From 6393dbc1fd542d1d6e2e13970b3304939c5d5605 Mon Sep 17 00:00:00 2001 From: Steven Roberts Date: Thu, 15 Oct 2026 05:34:05 -0700 Subject: [PATCH 1/2] Dashboard: compute 'What the evidence says so far' from the data The section's prose was hardcoded and went stale as studies were added: it said every real study was an RNA-seq pathogen challenge after heat, OA and starvation studies landed, and it listed single-study phenotypes as pools with '0 genes measured in both'. Every sentence is now derived from data.json: stressor and assay coverage, only k>=2 pools (with 'at least two studies' for 3-study pools), single-study phenotypes named as awaiting replication, tier counts, caveats that fire only when true, and next curation steps from unharmonized assays, unreplicated phenotypes and QC failures. Co-Authored-By: Claude Opus 5.5 --- docs/index.qmd | 90 ++++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 76 insertions(+), 14 deletions(-) diff --git a/docs/index.qmd b/docs/index.qmd index 4b20dbe..4061f74 100644 --- a/docs/index.qmd +++ b/docs/index.qmd @@ -76,20 +76,82 @@ html`
```{ojs} { - const pools = data.pools.filter(p => !p.simulated); - const classes = data.candidates.by_evidence_class.filter(d => !d.simulated); - const poolText = pools.length - ? pools.map(p => `${p.studies.join(" and ")} pool on ${phen(p.phenotype)}: ${fmt(p.n_features_k2plus)} genes measured in both, ${fmt(p.n_features_k2plus_significant)} significant after multiple-testing correction`).join("; ") - : "no cross-study pool has formed yet"; - const classText = classes.map(d => `${fmt(d.n)} ${d.evidence_class.replace(/_/g, " ")}`).join(", "); - const gap = k.n_real_comparisons_with_resilience_phenotype === 0 - ? "No real comparison measures a resilience outcome yet." - : `${k.n_real_comparisons_with_resilience_phenotype} of ${k.n_comparisons_real} real comparisons measure a resilience outcome.`; - return html`

${poolText}. Ranked candidates by evidence class: ${classText}.

-

${gap} All real studies so far are RNA-seq pathogen challenges, so the ranked list reflects transcriptional - response to infection, not resistance. A candidate here is an association across the available evidence, not a - validated biomarker. The next curation step is a second stressor class and a study with a measured survival or - pathogen-load phenotype (candidate studies).

`; + // Every sentence below is computed from data.json, so it stays current as studies are added. + const ASSAY = {rnaseq: "RNA-seq", methylation: "methylation", proteomics: "proteomics", metabolomics: "metabolomics"}; + const assay = (a) => ASSAY[a] ?? a; + const list = (xs) => xs.length < 2 ? xs.join("") : `${xs.slice(0, -1).join(", ")} and ${xs[xs.length - 1]}`; + // Lower-case a label mid-sentence, leaving acronyms like "pH" alone. + const lc = (s) => s.split(" ").map(w => w.length > 1 && w[1] === w[1].toLowerCase() ? w[0].toLowerCase() + w.slice(1) : w).join(" "); + const relevance = (id) => data.vocab.phenotype_resilience_relevance?.[id]; + + const real = data.studies.filter(s => !s.simulated); + const withEvidence = real.filter(s => s.n_evidence_records > 0); + const assaysRegistered = [...new Set(real.flatMap(s => s.assay_types))]; + const assaysWithEvidence = new Set(withEvidence.flatMap(s => s.assay_types)); + const awaiting = real.filter(s => s.n_evidence_records === 0 && s.qc_status !== "failed" + && s.assay_types.some(a => !assaysWithEvidence.has(a))); + const failed = real.filter(s => s.qc_status === "failed"); + + const pools = data.pools.filter(p => !p.simulated && p.feature_type === "gene"); + const cross = pools.filter(p => p.max_k >= 2).sort((a, b) => b.studies.length - a.studies.length); + const single = pools.filter(p => p.max_k < 2); + + const classes = data.candidates.by_evidence_class.filter(d => !d.simulated).sort((a, b) => b.n - a.n); + const nRanked = classes.reduce((s, d) => s + d.n, 0); + const top = data.candidates.top_real; + const topPhens = [...new Set(top.map(r => r.phenotype))]; + + const coverage = html`

${real.length} real studies cover ${k.n_real_stressor_classes} stressor + ${k.n_real_stressor_classes === 1 ? "class" : "classes"} + (${list(k.real_stressor_classes.map(s => lc(stress(s))))}) and + ${list(assaysRegistered.map(assay))} assays. ${withEvidence.length + ? `${withEvidence.length} of them have harmonized evidence (${k.n_comparisons_real_harmonized} of + ${k.n_comparisons_real} comparisons), ${assaysWithEvidence.size === 1 ? "all" : "spanning"} + ${list([...assaysWithEvidence].map(assay))}.` + : "None has harmonized evidence yet."}

`; + + const poolItems = cross.map(p => html`
  • ${phen(p.phenotype)} + (${p.studies.length} studies: ${p.studies.join(", ")}): ${fmt(p.n_features_k2plus)} genes measured in at + least two studies, ${fmt(p.n_features_k2plus_significant)} with a pooled effect significant after + multiple-testing correction.
