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Datasets:
autoencodix
/
tcga

Tasks:
Tabular Classification
Feature Extraction
Modalities:
Image
Tags:
tcga
bulk-rna-seq
methylation
mutation
cancer
genomics
License:
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metadata 
license: cc0-1.0
task_categories:
  - tabular-classification
  - feature-extraction
tags:
  - tcga
  - bulk-rna-seq
  - methylation
  - mutation
  - cancer
  - genomics
  - multi-omics
pretty_name: TCGA PanCancer Atlas (8-subtype base set + per-architecture extensions)

TCGA

A multi-omics subset of the TCGA PanCancer Atlas (RNA, methylation, mutation, clinical), for use with AUTOENCODIX tutorials.

Base set (for Vanillix and other Tutorials)

Covers 8 cancer types: BRCA, OV, LUAD, UCEC, LUSC, COAD, READ, UCS.

  • rna.parquet — 3552 samples × 17448 genes (RNA expression)
  • methylation.parquet — 3875 samples × 9829 genes (per-gene methylation)
  • mutation.parquet — 3461 samples × 20304 genes (combined mutation/CNA score per gene; restricted to samples that belong to the 8 cancer types above — the source file is a pan-cancer TCGA mutation matrix, trimmed here to match this cohort)
  • clinical.parquet — 3902 samples × 56 columns (sample-level clinical/annotation metadata, incl. CANCER_TYPE_ACRONYM, SUBTYPE)

Gene identifiers are NCBI/Entrez gene IDs throughout, consistent with the ontology files below.

ontology/ — extension for Ontix Tutorial

Two ontology hierarchies, each as a 2-level gene-grouping file (gene ID → level-1 group, level-1 group → level-2 group):

  • chromosome_ont_lvl1_ncbi.txt, chromosome_ont_lvl2.txt — chromosome/cytoband-based grouping
  • reactome_ont_lvl1.txt, reactome_ont_lvl2.txt — Reactome pathway-based grouping (level 2 uses human-readable Reactome pathway names)

xmodalix/ — extension for XModalix Tutorial

Synthetic MNIST-style images paired to TCGA samples, one per sample, for the image↔omics translation tutorial:

  • xmodalix/tcga_fake_images.zip — 3907 grayscale 28×28 PNGs
  • xmodalix/tcga_image_mappings.txt — tab-separated sample_ids, img_paths, extra_class_labels, CANCER_TYPE_ACRONYM; 3230 of the 3902 base-set clinical samples have a paired image (one image label per cancer type)

Loading 

from huggingface_hub import hf_hub_download
import pandas as pd

rna_path = hf_hub_download(repo_id="autoencodix/tcga", repo_type="dataset", filename="rna.parquet")
meth_path = hf_hub_download(repo_id="autoencodix/tcga", repo_type="dataset", filename="methylation.parquet")
mut_path = hf_hub_download(repo_id="autoencodix/tcga", repo_type="dataset", filename="mutation.parquet")
clin_path = hf_hub_download(repo_id="autoencodix/tcga", repo_type="dataset", filename="clinical.parquet")

rna = pd.read_parquet(rna_path)

Ontix additionally needs, e.g.:

chr1 = hf_hub_download(repo_id="autoencodix/tcga", repo_type="dataset", filename="ontology/chromosome_ont_lvl1_ncbi.txt")
rea1 = hf_hub_download(repo_id="autoencodix/tcga", repo_type="dataset", filename="ontology/reactome_ont_lvl1.txt")

XModalix additionally needs:

images_zip = hf_hub_download(repo_id="autoencodix/tcga", repo_type="dataset", filename="xmodalix/tcga_fake_images.zip")
mapping = hf_hub_download(repo_id="autoencodix/tcga", repo_type="dataset", filename="xmodalix/tcga_image_mappings.txt")

License

CC0 1.0 (Public Domain Dedication). The TCGA PanCancer Atlas open-access data (RNA, methylation, mutation, clinical) is designated public domain by the NIH Genomic Data Commons, with no restrictions on reuse. The Reactome pathway groupings in ontology/ are separately licensed under CC0 by Reactome. The synthetic xmodalix/ images are generated by this project and released under the same CC0 terms.