CancerSEA: a cancer single-cell state atlas. Issue Volume 47:Issue D1(2019) (17th October 2018)

• Record Type: Journal Article
• Authors: Yuan, Huating; Yan, Min; Zhang, Guanxiong; Liu, Wei; Deng, Chunyu; Liao, Gaoming; Xu, Liwen; Luo, Tao; Yan, Haoteng; Long, Zhilin; Shi, Aiai; Zhao, Tingting; Xiao, Yun; Li, Xia
• Is Part Of: Nucleic acids research. Volume 47:Issue D1(2019)
• Page Start: D900
• Page End: D908
• Publication Date: 2018-10-17
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gky939
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: High functional heterogeneity of cancer cells poses a major challenge for cancer research. Single-cell sequencing technology provides an unprecedented opportunity to decipher diverse functional states of cancer cells at single-cell resolution, and cancer scRNA-seq datasets have been largely accumulated. This emphasizes the urgent need to build a dedicated resource to decode the functional states of cancer single cells. Here, we developed CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/ or http://202.97.205.69/CancerSEA/ ), the first dedicated database that aims to comprehensively explore distinct functional states of cancer cells at the single-cell level. CancerSEA portrays a cancer single-cell functional state atlas, involving 14 functional states (including stemness, invasion, metastasis, proliferation, EMT, angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation and quiescence) of 41 900 cancer single cells from 25 cancer types. It allows querying which functional states are associated with the gene (or gene list) of interest in different cancers. CancerSEA also provides functional state-associated PCG/lncRNA repertoires across all cancers, in specific cancers, and in individual cancer single-cell datasets. In summary, CancerSEA provides a user-friendly interface for comprehensively searching, browsing, visualizing and downloading functional state activity profiles of tens of thousands of cancer single cells and the corresponding PCGs/lncRNAs expression profiles.