    • `); + const poolBlock = cross.length + ? html`

    Cross-study pools, where the same gene can be compared between independent studies:

      ${poolItems}
    ` + : html`

    No cross-study pool has formed yet; every phenotype is supported by a single study.

    `; + const singleText = single.length + ? html`

    ${list(single.map((p, i) => i ? lc(phen(p.phenotype)) : phen(p.phenotype)))} ${single.length === 1 ? "has" : "have"} evidence from one + study only (${list(single.flatMap(p => p.studies))}), so ${single.length === 1 ? "its" : "their"} genes stay + emerging until a second study replicates them.

    ` + : ""; + + const tierText = html`

    ${fmt(nRanked)} gene × phenotype candidates are ranked: + ${fmt(k.n_candidates_real_high_priority)} high priority (replicated across studies), + ${fmt(k.n_candidates_real_multi_omics)} multi-omics, ${fmt(k.n_candidates_real_emerging)} emerging. + By evidence class: ${classes.map(d => `${fmt(d.n)} ${d.evidence_class.replace(/_/g, " ")}`).join(", ")}.

    `; + + const caveats = []; + caveats.push(`${k.n_real_comparisons_with_resilience_phenotype} of ${k.n_comparisons_real} real comparisons + measure a resilience outcome; the rest measure disease, stress response, or exposure alone.`); + if (topPhens.length === 1 && relevance(topPhens[0]) !== "resilience") + caveats.push(`The top-ranked candidates all come from the ${lc(phen(topPhens[0]))} pool, so they describe the response to ${relevance(topPhens[0]) === "disease" ? "infection" : "the stressor"}, + not resistance to it.`); + if (k.n_candidates_real_multi_omics === 0) + caveats.push(`The multi-omics tier is empty because no non-RNA-seq study has been harmonized yet.`); + caveats.push(`A candidate here is an association across the available evidence, not a validated biomarker.`); + + const next = []; + if (awaiting.length) + next.push(`harmonize the registered ${list([...new Set(awaiting.flatMap(s => s.assay_types) + .filter(a => !assaysWithEvidence.has(a)))].map(assay))} studies (${awaiting.map(s => s.study_id).join(", ")}) + to open the multi-omics tier`); + if (single.length) + next.push(`find a second study for ${list(single.map(p => lc(phen(p.phenotype))))}`); + if (failed.length) + next.push(`resolve the QC failures in ${failed.map(s => s.study_id).join(", ")}`); + + return html`${coverage}${poolBlock}${singleText}${tierText} + ${Object.assign(document.createElement("p"), {innerHTML: caveats.join(" ")})} + ${next.length ? html`

    Next curation steps: ${list(next)} + (candidate studies).

