Endothelial Cell

Download processed single-cell RNA-seq data for vascular endothelial cells

Dataset Information

Cell Type

Endothelial Cells

Total Cells

84,057 cells

Sample Count

344 samples

Subpopulation Count

11 subpopulations

Download Resources

Raw Data Files

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

10X Raw Data Package

File: Endothelial_10x_raw_package.zip

Format: ZIP

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

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Zenodo

Barcode File

File: Endothelial_barcodes.tsv.gz

Format: TSV.GZ

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

Feature File

File: Endothelial_features.tsv.gz

Format: TSV.GZ

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

Matrix File

File: Endothelial_matrix.mtx.gz

Format: MTX.GZ

Description: Sparse expression matrix containing gene expression counts.

Metadata File

File: Endothelial_metadata.csv.gz

Format: CSV.GZ

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

Marker Genes

Published Gene set：scHNDB-Cell Type-Marker Genes（DOI 10.5281/zenodo.19535753）。

All markers (each subpopulation)

File: Endothelial_cell_all_Markers_of_each_subpopulation.xls

Description: All marker of each subpopulation

Download (China Mirror OSS)

Top10 markers (each subpopulation)

File: Endothelial_cell_top10_Markers_of_each_subpopulation.xls

Description: Top10 marker of each subpopulation

Download (China Mirror OSS)

Published reference gene sets

File: Endothelial_cell_ref_geneset.zip

Description: Published gene set (zip)

Download (China Mirror OSS)

Key Marker Genes for Endothelial Cells

  • Pan endothelial markers: PECAM1, VWF, CDH5
  • Arterial endothelial cells: GJA4, EFNB2, DLL4
  • Venous endothelial cells: NR2F2, ACKR1, APLNR
  • Lymphatic endothelial cells: PROX1, LYVE1, FLT4
  • Activated endothelium: ICAM1, VCAM1, SELE

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')