Home | SingleCellBase

Overview

Welcome to SingleCellBase. The SingleCellBase is a database for the scientific community interested in exploration of single cell RNA sequencing experiments from multiple species. We collect and integrate data from multiple studies and present them through a unified framework. At present, SingleCellBase contains 9,158 cell-marker entries spanning a total of 1,221 cell types and linking with 8,740 genes (cell markers), covering 464 diseases/status, and 165 tissues across 31 species.

Quick Search

Users can query the database through inputting tissue, cell or cell marker (gene):

Visualize

We developed a tool for visualizing single-cell RNA-seq data that enables researchers to investigate the expression patterns of cell markers in established cell annotation data. Feel free to try it.

Features

Plentiful species up to 31. All the species include both plant and animals like Homo sapiens, Mouse, Danio rerio, Chicken, Monkey, Arabidopsis, etc.
Accurate entries up to 9,158. Both the reliable literature resources of 10X Genomics websites and the rigorous examinations of double-checking following manually curation to ensure the accuracy and credibility of each record.
Users friendly interfaces design: Browse, Search, Download, Submit, Help and Contact Us.

News

^New This article is published online in the Biomarker Research journal (September 20, 2023)

Visualization tool was developed (May 3, 2023)

Website started to build (March 3, 2022)

Statistics

31 Species, including Homo sapiens, Mouse, Danio rerio, Chicken, Rhesus monkeys, etc.
9,158 entries, including class, species, cell marker, tissue type, disease type, cell type and source.
1,221 cell types, including Microglia, T cell, etc.
464 diseases/status, including Glioma, Melanoma, Prostate cancer, etc.
165 tissues, including Brain, Blood, etc.
8,740 genes, including CD133, CD274, TIGIT, etc.

Contact Us

Yonghong Ren, MA.Sc, CapitalBio Corporation, CapitalBio Technology Inc.
Fanlin Meng, Ph.D., CapitalBio Corporation, CapitalBio Technology Inc.

Citation

Fan-Lin Meng#, Xiao-Ling Huang#, Wen-Yan Qin, Kun-Bang Liu, Yan Wang, Ming Li, Yong-Hong Ren*, Yan-Ze Li* & Yi-Min Sun*. singleCellBase: a high-quality manually curated database of cell markers for single cell annotation across multiple species. Biomarker Research (2023) 11:83.10.1186/s40364-023-00523-3

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