feat: Report FP / FN negative variants in single table

workflow/Snakefile

@@ -23,6 +23,7 @@ if "variant-calls" in config:
                 vartype=["snvs", "indels"],
             ),
             get_fp_fn_reports,
+            get_fp_fn_reports_benchmarks,
             # collect the checkpoint inputs to avoid issues when
             # --all-temp is used: --all-temp leads to premature deletion
             # of the checkpoint output

workflow/resources/datavzrd/fp-fn-per-benchmark-config.yte.yaml

@@ -0,0 +1,44 @@
+__use_yte__: true
+
+__variables__:
+  green: "#74c476"
+  orange: "#fd8d3c"
+  labels: ?params.labels
+
+name: ?f"{wildcards.classification} of {wildcards.benchmark}"
+
+webview-controls: true
+default-view: results-table
+
+datasets:
+  results:
+    path: ?input.table
+    separator: "\t"
+    offer-excel: true
+    links:
+      link to table:
+        column: callset
+        table-row: results-table/callset
+
+views:
+  ?for view in ["main", "coverage"]:
+    results-table:
+      dataset: results
+      # desc: |
+        # ?f"""
+        #  Benchmark version: {params.genome} {params.version}
+        # """
+      page-size: 12
+      render-table:
+        columns:
+          coverage:
+            plot:
+              heatmap:
+                scale: ordinal
+          sort_index:
+            display-mode: hidden
+          ?if params.somatic:
+            true_genotype:
+              display-mode: hidden
+            predicted_genotype:
+              display-mode: hidden

workflow/rules/common.smk

@@ -93,7 +93,7 @@ def get_plot_cov_labels():  # TODO check if ever used anywhere
     def label(name):
         lower, upper = get_cov_interval(name)
         if upper:
-            return f"{lower}-{upper-1}"
+            return f"{lower}-{upper - 1}"
         return f"≥{lower}"
 
     return {name: label(name) for name in low_coverages}
@@ -403,6 +403,16 @@ def get_coverages(wildcards):
     return coverages
 
 
+def get_coverages_of_callset(callset):
+    benchmark = config["variant-calls"][callset]["benchmark"]
+    high_cov_status = benchmarks[benchmark].get("high-coverage", False)
+    if high_cov_status:
+        coverages = high_coverages
+    else:
+        coverages = low_coverages
+    return coverages
+
+
 def get_somatic_status(wildcards):
     if hasattr(wildcards, "benchmark"):
         return genomes[benchmarks[wildcards.benchmark]["genome"]].get("somatic")
@@ -468,10 +478,17 @@ def get_collect_stratifications_input(wildcards):
     )
 
 
+def get_collect_stratifications_fp_fn_input(wildcards):
+    return expand(
+        "results/fp-fn/callsets/{{callset}}/{cov}.{{classification}}.tsv",
+        cov=get_nonempty_coverages(wildcards),
+    )
+
+
 def get_fp_fn_reports(wildcards):
     for genome in used_genomes:
         yield from expand(
-            "results/report/fp-fn/{genome}/{cov}/{classification}",
+            "results/report/fp-fn/genomes/{genome}/{cov}/{classification}",
             genome=genome,
             cov={
                 cov
@@ -482,6 +499,15 @@ def get_fp_fn_reports(wildcards):
         )
 
 
+def get_fp_fn_reports_benchmarks(wildcards):
+    for genome in used_genomes:
+        yield from expand(
+            "results/report/fp-fn/benchmarks/{benchmark}/{classification}",
+            benchmark={benchmark for benchmark in used_benchmarks},
+            classification=["fp", "fn"],
+        )
+
+
 def get_benchmark_callsets(benchmark):
     return [
         callset
@@ -497,6 +523,13 @@ def get_collect_precision_recall_input(wildcards):
     )
 
