Tumor Tissue

Download processed single-cell and spatial transcriptomics data from primary tumor tissue samples

Dataset Information

Tissue Type

Primary Tumor Tissue

Total Cells

1,167,107

Sample Count

381 samples

Subpopulation Count

132 Subtypes

Raw Data Files

Raw Data Files

Complete single-cell RNA sequencing data in standard 10X Genomics format

10X Raw Data Package

File: Tumor_10x_raw_package.zip

Format: ZIP

Description: Contains barcodes/features/matrix/metadata in standard 10X format

Download
Zenodo

Barcode File

File: Tumor_barcodes.tsv.gz

Format: TSV.GZ

Description: Contains cell barcode information for each cell in the dataset.

Feature File

File: Tumor_features.tsv.gz

Format: TSV.GZ

Description: Gene features and annotations including gene symbols and IDs.

Matrix File

File: Tumor_matrix.mtx.gz

Format: MTX.GZ

Description: Sparse expression matrix containing gene expression counts.

Metadata File

File: Tumor_metadata.csv.gz

Format: CSV.GZ

Description: Cell-level metadata including annotations and sample information.

Marker Genes

Markers of tumor tissues are published in Zenodo：scHNDB-Tissue Type-Marker Genes（DOI 10.5281/zenodo.19535702）

All markers (each subpopulation)

File: Tumor_all_Markers_of_each_subpopulation.xls

Size: 11.3 MB

Description: All markers of each subpopulation

Download (China Mirror OSS)

Top10 markers (each subpopulation)

File: Tumor_top10_Markers_of_each_subpopulation.xls

Size: 83.6 KB

Description: Top10 marker of each subpopulation

Download (China Mirror OSS)

Key Marker Genes for Tumor Tissue

  • Cancer cells: TP53, MYC, EGFR, CCND1
  • Cancer stem cells: CD44, ALDH1A1, SOX2
  • Immune cells: CD3D, CD8A, FOXP3, CD68
  • Stromal cells: ACTA2, COL1A1, FAP
  • Endothelial cells: PECAM1, VWF, CDH5

Usage Instructions

These files are compatible with standard single-cell analysis tools. To load the data:

In R (Seurat):

library(Seurat)
data <- Read10X(data.dir = "path/to/extracted/files")
seurat_obj <- CreateSeuratObject(counts = data)

In Python (Scanpy):

import scanpy as sc
adata = sc.read_10x_mtx('path/to/extracted/files')