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• W2898889075 abstract "Abstract Motivation Single cell RNA-sequencing (scRNA-seq) technology enables whole transcriptome profiling at single cell resolution and holds great promises in many biological and medical applications. Nevertheless, scRNA-seq often fails to capture expressed genes, leading to the prominent dropout problem. These dropouts cause many problems in down-stream analysis, such as significant noise increase, power loss in differential expression analysis and obscuring of gene-to-gene or cell-to-cell relationship. Imputation of these dropout values thus becomes an essential step in scRNA-seq data analysis. Results In this paper, we model the dropout imputation problem as robust matrix decomposition. This model has minimal assumptions and allows us to develop a computational efficient imputation method scRMD. Extensive data analysis shows that scRMD can accurately recover the dropout values and help to improve downstream analysis such as differential expression analysis and clustering analysis. Contact ruibinxi@math.pku.edu.cn" @default.
• W2898889075 created "2018-11-09" @default.
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• W2898889075 date "2018-11-04" @default.
• W2898889075 modified "2023-10-17" @default.
• W2898889075 title "scRMD: Imputation for single cell RNA-seq data via robust matrix decomposition" @default.
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• W2898889075 doi "https://doi.org/10.1101/459404" @default.
• W2898889075 hasPublicationYear "2018" @default.
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