    ` : ""}`; } ``` From fcd6c3d5580e2b76911c335d7bad576d59597f87 Mon Sep 17 00:00:00 2001 From: Steven Roberts Date: Thu, 15 Oct 2026 05:38:37 -0700 Subject: [PATCH 2/2] Dashboard: top candidates per cross-study pool The overall top list is filled by whichever pool has the most studies, so thermal tolerance's replicated candidates never appeared beside disease susceptibility's. The builder now writes the top 5 per phenotype (top_real_by_phenotype / top_simulated_by_phenotype, with cards and gene annotations), and the home page shows one table per k>=2 pool. Co-Authored-By: Claude Opus 5.5 --- docs/index.qmd | 31 +++++++++++++++++++++++++++---- src/reporting/dashboard.py | 24 ++++++++++++++++++------ tests/test_dashboard.py | 18 ++++++++++++++++++ 3 files changed, 63 insertions(+), 10 deletions(-) diff --git a/docs/index.qmd b/docs/index.qmd index 4061f74..df4f92c 100644 --- a/docs/index.qmd +++ b/docs/index.qmd @@ -133,7 +133,7 @@ html`
    measure a resilience outcome; the rest measure disease, stress response, or exposure alone.`); if (topPhens.length === 1 && relevance(topPhens[0]) !== "resilience") caveats.push(`The top-ranked candidates all come from the ${lc(phen(topPhens[0]))} pool, so they describe the response to ${relevance(topPhens[0]) === "disease" ? "infection" : "the stressor"}, - not resistance to it.`); + not resistance to it; each pool's own top candidates are listed below.`); if (k.n_candidates_real_multi_omics === 0) caveats.push(`The multi-omics tier is empty because no non-RNA-seq study has been harmonized yet.`); caveats.push(`A candidate here is an association across the available evidence, not a validated biomarker.`); @@ -171,8 +171,8 @@ html`

    Ranked by tier, then score${topPhen ? html`, all for ${topPhen} ``` ```{ojs} -Inputs.table(data.candidates.top_real, { - columns: ["feature_id_standardized", "gene_description", ...(topPhen ? [] : ["phenotype_label"]), +candidateTable = (rows, {showPhenotype = false, maxRows = 15} = {}) => Inputs.table(rows, { + columns: ["feature_id_standardized", "gene_description", ...(showPhenotype ? ["phenotype_label"] : []), "tier", "k_studies", "pooled_effect", "adjusted_p_value"], header: {feature_id_standardized: "Gene", gene_description: "Description", phenotype_label: "Phenotype", tier: "Tier", k_studies: "Studies", pooled_effect: "log2FC", adjusted_p_value: "Adj. p"}, @@ -183,10 +183,33 @@ Inputs.table(data.candidates.top_real, { pooled_effect: v => (v == null ? "—" : v.toFixed(2)), adjusted_p_value: fmtP }, - rows: 15, layout: "auto" + rows: maxRows, layout: "auto" }) ``` +```{ojs} +candidateTable(data.candidates.top_real, {showPhenotype: !topPhen}) +``` + +### Top candidates per pool + +```{ojs} +{ + // The overall list above is dominated by the pool with the most studies, so each + // cross-study pool also shows its own best candidates. + const byPhen = data.candidates.top_real_by_phenotype ?? {}; + const pools = data.pools + .filter(p => !p.simulated && p.feature_type === "gene" && p.max_k >= 2 && byPhen[p.phenotype]?.length) + .sort((a, b) => b.studies.length - a.studies.length || a.phenotype.localeCompare(b.phenotype)); + if (!pools.length) return html`

    No cross-study pool has ranked candidates yet.

    `; + return html`${pools.map(p => html` +

    ${phen(p.phenotype)}

    +

    ${p.studies.length} studies (${p.studies.join(", ")}); top ${byPhen[p.phenotype].length} + by tier, then score.

    + ${candidateTable(byPhen[p.phenotype], {maxRows: byPhen[p.phenotype].length})}`)}`; +} +``` + ## Studies ```{ojs} diff --git a/src/reporting/dashboard.py b/src/reporting/dashboard.py index 12e63cb..2d48c20 100644 --- a/src/reporting/dashboard.py +++ b/src/reporting/dashboard.py @@ -45,6 +45,9 @@ def _built_crosswalks() -> list[Path]: SIGNIFICANCE_THRESHOLD = 0.05 TOP_N_CANDIDATES = 15 +# The overall top list is dominated by whichever pool has the most studies, so +# each phenotype also gets its own short list. +TOP_N_PER_PHENOTYPE = 5 # --------------------------------------------------------------------------- # @@ -266,7 +269,7 @@ def _gene_annotations(gene_ids: list[str]) -> dict[str, dict]: def _candidate_summary(phenotype_labels: dict, cards_dir: Path | None = None) -> dict: if not CANDIDATES_TABLE.exists(): return {"by_tier": [], "by_evidence_class": [], "top_real": [], "top_simulated": [], - "n_total": 