Regulatory network-based imputation of dropouts in single-cell RNA sequencing data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values ('dropout imputation'). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Further, it is unknown if all genes equally benefit from imputation or which imputation method works best for a given gene. Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art me

SUBMITTER: Leote AC 

PROVIDER: S-EPMC8890719 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature