Network-Based Single-Cell RNA-Seq Data Imputation Enhances Cell Type Identification.

ABSTRACT: Single-cell RNA sequencing is a powerful technology for obtaining transcriptomes at single-cell resolutions. However, it suffers from dropout events (i.e., excess zero counts) since only a small fraction of transcripts get sequenced in each cell during the sequencing process. This inherent sparsity of expression profiles hinders further characterizations at cell/gene-level such as cell type identification and downstream analysis. To alleviate this dropout issue we introduce a network-based method, netImpute, by leveraging the hidden information in gene co-expression networks to recover real signals. netImpute employs Random Walk with Restart (RWR) to adjust the gene expression level in a given cell by borrowing information from its neighbors in a gene co-expression network. Performance eva

SUBMITTER: Zand M 

PROVIDER: S-EPMC7230610 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature