Normal Tissue
Download processed single-cell and spatial transcriptomics data from adjacent normal tissue samples
Dataset Information
Tissue Type
Adjacent Normal Tissue
Total Cells
276,553
Sample Count
76 samples
Subpopulation Count
120 Subtypes
Raw Data Files
Raw Data Files
Complete single-cell RNA sequencing data in standard 10X Genomics format
10X Raw Data Package
File: Normal_10x_raw_package.zip
Format: ZIP
Description: Contains barcodes/features/matrix/metadata in standard 10X format
Download
Barcode File
File: Normal_barcodes.tsv.gz
Format: TSV.GZ
Description: Contains cell barcode information for each cell in the dataset.
Feature File
File: Normal_features.tsv.gz
Format: TSV.GZ
Description: Gene features and annotations including gene symbols and IDs.
Matrix File
File: Normal_matrix.mtx.gz
Format: MTX.GZ
Description: Sparse expression matrix containing gene expression counts.
Metadata File
File: Normal_metadata.csv.gz
Format: CSV.GZ
Description: Cell-level metadata including annotations and sample information.
Marker Genes
Markers and published genesets of adjacent normal tissue are uploaded in Zenodo：scHNDB-Tissue Type-Marker Genes（DOI 10.5281/zenodo.19535702）
All markers (each subpopulation)
File: Normal_marker_all_Markers_of_each_subpopulation.xls
Description: All marker of each subpopulation
Download (China Mirror OSS)Top10 markers (each subpopulation)
File: Normal_top10_Markers_of_each_subpopulation.xls
Description: Top10 marker of each subpopulation
Download (China Mirror OSS)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')