CaSee: A lightning transfer-learning model directly used to discriminate cancer/normal cells from scRNA-seq
CaSee: A lightning transfer-learning model directly used to discriminate cancer/normal cells from scRNA-seq
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CaSee：直接用于从 scRNA-seq 中区分癌症/正常细胞的闪电迁移学习模型
DOI：
10.1101/2022.02.10.480003
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发表时间：
2022-02
期刊：
影响因子：
8
通讯作者：
Zhiyuan Hu
中科院分区：
文献类型：
--
作者：
Yuan Sh;Xiuli Zhang;Zhimin Yang;Jierong Dong;Yuanzhuo Wang;Ying Zhou;Xuejie Li;Caixia Guo;Zhiyuan Hu
Single-cell RNA sequencing (scRNA-seq) is one of the most efficient technologies for human tumor research. However, data analysis is still faced with some technical challenges, especially the difficulty in efficiently and accurately discriminate cancer/normal cells in the scRNA-seq expression matrix. In this study, we developed a cancer/normal cell discrimination pipeline called pan-cancer seeker (CaSee) devoted to scRNA-seq expression matrix, which is based on the traditional high-quality pan-cancer bulk sequencing data using transfer learning. It is compatible with mainstream sequencings technology platforms, 10x Genomics Chromium, Smart-seq2, and Microwell-seq. Here, CaSee pipeline exhibited excellent performance in the multicenter data evaluation of 11 retrospective cohorts and one independent dataset, with an average discrimination accuracy of 96.69%. In general, the development of a deep-learning based, pan-cancer cell discrimination model, CaSee, to distinguish cancer cells from normal cells will be compelling to researchers working in the genomics, cancer, and single-cell fields.
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影响因子：
64.8
作者：
通讯作者：
--
CancerSEA：癌症单细胞状态图谱
影响因子：
14.9
作者：
Yuan H;Yan M;Zhang G;Liu W;Deng C;Liao G;Xu L;Luo T;Yan H;Long Z;Shi A;Zhao T;Xiao Y;Li X
通讯作者：
Li X
DOI：
--
发表时间：
2018-01
期刊：
ArXiv
影响因子：
--
作者：
Kai Qiao;Chi Zhang;Linyuan Wang;Bin Yan;Jian Chen-;Lei Zeng;Li Tong
通讯作者：
Kai Qiao;Chi Zhang;Linyuan Wang;Bin Yan;Jian Chen-;Lei Zeng;Li Tong
影响因子：
50.3
作者：
Taylor AM;Shih J;Ha G;Gao GF;Zhang X;Berger AC;Schumacher SE;Wang C;Hu H;Liu J;Lazar AJ;Cancer Genome Atlas Research Network;Cherniack AD;Beroukhim R;Meyerson M
通讯作者：
Meyerson M
影响因子：
46.9
作者：
Gao R;Bai S;Henderson YC;Lin Y;Schalck A;Yan Y;Kumar T;Hu M;Sei E;Davis A;Wang F;Shaitelman SF;Wang JR;Chen K;Moulder S;Lai SY;Navin NE
通讯作者：
Navin NE