NsDCC: dual-level contrastive clustering with nonuniform sampling for scRNA-seq data analysis.

ABSTRACT: Dimensionality reduction and clustering are crucial tasks in single-cell RNA sequencing (scRNA-seq) data analysis, treated independently in the current process, hindering their mutual benefits. The latest methods jointly optimize these tasks through deep clustering. However, contrastive learning, with powerful representation capability, can bridge the gap that common deep clustering methods face, which requires pre-defined cluster centers. Therefore, a dual-level contrastive clustering method with nonuniform sampling (nsDCC) is proposed for scRNA-seq data analysis. Dual-level contrastive clustering, which combines instance-level contrast and cluster-level contrast, jointly optimizes dimensionality reduction and clustering. Multi-positive contrastive learning and unit matrix constraint are

SUBMITTER: Wang L 

PROVIDER: S-EPMC11427072 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature