CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data. Bioengineering (Basel) 2025 Jan 03;12(1)
Date
01/24/2025Pubmed ID
39851305Pubmed Central ID
PMC11763284DOI
10.3390/bioengineering12010031Scopus ID
2-s2.0-85216113629 (requires institutional sign-in at Scopus site)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 levels. Our comprehensive evaluations demonstrate that CCI not only reconstructs the original data pattern, but also improves the performance of downstream analyses. CCI outperforms existing methods for data imputation under different scenarios, exhibiting accuracy, robustness, and generalization.
Author List
Juan W, Ahn KW, Chen YG, Lin CWAuthors
Kwang Woo Ahn PhD Director, Professor in the Data Science Institute department at Medical College of WisconsinYi-Guang Chen PhD Professor in the Pediatrics department at Medical College of Wisconsin
Chien-Wei Lin PhD Associate Professor in the Data Science Institute department at Medical College of Wisconsin
