versions = ch_versions.mix(METHYLQC_MULTIQC.out.versions) + + emit: + methobj = COMBINED_METHOBJ.out.methobj // channel: path(methobj.rds) + methobj_lowcov = COMBINED_METHOBJ.out.methobj_lowcov // channel: path(methobj_lowcov.rds) + control_stats = CONTROL_STATS.out.control_stats // channel: path(control_stats.csv) + multiqc_files = METHYLQC_MULTIQC.out.mqc // channel: path(*_mqc.json) + versions = ch_versions // channel: path(versions.yml) +} + +/* + * Turn a per-sample channel of [ meta, files ] into two value channels holding the + * cohort's files and metadata, both ordered by sample id. + */ +def cohort(ch_samples) { + def ch_sorted = ch_samples.toSortedList { a, b -> a[0].id <=> b[0].id } + return [ + files: ch_sorted.map { samples -> samples.collect { _meta, files -> files }.flatten() }, + meta : ch_sorted.map { samples -> samples.collect { meta, _files -> meta } } + ] +} diff --git a/tests/samplesheets/NSQCAM529_test.csv b/tests/samplesheets/NSQCAM529_test.csv new file mode 100644 index 000000000..e3a1fca78 --- /dev/null +++ b/tests/samplesheets/NSQCAM529_test.csv @@ -0,0 +1,2 @@ +sample,fastq_1,fastq_2,group +Healthy_1-1,s3://exosomedx/fastq/230208_VH01172_21_AACJV2VM5_demultiplex2_230208_DEV457_NSQCAM529_Twist_HumanMethylome_CRC_vs_Healthy_KM/Healthy_1-1_S1_R1_001.fastq.gz,s3://exosomedx/fastq/230208_VH01172_21_AACJV2VM5_demultiplex2_230208_DEV457_NSQCAM529_Twist_HumanMethylome_CRC_vs_Healthy_KM/Healthy_1-1_S1_R2_001.fastq.gz,Healthy diff --git a/tests/samplesheets/nf-core_samplesheet_test.csv b/tests/samplesheets/nf-core_samplesheet_test.csv new file mode 100644 index 000000000..4c92b5930 --- /dev/null +++ b/tests/samplesheets/nf-core_samplesheet_test.csv @@ -0,0 +1,5 @@ +sample,fastq_1,fastq_2,group +SRR389222_sub1,https://github.com/nf-core/test-datasets/raw/methylseq/testdata/SRR389222_sub1.fastq.gz,, +SRR389222_sub2,https://github.com/nf-core/test-datasets/raw/methylseq/testdata/SRR389222_sub2.fastq.gz,, +SRR389222_sub2,https://github.com/nf-core/test-datasets/raw/methylseq/testdata/SRR389222_sub3.fastq.gz,, +Ecoli_10K_methylated,https://github.com/nf-core/test-datasets/raw/methylseq/testdata/Ecoli_10K_methylated_R1.fastq.gz,https://github.com/nf-core/test-datasets/raw/methylseq/testdata/Ecoli_10K_methylated_R2.fastq.gz, diff --git a/tests/samplesheets/test_NSQCAM529_subsample_100k.csv b/tests/samplesheets/test_NSQCAM529_subsample_100k.csv new file mode 100644 index 000000000..ba4ab4a19 --- /dev/null +++ b/tests/samplesheets/test_NSQCAM529_subsample_100k.csv @@ -0,0 +1,2 @@ +sample,single_end,single_end,fastq_1,fastq_2,group +Healthy_1-1,FALSE,FALSE,s3://exosomedx/pipeline_resources/methylseq/test/data/Healthy_1-1_S1_subsample_100k_R1_001.fastq.gz,s3://exosomedx/pipeline_resources/methylseq/test/data/Healthy_1-1_S1_subsample_100k_R2_001.fastq.gz,Healthy \ No newline at end of file diff --git a/tests/samplesheets/test_NSQCAM529_subsample_100k_200k.csv b/tests/samplesheets/test_NSQCAM529_subsample_100k_200k.csv new file mode 100644 index 000000000..95180eab7 --- /dev/null +++ b/tests/samplesheets/test_NSQCAM529_subsample_100k_200k.csv @@ -0,0 +1,3 @@ +sample,single_end,single_end,fastq_1,fastq_2,group +Healthy_1_100k,FALSE,FALSE,s3://exosomedx/pipeline_resources/methylseq/test/data/Healthy_1-1_S1_subsample_100k_R1_001.fastq.gz,s3://exosomedx/pipeline_resources/methylseq/test/data/Healthy_1-1_S1_subsample_100k_R2_001.fastq.gz,Healthy +Healthy_1_200k,FALSE,FALSE,s3://exosomedx/pipeline_resources/methylseq/test/data/Healthy_1-1_S1_subsample_200k_R1_001.fastq.gz,s3://exosomedx/pipeline_resources/methylseq/test/data/Healthy_1-1_S1_subsample_200k_R2_001.fastq.gz,Healthy \ No newline at end of file diff --git a/tests/samplesheets/test_NSQCAM529_subsample_20k.csv b/tests/samplesheets/test_NSQCAM529_subsample_20k.csv new file mode 100644 index 000000000..fc8fad83d --- /dev/null +++ b/tests/samplesheets/test_NSQCAM529_subsample_20k.csv @@ -0,0 +1,2 @@ +sample,single_end,single_end,fastq_1,fastq_2,group +Healthy_1-1,FALSE,FALSE,s3://exosomedx/pipeline_resources/methylseq/test/data/Healthy_1-1_S1_20k_subsample_R1_001.fastq.gz,s3://exosomedx/pipeline_resources/methylseq/test/data/Healthy_1-1_S1_subsample_20k_R2_001.fastq.gz,Healthy \ No newline at end of file diff --git a/tests/scripts/dsl2_exo_picardhs.sh b/tests/scripts/dsl2_exo_picardhs.sh new file mode 100644 index 000000000..bc71c210e --- /dev/null +++ b/tests/scripts/dsl2_exo_picardhs.sh @@ -0,0 +1,12 @@ +nextflow run ../../main.nf -bucket-dir 's3://exosome-nextflow-wd/methylseq/NSQCAM_529' \ + --input ../samplesheets/NSQCAM529_test.csv \ + --outdir ../results/NSQCAM529_test \ + --genome grch38_core_bs_controls \ + --aligner bwameth \ + --clip_r1 20 \ + --clip_r2 15 \ + --methyl_kit \ + --with_hybcap \ + --probe_bed "s3://exosomedx/pipeline_resources/bedfiles/covered_targets_Twist_Methylome_hg38_annotated_collapsed.bed" \ + --fai "s3://exosomedx/pipeline_resources/references/methylation/grch38_core+bs_controls/bwameth/genome.fa.fai" \ + -c /efs/nextflow/configs/awsbatch.cfg -resume diff --git a/tests/scripts/exo_dev.sh b/tests/scripts/exo_dev.sh new file mode 100644 index 000000000..861cc9929 --- /dev/null +++ b/tests/scripts/exo_dev.sh @@ -0,0 +1,13 @@ +nextflow run exosomedx/methylseq -r exo_dev -latest \ + -bucket-dir 's3://exosome-nextflow-wd/methylseq/NSQCAM_529' \ + --input tests/samplesheets/test_NSQCAM529_subsample_100k.csv \ + --outdir tests/results/test_NSQCAM529_subsample_100k \ + --genome grch38_core_bs_controls \ + --aligner bwameth \ + --clip_r1 20 \ + --clip_r2 15 \ + --methyl_kit \ + --with_hybcap \ + --probe_bed "s3://exosomedx/pipeline_resources/bedfiles/covered_targets_Twist_Methylome_hg38_annotated_collapsed.bed" \ + --fai "s3://exosomedx/pipeline_resources/references/methylation/grch38_core+bs_controls/bwameth/genome.fa.fai" \ + -c conf/demand-nextflowbatch-ami-500Gb_multisubnets_v2.cfg -resume \ No newline at end of file diff --git a/tests/scripts/test_NSQCAM529_subsample_100k_200k.sh b/tests/scripts/test_NSQCAM529_subsample_100k_200k.sh new file mode 100644 index 000000000..fe77ea13b --- /dev/null +++ b/tests/scripts/test_NSQCAM529_subsample_100k_200k.sh @@ -0,0 +1,13 @@ +nextflow run main.nf \ + -bucket-dir 's3://exosome-nextflow-wd/methylseq/NSQCAM_529' \ + --input tests/samplesheets/test_NSQCAM529_subsample_100k_200k.csv \ + --outdir tests/results/test_NSQCAM529_subsample_100k_200k \ + --genome grch38_core_bs_controls \ + --aligner bwameth \ + --clip_r1 20 \ + --clip_r2 15 \ + --methyl_kit \ + --with_hybcap \ + --probe_bed "s3://exosomedx/pipeline_resources/bedfiles/covered_targets_Twist_Methylome_hg38_annotated_collapsed.bed" \ + --fai "s3://exosomedx/pipeline_resources/references/methylation/grch38_core+bs_controls/bwameth/genome.fa.fai" \ + -c conf/demand-nextflowbatch-ami-500Gb_multisubnets_v2.cfg \ No newline at end of file diff --git a/workflows/methylseq/main.nf b/workflows/methylseq/main.nf index 9630375df..b8a847ecb 100644 --- a/workflows/methylseq/main.nf +++ b/workflows/methylseq/main.nf @@ -21,9 +21,15 @@ include { paramsSummaryMultiqc } from '../../subworkflows/nf-core/utils include { softwareVersionsToYAML } from '../../subworkflows/nf-core/utils_nfcore_pipeline' include { methodsDescriptionText } from '../../subworkflows/local/utils_nfcore_methylseq_pipeline' include { validateInputSamplesheet } from '../../subworkflows/local/utils_nfcore_methylseq_pipeline' +include { getGenomeAttribute } from '../../subworkflows/local/utils_nfcore_methylseq_pipeline' include { BAM_TAPS_CONVERSION } from '../../subworkflows/nf-core/bam_taps_conversion' include { BAM_METHYLDACKEL } from '../../subworkflows/nf-core/bam_methyldackel/main' include { TARGETED_SEQUENCING } from '../../subworkflows/local/targeted_sequencing' +include { METHYLDACKEL_EXTRACT as METHYLDACKEL_EXTRACT_METHYLKIT } from '../../modules/nf-core/methyldackel/extract/main' +include { CPG_COV } from '../../subworkflows/local/cpg_cov' +include { METHYLQC } from '../../subworkflows/local/methylqc' +include { METHSNSV } from '../../modules/local/exo/methsnsv' +include { CUSTOM_MULTIQC_PICARDHS } from '../../modules/local/exo/custom_multiqc/custom_multiqc_picardhs' /* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -49,7 +55,9 @@ workflow METHYLSEQ { ch_reads = channel.empty() ch_bam = channel.empty() ch_bai = channel.empty() - ch_gzi = channel.empty() + // EvolvDx: upstream leaves ch_gzi empty, which makes the .combine(ch_gzi) inside TARGETED_SEQUENCING emit + // nothing, so PICARD_COLLECTHSMETRICS never runs. Provide an empty (meta, path) pair instead. + ch_gzi = channel.value([ [:], [] ]) ch_bedgraph = channel.empty() ch_aligner_mqc = channel.empty() ch_rastair_mbias = channel.empty() @@ -255,6 +263,24 @@ workflow METHYLSEQ { ch_methylkit = BAM_METHYLDACKEL.out.methydackel_extract_methylkit // channel: [ val(meta), [ methylkit ] ] ch_mbias = BAM_METHYLDACKEL.out.methydackel_mbias // channel: [ val(meta), [ mbias ] ] ch_versions = ch_versions.mix(BAM_METHYLDACKEL.out.versions) + + // + // EvolvDx: MethylDackel's --methylKit option replaces the bedGraph output, but the EvolvDx QC + // steps need both formats. Run a second extraction that writes methylKit files. + // + if (params.run_methylqc) { + if (params.methyl_kit) { + error "ERROR: --run_methylqc needs bedGraph and methylKit output; do not combine it with --methyl_kit (the pipeline extracts methylKit files itself)" + } + METHYLDACKEL_EXTRACT_METHYLKIT ( + ch_methyldackel_inputs.bam, + ch_methyldackel_inputs.bai, + ch_methyldackel_inputs.fasta, + ch_methyldackel_inputs.fasta_index + ) + ch_methylkit = METHYLDACKEL_EXTRACT_METHYLKIT.out.methylkit + ch_versions = ch_versions.mix(METHYLDACKEL_EXTRACT_METHYLKIT.out.versions) + } } // @@ -306,6 +332,64 @@ workflow METHYLSEQ { ch_versions = ch_versions.mix(PRESEQ_LCEXTRAP.out.versions) } + // + // SUBWORKFLOW: EvolvDx CpG island coverage + // skipped by default. to use run with `--run_cpg_coverage` param. + // + if (params.run_cpg_coverage) { + if (!params.cpg_island_bed) { + error "ERROR: --cpg_island_bed must be specified when using --run_cpg_coverage" + } + CPG_COV ( + ch_bam, + channel.fromPath(params.cpg_island_bed, checkIfExists: true).first() + ) + ch_versions = ch_versions.mix(CPG_COV.out.versions) + } + + // + // MODULE: EvolvDx methylation SNV/SV statistics from bedGraph + // skipped by default. to use run with `--run_methsnsv` param. + // + if (params.run_methsnsv) { + if (params.aligner != 'bwameth' || params.taps) { + error "ERROR: --run_methsnsv requires --aligner bwameth (MethylDackel bedGraph output) and is not compatible with --taps" + } + if (params.methyl_kit) { + error "ERROR: --run_methsnsv needs bedGraph output, which MethylDackel does not write with --methyl_kit" + } + METHSNSV ( + ch_bedgraph + ) + ch_versions = ch_versions.mix(METHSNSV.out.version) + } + + // + // SUBWORKFLOW: EvolvDx cohort-level methylation QC + // skipped by default. to use run with `--run_methylqc` param. + // + if (params.run_methylqc) { + if (params.aligner != 'bwameth' || params.taps) { + error "ERROR: --run_methylqc requires --aligner bwameth (MethylDackel output) and is not compatible with --taps" + } + // EvolvDx QC references: explicit --params win, otherwise taken from the selected genome + def methylqc_target_bed = params.methylqc_target_bed ?: getGenomeAttribute('twist_methylome_bed') + def repeat_annotation = params.repeat_annotation ?: getGenomeAttribute('repeat_annot') + def chrom_sizes = params.chrom_sizes ?: getGenomeAttribute('hg38_chrom_sizes') + if (!methylqc_target_bed || !repeat_annotation || !chrom_sizes) { + error "ERROR: --run_methylqc requires --methylqc_target_bed, --repeat_annotation and --chrom_sizes (or a --genome that defines twist_methylome_bed, repeat_annot and hg38_chrom_sizes)" + } + METHYLQC ( + ch_methylkit, + ch_bedgraph, + ch_mbias, + channel.fromPath(methylqc_target_bed, checkIfExists: true).first(), + channel.fromPath(repeat_annotation, checkIfExists: true).first(), + channel.fromPath(chrom_sizes, checkIfExists: true).first() + ) + ch_versions = ch_versions.mix(METHYLQC.out.versions) + } + // // Collate and save software versions // @@ -382,6 +466,25 @@ workflow METHYLSEQ { if (!params.skip_fastqc) { ch_multiqc_files = ch_multiqc_files.mix(FASTQC.out.zip.collect{ it[1] }.ifEmpty([])) } + if (params.run_methylqc) { + ch_multiqc_files = ch_multiqc_files.mix(METHYLQC.out.multiqc_files.flatten()) + } + + if (params.run_picardhs_report) { + if (!params.run_targeted_sequencing || !params.collecthsmetrics) { + error "ERROR: --run_picardhs_report requires --run_targeted_sequencing and --collecthsmetrics" + } + CUSTOM_MULTIQC_PICARDHS ( + TARGETED_SEQUENCING.out.picard_metrics.collect{ it[1] }, + file(params.input, checkIfExists: true), + file("$projectDir/assets/multiqc/picard_on_off_rate_multiqc_header.txt", checkIfExists: true), + file("$projectDir/assets/multiqc/picard_uniformity_multiqc_header.yml", checkIfExists: true), + file("$projectDir/assets/multiqc/other_picard_hs_metrics_multiqc_header.yml", checkIfExists: true) + ) + ch_multiqc_files = ch_multiqc_files.mix(CUSTOM_MULTIQC_PICARDHS.out.yml) + ch_multiqc_files = ch_multiqc_files.mix(CUSTOM_MULTIQC_PICARDHS.out.tsv) + ch_versions = ch_versions.mix(CUSTOM_MULTIQC_PICARDHS.out.versions) + } MULTIQC ( ch_multiqc_files.collect(), diff --git a/workflows/methylseq/nextflow.config b/workflows/methylseq/nextflow.config index 7578383b2..f04997b36 100644 --- a/workflows/methylseq/nextflow.config +++ b/workflows/methylseq/nextflow.config @@ -4,6 +4,7 @@ includeConfig "../../conf/modules/trimgalore.config" includeConfig "../../conf/modules/multiqc.config" includeConfig "../../conf/modules/preseq_lcextrap.config" includeConfig "../../conf/modules/qualimap_bamqc.config" +includeConfig "../../conf/modules/evolvdx.config" // subworkflow configs includeConfig "../../conf/subworkflows/fasta_index_bismark_bwameth.config"