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Cited by in F6Publishing
For: Wang Z, Ding H, Zou Q. Identifying cell types to interpret scRNA-seq data: how, why and more possibilities. Briefings in Functional Genomics 2020;19:286-91. [DOI: 10.1093/bfgp/elaa003] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
1 Ye X, Zhang W, Futamura Y, Sakurai T. Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning. Cells 2020;9:E1938. [PMID: 32825786 DOI: 10.3390/cells9091938] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
2 Wang Y, Tian X, Ai D. Cell Heterogeneity Analysis in Single-Cell RNA-seq Data Using Mixture Exponential Graph and Markov Random Field Model. Biomed Res Int 2021;2021:9919080. [PMID: 34095314 DOI: 10.1155/2021/9919080] [Reference Citation Analysis]
3 Zhang Z, Cui F, Wang C, Zhao L, Zou Q. Goals and approaches for each processing step for single-cell RNA sequencing data. Brief Bioinform 2021;22:bbaa314. [PMID: 33316046 DOI: 10.1093/bib/bbaa314] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
4 Pasquini G, Rojo Arias JE, Schäfer P, Busskamp V. Automated methods for cell type annotation on scRNA-seq data. Comput Struct Biotechnol J 2021;19:961-9. [PMID: 33613863 DOI: 10.1016/j.csbj.2021.01.015] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]