NK Cell

Download processed single-cell RNA-seq data for natural killer cells

Dataset Information

Cell Type

Natural Killer Cells

Total Cells

93,197 cells

Sample Count

518 samples

Subpopulation Count

15 subpopulations

Download Resources

Raw Data Files

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

10X Raw Data Package

File: NKcell_10x_raw_package.zip

Format: ZIP

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

Download
Zenodo

Barcode File

File: NKcell_barcodes.tsv.gz

Format: TSV.GZ

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

Feature File

File: NKcell_features.tsv.gz

Format: TSV.GZ

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

Matrix File

File: NKcell_matrix.mtx.gz

Format: MTX.GZ

Description: Sparse expression matrix containing gene expression counts.

Metadata File

File: NKcell_metadata.csv.gz

Format: CSV.GZ

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

Marker Genes

Marker lists and published genesets are uploaded in Zenodo：scHNDB-Cell Type-Marker Genes（DOI 10.5281/zenodo.19535753）。

All markers (each subpopulation)

File: NK_cell_all_Markers_of_each_subpopulation.xls

Description: All markers of each subpopulation

Download (China Mirror OSS)

Top10 markers (each subpopulation)

File: NK_cell_top10_Markers_of_each_subpopulation.xls

Description:Top10 marker of each subpopulation

Download (China Mirror OSS)

Published reference gene sets

File: NK_cell_ref_geneset.zip

Description:Published gene sets (zip)

Download (China Mirror OSS)

Key Marker Genes for NK Cells

  • Pan NK markers: NCAM1, NCR1, NKG7
  • CD16+ NK cells: FCGR3A, FCER1G, CX3CR1
  • CD56+ NK cells: NCAM1, CD56, KLRC1
  • Cytotoxic markers: GZMB, GZMA, PRF1
  • Activation markers: IFNG, TNF, CCL5

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')