 
+def get_collect_fp_fn_benchmark_input(wildcards):
+    callsets = get_benchmark_callsets(wildcards.benchmark)
+    return expand(
+        "results/fp-fn/callsets/{callset}.{{classification}}.tsv", callset=callsets
+    )
+
+
 def get_genome_name(wildcards):
     if hasattr(wildcards, "benchmark"):
         return get_benchmark(wildcards.benchmark).get("genome")
@@ -546,19 +579,30 @@ def get_callset_label_entries(callsets):
 
 
 def get_collect_fp_fn_callsets(wildcards):
-    return get_genome_callsets(wildcards.genome)
+    callsets = get_genome_callsets(wildcards.genome)
+    callsets = [
+        callset
+        for callset in callsets
+        if wildcards.cov in get_coverages_of_callset(callset)
+    ]
+    return callsets
 
 
 def get_collect_fp_fn_input(wildcards):
     callsets = get_collect_fp_fn_callsets(wildcards)
     return expand(
-        "results/fp-fn/callsets/{{cov}}/{callset}/{{classification}}.tsv",
+        "results/fp-fn/callsets/{callset}/{{cov}}.{{classification}}.tsv",
         callset=callsets,
     )
 
 
 def get_collect_fp_fn_labels(wildcards):
     callsets = get_genome_callsets(wildcards.genome)
+    callsets = [
+        callset
+        for callset in callsets
+        if wildcards.cov in get_coverages_of_callset(callset)
+    ]
     return get_callset_label_entries(callsets)
 
 

workflow/rules/eval.smk

@@ -338,9 +338,9 @@ rule extract_fp_fn:
         calls="results/vcfeval/{callset}/{cov}/output.vcf.gz",
         common_src=common_src,
     output:
-        "results/fp-fn/callsets/{cov}/{callset}/{classification}.tsv",
+        "results/fp-fn/callsets/{callset}/{cov}.{classification}.tsv",
     log:
-        "logs/extract-fp-fn/{cov}/{callset}/{classification}.log",
+        "logs/extract-fp-fn/{callset}/{cov}.{classification}.log",
     conda:
         "../envs/vembrane.yaml"
     script:
@@ -374,18 +374,55 @@ rule collect_fp_fn:
         "../scripts/collect-fp-fn.py"
 
 
+rule collect_stratifications_fp_fn:
+    input:
+        get_collect_stratifications_fp_fn_input,
+    output:
+        "results/fp-fn/callsets/{callset}.{classification}.tsv",
+    params:
+        coverages=get_nonempty_coverages,
+        coverage_lower_bounds=get_coverages,
+    log:
+        "logs/fp-fn/callsets/{callset}.{classification}.log",
+    conda:
+        "../envs/stats.yaml"
+    # This has to happen after precision/recall has been computed, otherwise we risk
+    # extremely high memory usage if a callset does not match the truth at all.
+    priority: 1
+    script:
+        "../scripts/collect-stratifications-fp-fn.py"
+
+
+rule collect_fp_fn_benchmark:
+    input:
+        tables=get_collect_fp_fn_benchmark_input,
+    output:
+        "results/fp-fn/benchmarks/{benchmark}.{classification}.tsv",
+    params:
+        callsets=lambda w: get_benchmark_callsets(w.benchmark),
+    log:
+        "logs/fp-fn/benchmarks/{benchmark}.{classification}.log",
+    conda:
+        "../envs/stats.yaml"
+    script:
+        "../scripts/collect-fp-fn-benchmarks.py"
+
+
 rule report_fp_fn:
     input:
         main_dataset="results/fp-fn/genomes/{genome}/{cov}/{classification}/main.tsv",
         dependency_sorting_datasets="results/fp-fn/genomes/{genome}/{cov}/{classification}/dependency-sorting",
         config=workflow.source_path("../resources/datavzrd/fp-fn-config.yte.yaml"),
     output:
         report(
-            directory("results/report/fp-fn/{genome}/{cov}/{classification}"),
+            directory("results/report/fp-fn/genomes/{genome}/{cov}/{classification}"),
             htmlindex="index.html",
             category="{classification} variants",
-            subcategory=lambda w: w.genome,
-            labels=lambda w: {"coverage": w.cov},
+            subcategory="per genome",
+            labels=lambda w: {
+                "coverage": w.cov,
+                "genome": w.genome,
+            },
         ),
     log:
         "logs/datavzrd/fp-fn/{genome}/{cov}/{classification}.log",
@@ -394,3 +431,30 @@ rule report_fp_fn:
         version=get_genome_version,
     wrapper:
         "v5.0.1/utils/datavzrd"
+
+
+rule report_fp_fn_benchmark:
+    input:
+        table="results/fp-fn/benchmarks/{benchmark}.{classification}.tsv",
+        config=workflow.source_path(
+            "../resources/datavzrd/fp-fn-per-benchmark-config.yte.yaml"
+        ),
+    output:
+        report(
+            directory("results/report/fp-fn/benchmarks/{benchmark}/{classification}"),
+            htmlindex="index.html",
+            category="{classification} variants",
+            subcategory="per benchmark",
+            labels={
+                "benchmark": "{benchmark}",
+                "classification": "{classification}",
+            },
+        ),
+    log:
+        "logs/datavzrd/fp-fn/{benchmark}/{classification}.log",
+    params:
+        labels=lambda w: get_callsets_labels(get_benchmark_callsets(w.benchmark)),
+        version=get_genome_version,
+        somatic=get_somatic_status,
+    wrapper:
+        "v5.0.1/utils/datavzrd"

workflow/scripts/collect-fp-fn-benchmarks.py

@@ -0,0 +1,42 @@
+import sys
+sys.stderr = open(snakemake.log[0], "w")
+
+import pandas as pd
+
+
+def load_data(path, callset):
+    d = pd.read_csv(path, sep="\t")
+    d.insert(0, "callset", callset)
+    return d
+
+
+results = pd.concat(
+    [
+        load_data(f, callset)
+        for f, callset in zip(snakemake.input.tables, snakemake.params.callsets)
+    ],
+    axis="rows",
+)
+
+def cov_key(cov_label):
+    # return lower bound as integer for sorting
+    if ".." in cov_label:
+        return int(cov_label.split("..")[0])
+    else:
+        return int(cov_label[1:])
+
+
+
+def sort_key(col):
+    if col.name == "callset":
+        return col
+    if col.name == "coverage":
+        return col.apply(cov_key)
+    else:
+        return col
+
+
+results.sort_values(["callset", "coverage"], inplace=True, key=sort_key)
+results["sort_index"] = results["coverage"].apply(cov_key)
+
+results.to_csv(snakemake.output[0], sep="\t", index=False)

workflow/scripts/collect-stratifications-fp-fn.py

@@ -0,0 +1,56 @@
+import sys
+
+sys.stderr = open(snakemake.log[0], "w")
+
+import pandas as pd
+
+
+def get_cov_label(coverage):
+    lower = snakemake.params.coverage_lower_bounds[coverage]
+    bounds = [
+        bound
+        for bound in snakemake.params.coverage_lower_bounds.values()
+        if bound > lower
+    ]
+    if bounds:
+        upper = min(bounds)
+        return f"{lower}..{upper}"
+    else:
+        return f"≥{lower}"
+
+
+def load_data(f, coverage):
+    d = pd.read_csv(f, sep="\t")
+    d.insert(0, "coverage", get_cov_label(coverage))
+    return d
+
+
+if snakemake.input:
+    report = pd.concat(
+        load_data(f, cov) for cov, f in zip(snakemake.params.coverages, snakemake.input)
+    )
+
+    # TODO With separate files for SNVs and indels with e.g. STRELKA no predicted variants for the other type are expected
+    # If later relevant, add annotation to the report
+    # if (report["tp_truth"] == 0).all():
+    #     raise ValueError(
+    #         f"The callset {snakemake.wildcards.callset} does not predict any variant from the truth. "
+    #         "This is likely a technical issue in the callset and should be checked before further evaluation."
+    #     )
+
+    report.to_csv(snakemake.output[0], sep="\t", index=False)
+else:
+    pd.DataFrame(
+        {
+            col: []
+            for col in [
+                "coverage",
+                "class",
+                "chromosome	position",
+                "ref_allele",
+                "alt_allele"
+                "true_genotype",
+                "predicted_genotype"
+            ]
+        }
+    ).to_csv(snakemake.output[0], sep="\t")