row groups + page index for HF Data Studio; gdc_portal_url; strict pydantic models; deduplicated dataset card

README.md

@@ -25,25 +25,23 @@ configs:
 
 # TCGA Patients (Open Access)
 
-**Open-access** TCGA patient data from the NCI Genomic Data Commons (GDC).
-One HuggingFace subset per TCGA project; one row per patient.
+Open-access patient data from The Cancer Genome Atlas (TCGA), pulled from the
+National Cancer Institute (NCI) Genomic Data Commons (GDC). One HuggingFace
+(HF) subset per TCGA project; one row per patient.
 
 - **Projects included:** `TCGA-CHOL`, `TCGA-DLBC`, `TCGA-LUAD`
-- **Generated:** 2026-05-04 05:19:32 UTC
-- **Source:** NCI GDC `/cases` endpoint, open-access tier only.
+- **Generated:** 2026-05-04 23:16:21 UTC
+- **Source:** GDC `/cases` endpoint, open-access tier only.
 - **Schema:** derived from the [GDC Data Dictionary][gdc-dict]; the live
-  dictionary snapshot the GDC was serving when the data was fetched is
-  hashed into each project's `gdc_status.json` (and stored alongside the raw
-  data on the producer side, not shipped with the parquet).
+  dictionary the GDC was serving when the data was fetched is hashed into
+  each project's `gdc_status.json` for provenance.
 - **GDC data release:** Data Release 45.0 - December 04, 2025
 
-## GDC data model primer
+## Data model
 
-A short orientation for downstream users. The GDC organizes everything around a
-**case** (a single patient) with a hierarchy of physical biospecimens beneath
-it, plus assay-derived data files attached to those biospecimens.
-
-**Biospecimen hierarchy** (per GDC's [data dictionary][gdc-dict]):
+Closely follows the GDC data model — see the [GDC Data Dictionary][gdc-dict]
+for the canonical entity-by-entity definitions. Each row is one `case` (one
+patient) with the full biospecimen subtree:
 
 ```
 case          one patient (TCGA-XX-1234)
@@ -54,36 +52,39 @@ case          one patient (TCGA-XX-1234)
             └── aliquot    a vial of that analyte handed off for sequencing
 ```
 
-We preserve this hierarchy verbatim — each `samples[i]` carries
-`portions[j].analytes[k].aliquots[m]` exactly as the GDC returns it. No
-flattening, no field hoisting; portion- and analyte-level fields like
-`is_ffpe`, `a260_a280_ratio`, and `normal_tumor_genotype_snp_match` are
-preserved. The `TcgaHfPatient` reference implementation (below) provides
-flat-aliquot convenience views over this tree.
-
-**Top-level molecular columns** are named `samples_<gdc_data_type_snake_case>`
-(e.g. `samples_masked_somatic_mutation`, `samples_gene_expression_quantification`).
-Each entry carries FK fields back to the patient's `samples[].portions[].analytes[].aliquots[]`
-so cross-modality joins are local to the row.
-
-**Timeline anchor is uniform per the GDC dictionary.** Every `days_to_*` field
-is documented as days from the case's `index_date` (a top-level field on each
-row; for TCGA usually `"Diagnosis"`). `TcgaHfPatient.timeline()` returns every
-dated event for the patient on this single anchor — clinical (consent →
-diagnosis → treatments → follow-ups → lost-to-follow-up → death) plus
-biospecimen (`sample_procurement` from `days_to_sample_procurement`,
-`bcr_receipt` from `days_to_collection`).
-
-For some TCGA cases `days_to_collection` exceeds `days_to_death`. We don't
-attempt to reinterpret these — we surface the count via
-`TcgaHfPatient.consistency_check()` as `bcr_receipts_after_death` and leave
-interpretation to the consumer.
-
-**Reference Python implementation:** the `tcga2hf` package on GitHub ships a
-fully-typed pydantic `TcgaHfPatient` model that mirrors this schema and adds
-convenience joins (tumor/normal pairs, mutations-by-gene, expression-by-gene,
-longitudinal timeline). Useful both as a loader and as documentation for what
-the data means.
+Every `days_to_*` field anchors to the case's `index_date` (TCGA: almost
+always `"Diagnosis"`), per the dictionary, so clinical and biospecimen events
+share a single timeline.
+
+### Where this dataset deviates from the GDC
+
+The few places this row layout differs from a direct mapping of the GDC `case`
+tree:
+
+- **Top-level convenience columns.** Each row carries `gdc_portal_url`
+  (templated link to the patient's GDC Data Portal page) and
+  `samples_<gdc_data_type_snake_case>` molecular vectors (e.g.
+  `samples_masked_somatic_mutation`,
+  `samples_gene_expression_quantification`). These let consumers
+  column-project just the modalities they need; each entry carries foreign
+  keys (FKs) back to `samples[].portions[].analytes[].aliquots[]`.
+- **Resolved sample FKs on Mutation Annotation Format (MAF) rows.** The GDC
+  ships MAF variants with aliquot UUIDs in `Tumor_Sample_UUID` /
+  `Matched_Norm_Sample_UUID`; we additionally resolve those to
+  `tumor_sample_id` / `matched_normal_sample_id` so consumers can join
+  straight to `samples[]`.
+- **Lifted expression QC counts.** Each Gene Expression Quantification
+  record has the STAR per-feature quality-control counts `N_unmapped`,
+  `N_multimapping`, `N_noFeature`, `N_ambiguous` lifted from the source
+  Tab-Separated Values (TSV) file onto the row as scalar fields. The
+  `stranded_first` / `stranded_second` columns are dropped — TCGA used
+  unstranded library prep, so they're zero or unused.
+
+## Loading
+
+The [`tcga2hf` package][repo] ships a typed `TcgaHfPatient` pydantic model
+that mirrors this schema and adds convenience joins (tumor/normal pairs,
+mutations-by-gene, expression-by-gene, longitudinal timeline).
 
 ```python
 import pyarrow.parquet as pq
@@ -93,7 +94,8 @@ t = pq.read_table("TCGA-CHOL/train.parquet")
 patients = [TcgaHfPatient.model_validate(r) for r in t.to_pylist()]
 ```
 
-**Authoritative GDC references:**
+## GDC references
+
 - [Data dictionary][gdc-dict] (every entity + field definition)
 - [Biospecimen Encyclopedia](https://docs.gdc.cancer.gov/Encyclopedia/pages/Biospecimen/)
 - [MAF format spec](https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/)
@@ -101,8 +103,6 @@ patients = [TcgaHfPatient.model_validate(r) for r in t.to_pylist()]
 - [Sample Type codes](https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/sample-type-codes)
 - [TCGA Barcode reference](https://docs.gdc.cancer.gov/Encyclopedia/pages/TCGA_Barcode/)
 
-[gdc-dict]: https://docs.gdc.cancer.gov/Data_Dictionary/
-
 ## License & redistribution
 
 Per the [NCI GDC Data Analysis Policy](https://gdc.cancer.gov/analyze-data/data-analysis-policies):
@@ -143,9 +143,9 @@ Suggested citations:
 - The Cancer Genome Atlas Research Network. https://www.cancer.gov/tcga
 - NCI Genomic Data Commons. https://gdc.cancer.gov
 
-References:
+Policy references:
 [GDC Policies](https://gdc.cancer.gov/about-gdc/gdc-policies),
-[GDC Encyclopedia — Controlled Access][controlled] (defines what is *not* here),
+[GDC Encyclopedia — Controlled Access][controlled] (defines what is *not* in this dataset),
 [NIH Genomic Data Sharing Policy](https://sharing.nih.gov/genomic-data-sharing).
 
 [controlled]: https://docs.gdc.cancer.gov/Encyclopedia/pages/Controlled_Access/
@@ -154,3 +154,6 @@ References:
 
 Prototype dataset. Schema, included projects, and column coverage are still
 evolving. Re-derive from the GDC for any analysis where freshness matters.
+
+[gdc-dict]: https://docs.gdc.cancer.gov/Data_Dictionary/
+[repo]: https://github.com/galtay/tcga2hf

TCGA-CHOL/train.parquet

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TCGA-DLBC/train.parquet

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TCGA-LUAD/train.parquet

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