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• W3089711218 abstract "Abstract A main challenge in analyzing single-cell RNA sequencing (scRNA-seq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called ‘dropout’), the gene expression matrix has a substantial amount of zero read counts. Existing imputation methods treat either each cell or each gene as independently and identically distributed, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs (SIngle-cell RNA-seq iMPutation and celL clustErings), which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering via multiple imputations. In simulations, SIMPLEs performed significantly better than prevailing scRNA-seq imputation methods according to various metrics. By applying SIMPLEs to several real datasets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes." @default.
• W3089711218 created "2020-10-08" @default.
• W3089711218 creator A5031970710 @default.
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• W3089711218 date "2020-09-28" @default.
• W3089711218 modified "2023-10-06" @default.
• W3089711218 title "SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation" @default.
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• W3089711218 doi "https://doi.org/10.1093/nargab/lqaa077" @default.
• W3089711218 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7526005" @default.
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