0} + "top_real_by_phenotype": {}, "top_simulated_by_phenotype": {}, "n_total": 0} df = pd.read_csv(CANDIDATES_TABLE, sep="\t", low_memory=False) df["simulated"] = df["simulated"].map(_bool) by_tier = [ @@ -290,8 +293,8 @@ def _candidate_summary(phenotype_labels: dict, cards_dir: Path | None = None) -> "n_supporting_layers", "mapping_confidences", "simulated", "card_file"] cols = [c for c in cols if c in ranked.columns] - def _top(frame: pd.DataFrame) -> list[dict]: - rows = frame.head(TOP_N_CANDIDATES)[cols].to_dict(orient="records") + def _top(frame: pd.DataFrame, n: int = TOP_N_CANDIDATES) -> list[dict]: + rows = frame.head(n)[cols].to_dict(orient="records") for r in rows: r["feature_id_standardized"] = str(r["feature_id_standardized"]) r["phenotype_label"] = phenotype_labels.get(r.get("phenotype"), r.get("phenotype")) @@ -302,11 +305,18 @@ def _top(frame: pd.DataFrame) -> list[dict]: top_real = _top(ranked[~ranked["simulated"]]) top_sim = _top(ranked[ranked["simulated"]]) - for r in top_real + top_sim: + # Ranked order is kept within each phenotype, so head() is that phenotype's top. + by_phen_real = {phen: _top(grp, TOP_N_PER_PHENOTYPE) + for phen, grp in ranked[~ranked["simulated"]].groupby("phenotype", sort=True)} + by_phen_sim = {phen: _top(grp, TOP_N_PER_PHENOTYPE) + for phen, grp in ranked[ranked["simulated"]].groupby("phenotype", sort=True)} + all_real = top_real + [r for rows in by_phen_real.values() for r in rows] + all_rows = all_real + top_sim + [r for rows in by_phen_sim.values() for r in rows] + for r in all_rows: r["card_page"] = _publish_card(r.pop("card_file", None), cards_dir) - ann = _gene_annotations([r["feature_id_standardized"] for r in top_real + ann = _gene_annotations([r["feature_id_standardized"] for r in all_real if r.get("feature_type") == "gene"]) - for r in top_real: + for r in all_real: r.update(ann.get(r["feature_id_standardized"], {})) return { @@ -314,6 +324,8 @@ def _top(frame: pd.DataFrame) -> list[dict]: "by_evidence_class": by_class, "top_real": top_real, "top_simulated": top_sim, + "top_real_by_phenotype": by_phen_real, + "top_simulated_by_phenotype": by_phen_sim, "n_total": int(len(df)), "n_real": int((~df["simulated"]).sum()), "n_simulated": int(df["simulated"].sum()), diff --git a/tests/test_dashboard.py b/tests/test_dashboard.py index aae12a1..4be6222 100644 --- a/tests/test_dashboard.py +++ b/tests/test_dashboard.py @@ -17,6 +17,11 @@ } +def dashboard_tier_order(): + from prioritize.rank import TIER_ORDER + return TIER_ORDER + + def _point_at_reports(monkeypatch, reports_dir): monkeypatch.setattr(dashboard, "EVIDENCE_TABLE", reports_dir / "evidence" / "evidence_table.tsv") monkeypatch.setattr(dashboard, "CANDIDATES_TABLE", reports_dir / "evidence_cards" / "candidates.tsv") @@ -41,6 +46,7 @@ def test_builder_reports_missing_outputs_instead_of_failing(tmp_path, monkeypatc assert data["kpis"]["n_studies_real"] >= 1 assert data["kpis"]["n_evidence_records_real"] == 0 assert data["candidates"]["top_real"] == [] + assert data["candidates"]["top_real_by_phenotype"] == {} assert all(not c["harmonized"] for c in data["comparisons"]) # Nothing fabricated: no cards were copied when there were none to copy. assert not (out.parent / "cards").exists() @@ -70,6 +76,18 @@ def test_builder_summarizes_a_demo_pipeline_run(isolated_reports, isolated_regis assert kpis["n_candidates_real_high_priority"] == 0 assert all(r["simulated"] for r in data["candidates"]["top_simulated"]) assert data["candidates"]["top_real"] == [] + assert data["candidates"]["top_real_by_phenotype"] == {} + + # Each phenotype gets its own short list, so a pool that never reaches the + # overall top list still shows its best candidates, in ranked order. + by_phen = data["candidates"]["top_simulated_by_phenotype"] + assert {"thermal_tolerance", "larval_viability"} <= set(by_phen) + tier_rank = {t: i for i, t in enumerate(dashboard_tier_order())} + for phen, rows in by_phen.items(): + assert 0 < len(rows) <= dashboard.TOP_N_PER_PHENOTYPE + assert all(r["phenotype"] == phen and r["simulated"] for r in rows) + keys = [(tier_rank[r["tier"]], -r["score"]) for r in rows] + assert keys == sorted(keys) by_study = {r["study_id"]: r for r in data["evidence"]["by_study"]} assert set(ALL_DEMO_STUDY_IDS) <= set(by_study)