ScEFSC: Accurate single-cell RNA-seq data analysis via ensemble consensus clustering based on multiple feature selections.

ABSTRACT: With the development of next-generation sequencing technologies, single-cell RNA sequencing (scRNA-seq) has become one indispensable tool to reveal the wide heterogeneity between cells. Clustering is a fundamental task in this analysis to disclose the transcriptomic profiles of single cells and is one of the key computational problems that has received widespread attention. Recently, many clustering algorithms have been developed for the scRNA-seq data. Nevertheless, the computational models often suffer from realistic restrictions such as numerical instability, high dimensionality and computational scalability. Moreover, the accumulating cell numbers and high dropout rates bring a huge computational challenge to the analysis. To address these limitations, we first provide a systematic and

SUBMITTER: Bian C 

PROVIDER: S-EPMC9108753 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature