Datasets:

isalgo
/

airr_tcga

Artifact	Origin	Format	Provenance
`samples.tar.gz`	TCGA tumour bulk RNA-seq → AIRR clonotypes (RNA-seq receptor profiling, Bolotin 2017)	tar.gz of per-sample AIRR TSVs	experimental (RNA-derived receptor rearrangements)
`metadata.tsv`	join of the source tables below, restricted to the 9,591 AIRR samples	TSV, keyed by `sample_id`	derived
`total_reads` (col)	GDC `rna_seq.genomic.gdc_realn.bam` `Total_Reads`	integer	experimental (raw library read total, all reads)
`aligned_reads` (col)	GDC realigned-BAM `Aligned_Reads`	integer	experimental (genome-aligned subset)
`metadata.hla.tsv`	HLA class-I: TCGA PanImmune (primary) ∪ OptiType-2017 fill, one row per donor	TSV, keyed by `subject_id`, `hla_source` col	derived (from experimental OptiType-based calls)

Upstream sources (internal staging, 2025)

Source	Path	Role
AIRR clonotypes	`airr/tcga_formatted_airr.tsv.gz`	the receptor tables (split per `sample_id` into `samples.tar.gz`)
Sample metadata	`meta/tcga_meta_slim.tsv`	`sample_id`, demographics, `total_reads` (backbone; covers 100% of AIRR samples)
Clinical annotation	`meta/tcga_annotation_standardized.tsv`	disease/cancer_type/stage/therapy/response/OS/PFS/AE (covers 9,104 / 9,591)
Read counts	`meta/readcount/tcga_read_counts.tsv`	per-BAM `Total_Reads` / `Aligned_Reads`, keyed by aliquot UUID
GDC sample sheets	`meta/readcount/tcga_*_sample_sheet.tsv`	UUID → TCGA barcode (`Case ID`, `Sample ID`)
HLA class-I (primary)	TCGA PanCanAtlas PanImmune, `tcga_hla_alleles.tsv`	TCGA PanImmune A/B/C calls, donor-level (`patientBarcode`) — 8,507 donors
HLA class-I (fill)	TCGA PanCanAtlas OptiType, `OptiTypeCallsHLA_20171207.tsv`	OptiType A/B/C per aliquot (`aliquot_id`); collapsed to donor, used only where PanImmune lacks the donor

TCGA Research Network — https://www.cancer.gov/tcga; data via the GDC (https://portal.gdc.cancer.gov).
RNA-seq AIRR-profiling method: Bolotin DA et al., Nat Biotechnol 2017;35(10):908–911, DOI 10.1038/nbt.3979, PMID 29020005.
HLA typing: TCGA PanImmune — Thorsson V et al., Immunity 2018;48(4):812–830.e14, DOI 10.1016/j.immuni.2018.03.023, PMID 29628290; via https://gdc.cancer.gov/about-data/publications/panimmune. OptiType — Szolek A et al., Bioinformatics 2014;30(23):3310–3316, DOI 10.1093/bioinformatics/btu548, PMID 25143287.

sample_id (TCGA-XX-XXXX.N) is the single key; all 9,591 AIRR samples are present in tcga_meta_slim (0 missing), no duplicate ids.
Read-count semantics verified: tcga_meta_slim.total_reads == GDC realigned-BAM Total_Reads on matched samples (median ratio 1.000, 84% exact); clearly ≠ Aligned_Reads (9% match). So total_reads is the total raw FASTQ read count, not mapped reads.
Read-count gap fill: 556 AIRR samples had blank total_reads in tcga_meta_slim; 514 single-BAM cases were filled unambiguously from the GDC read-count table (UUID→barcode via the sample sheets), leaving 42 (0.4%) without a matchable BAM.
Regeneration: samples.tar.gz = per-sample_id split of the AIRR table (already sample-contiguous, no sort needed); metadata.tsv = tcga_meta_slim ⟕ annotation extras ⟕ aligned_reads, filtered to AIRR samples.
HLA (metadata.hla.tsv): key subject_id (TCGA-XX-XXXX). Union of two donor-level OptiType-based call sets to maximise coverage — PanImmune primary (hla_source=panimmune, 7,649 donors, from tcga_hla_alleles.tsv patientBarcode) plus OptiType-2017 fill (hla_source=optitype, 1,474 donors, from OptiTypeCallsHLA_20171207.tsv collapsed per donor = first 3 barcode fields of aliquot_id). Alleles HLA-A*02:01 (4-digit). 9,123 / 9,450 airr_tcga donors carry HLA (96.5%) → 9,263 / 9,591 samples; 187 donors in neither set. (PanImmune alone covered 80.9%, OptiType alone 87.0%, union 96.5%.)