A curated, continuously-harvested collection of publicly available human multi-omics datasets spanning the full tumor trajectory: precancerous lesions → early carcinoma → advanced / metastatic. Single-cell and spatial transcriptomics are prioritized.

⚠️ Provenance & licensing. Every dataset here was downloaded from a public, open-access repository (no controlled-access or patient-identifiable data). Each dataset retains the license and terms of its ORIGINAL source. This repository is a research convenience mirror. If you use any dataset, cite the original study and follow its license — see the per-dataset manifest (_catalog/) for source, accession, URL, PMID/DOI.

Contents (auto-updated 2026-06-22 15:20 UTC)

Datasets: 3931 | Total size: 706.6 GB
Single-cell: 596 | Spatial: 173 | Progression-series: 1131
By source: GEO 3507 · figshare 314 · cBioPortal 95 · CELLxGENE 93 · ArrayExpress 54 · SCEA 4 · UCSC-CellBrowser 1

By modality

other/unspecified: 1568
bulk-RNA-seq: 671
scRNA-seq: 544
methylation: 441
microarray: 237
DNA-mutation: 196
spatial-transcriptomics: 156
proteomics-MS: 117
bulk-ATAC: 69
snRNA-seq: 44
spatial-proteomics: 17
scATAC: 8

Repository structure

data/<SOURCE>/<ACCESSION>/ — processed / analysis-ready files per dataset (e.g. .h5ad, matrix.mtx+barcodes/features, Visium spatial matrices, count tables).
_catalog/precancer_catalog.csv / .xlsx / .parquet — full manifest, one row per dataset: source, accession, modality, progression stage, size, source URL, PMID/DOI, summary.
_catalog/README.md — human-readable running summary.

The _catalog manifest is the authoritative per-dataset metadata index.

Sources

NCBI GEO · CZ CELLxGENE Discover · EBI Single Cell Expression Atlas · UCSC Cell Browser · HCA · figshare · Zenodo · cBioPortal · PRIDE · ArrayExpress/BioStudies. Each dataset's license is that of its source repository / original publication.

How to use

from huggingface_hub import snapshot_download
p = snapshot_download("wei82/precancer-omics-data", repo_type="dataset",
                      allow_patterns=["_catalog/*", "data/CELLxGENE/*"])
# then load e.g. an .h5ad with scanpy / anndata, or matrix.mtx with scanpy.read_10x_mtx

Analysis outputs derived from these data are kept private at wei82/precancer-omics-analysis (each result references the source dataset uid = source:accession).

Disclaimer

Automatically harvested; not affiliated with the original data providers. Provided for research convenience. Verify each dataset's original license before reuse or redistribution.

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Precancer → Tumor → Late/Metastatic Progression Multi-omics