JSRC: a flexible and accurate joint learning algorithm for clustering of single-cell RNA-sequencing data.

ABSTRACT: Single-cell RNA-sequencing (scRNA-seq) explores the transcriptome of genes at cell level, which sheds light on revealing the heterogeneity and dynamics of cell populations. Advances in biotechnologies make it possible to generate scRNA-seq profiles for large-scale cells, requiring effective and efficient clustering algorithms to identify cell types and informative genes. Although great efforts have been devoted to clustering of scRNA-seq, the accuracy, scalability and interpretability of available algorithms are not desirable. In this study, we solve these problems by developing a joint learning algorithm [a.k.a. joints sparse representation and clustering (jSRC)], where the dimension reduction (DR) and clustering are integrated. Specifically, DR is employed for the scalability and joint l

SUBMITTER: Wu W 

PROVIDER: S-EPMC7953970 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature