ScTsI: an effective two-stage imputation method for single-cell RNA-seq data.

ABSTRACT: Single-cell RNA-seq facilitates the understanding of cell types and states and the revealing of the cellular heterogeneity in developmental processes and disease mechanisms. However, the dropout events in single-cell RNA-seq data, in which genes are not detected due to technical noise or limited sequencing depth, seriously affect downstream analyses. Imputation is an effective way to relieve the impact of dropout events. However, the current methods may introduce new noise or modify the high expression values in the imputation process and their performance may be lower than expected when dealing with data with a high dropout rate, facing with different types of data, and aiming at various downstream analyses. We propose a two-stage imputation algorithm, scTsI, for single-cell RNA-seq data.

SUBMITTER: Zhang H 

PROVIDER: S-EPMC12204820 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature