Open AccessInterCellDB: A User‐Defined Database for Inferring Intercellular Networks
Author(s) -
Jin Ziyang,
Zhang Xiaotao,
Dai Xuejiao,
Huang Jinyan,
Hu Xiaoming,
Zhang Jianmin,
Shi Ligen
Publication year - 2022
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202200045
Subject(s) - decipher , crosstalk , computational biology , intracellular , computer science , annotation , cell , biology , bioinformatics , microbiology and biotechnology , genetics , physics , optics
Recent advances in single cell RNA sequencing (scRNA‐seq) empower insights into cell–cell crosstalk within specific tissues. However, customizable data analysis tools that decipher intercellular communication from gene expression in association with biological functions are lacking. The authors have developed InterCellDB, a platform that allows a user‐defined analysis of intercellular communication using scRNA‐seq datasets in combination with protein annotation information, including cellular localization and functional classification, and protein interaction properties. The application of InterCellDB in tumor microenvironment research is exemplified using two independent scRNA‐seq datasets from human and mouse and it is demonstrated that InterCellDB‐inferred cell–cell interactions and ligand–receptor pairs are experimentally valid.