CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technique in molecular biology and genomics, revealing the cellular heterogeneity. However, scRNA-seq data often suffer from dropout events, meaning that certain genes exhibit very low or even zero expression levels due to technical limitations. Existing imputation methods for dropout events lack comprehensive evaluations in downstream analyses and do not demonstrate robustness across various scenarios. In response to this challenge, we propose a consensus clustering-based imputation (CCI) method. CCI performs clustering on each subset of data sampling across genes and summarizes clustering outcomes to define cellular similarities. CCI leverages the information from similar cells and employs the similarities to impute gene expression

SUBMITTER: Juan W 

PROVIDER: S-EPMC11763284 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature