fix: filter non-productive TCRs, dynamic gene family columns, single-…#82
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fix: filter non-productive TCRs, dynamic gene family columns, single-…#82KevinMLanderos wants to merge 1 commit into
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…cell quality filters
Three bugs were fixed:
1. bin/sample_calc.py — Gene family CSVs (v_family, d_family, j_family) were
built with hard-coded maximum indices (TRBV: 30, TRBD: 2, TRBJ: 2), silently
dropping any gene with a number above those limits. The max index is now
derived dynamically from genes observed in each sample. Samples with no valid
calls for a gene type write a sample-only row instead of crashing.
2. modules/local/annotate/main.nf — ANNOTATE_PROCESS did not filter for
productive TCRs, so non-productive rearrangements propagated into every
downstream file: concatenated_cdr3_sorted.tsv, OLGA pgen calculation,
TCRSHARING, and the patient workflow (GIANA, GLIPH2, overlap metrics).
The process now reads the 'productive' column when present and retains only
productive entries before writing per-sample _cdr3.tsv files, fixing all
downstream analyses in one place.
3. bin/pseudobulk.py — Cell Ranger AIRR output was not filtered for cell or
contig quality before pseudobulking. is_cell, high_confidence, and productive
filters are now applied in both pseudobulk() and pseudobulk_phenotype() when
those columns are present, ensuring background barcodes, low-confidence
assemblies, and non-productive contigs are excluded from single-cell input.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Unit Test Results10 tests 2 ✅ 21s ⏱️ For more details on these failures, see this check. Results for commit 389a328. |
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Three bugs were fixed:
bin/sample_calc.py — Gene family CSVs (v_family, d_family, j_family) were built with hard-coded maximum indices (TRBV: 30, TRBD: 2, TRBJ: 2), silently dropping any gene with a number above those limits. The max index is now derived dynamically from genes observed in each sample. Samples with no valid calls for a gene type write a sample-only row instead of crashing.
modules/local/annotate/main.nf — ANNOTATE_PROCESS did not filter for productive TCRs, so non-productive rearrangements propagated into every downstream file: concatenated_cdr3_sorted.tsv, OLGA pgen calculation, TCRSHARING, and the patient workflow (GIANA, GLIPH2, overlap metrics). The process now reads the 'productive' column when present and retains only productive entries before writing per-sample _cdr3.tsv files, fixing all downstream analyses in one place.
bin/pseudobulk.py — Cell Ranger AIRR output was not filtered for cell or contig quality before pseudobulking. is_cell, high_confidence, and productive filters are now applied in both pseudobulk() and pseudobulk_phenotype() when those columns are present, ensuring background barcodes, low-confidence assemblies, and non-productive contigs are excluded from single-cell input.