A Hybrid Approach for Enhancing Single-Cell Type Clustering

Recent developments in single-cell RNA sequencing (scRNA-seq) technology have made it possible to conduct a thorough analysis of the ways in which cells express their transcriptomes in response to various physiological states and cues from the outside world. "Dropout," the phenomenon in which a gene is observed at low or moderate levels in one type of cell but not in another, poses a major analytical challenge when analyzing scRNA-seq data. Taking on the problem of dropout events in gene expression datasets is the main goal of this work. Our proposed approach consists of two main steps: first, we apply the consensus clustering-based algorithm, ccImpute, to impute dropout events, and then we utilize scCLUE, an ensemble feature selection and network similarity measurement-based single-cell clustering method, to cluster single-cell types. Our experimental results with four publicly available datasets show that the proposed approach outperforms the original scCLUE and other clustering algorithms. By addressing the problems caused by dropout events in scRNA-seq data, it improves clustering performance and facilitates more effective separation of similar cell populations.