A sparse Bayesian factor model for the construction of gene co-expression networks from single-cell RNA sequencing count data

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作者
Michael Sekula
Jeremy Gaskins
Susmita Datta
机构
[1] Department of Bioinformatics and Biostatistics,
[2] University of Louisville,undefined
[3] Department of Biostatistics,undefined
[4] University of Florida,undefined
来源
BMC Bioinformatics | / 21卷
关键词
Co-expression; Latent factor model; Networking; RNA sequencing; Single-cell;
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[1]  
De Smet R(2010)Advantages and limitations of current network inference methods Nat Rev Microbiol 8 717-29
[2]  
Marchal K(1998)Cluster analysis and display of genome-wide expression patterns Proc Natl Acad Sci USA 95 14863-8
[3]  
Eisen MB(2005)Systematic survey reveals general applicability of “guilt-by-association” within gene coexpression networks BMC Bioinformatics 6 227-54
[4]  
Spellman PT(2016)Single-cell co-expression analysis reveals distinct functional modules, co-regulation mechanisms and clinical outcomes PLoS Comput Biol 12 e1004892-98
[5]  
Brown PO(2018)Mapping gene regulatory networks from single-cell omics data Brief Funct Genomics 17 246-21
[6]  
Botstein D(2016)Design and computational analysis of single-cell RNA-sequencing experiments Genome Biol 17 63-4
[7]  
Wolfe CJ(2019)Network modeling of single-cell omics data: Challenges, opportunities, and progresses Emerg Top Life Sci 3 379-17
[8]  
Kohane IS(2018)Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data BMC Bioinformatics 19 1-17
[9]  
Butte AJ(2018)DEsingle for detecting three types of differential expression in single-cell RNA-seq data Bioinformatics 34 3223-11
[10]  
Wang J(2018)A general and flexible method for signal extraction from single-cell RNA-seq data Nat Commun 9 1-42