SmartImpute: A Targeted Imputation Framework for Single-cell Transcriptome Data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and tissue transcriptomic complexity. However, the high frequency of dropout events in scRNA-seq data complicates downstream analyses such as cell type identification and trajectory inference. Existing imputation methods address the dropout problem but face limitations such as high computational cost and risk of over-imputation. We present SmartImpute, a novel computational framework designed for targeted imputation of scRNA-seq data. SmartImpute focuses on a predefined set of marker genes, enhancing the biological relevance and computational efficiency of the imputation process while minimizing the risk of model misspecification. Utilizing a modified Generative Adversarial Imputation Netw

SUBMITTER: Yao S 

PROVIDER: S-EPMC11275709 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature