Multi-omics single-cell data alignment and integration with enhanced contrastive learning and differential attention mechanism.

ABSTRACT:

Motivation

Identifying cell types that constitute complex tissue components using single-cell sequencing data is a critical issue in the field of biology. With the continuous advancement of sequencing technologies, the recognition of cell types has evolved from analyzing single-omics scRNA-seq data to integrating multi-omics single-cell data. However, existing methods for integrative analysis of high-dimensional multi-omics single-cell sequencing data have several limitations, including reliance on specific distribution assumptions of the data, sensitivity to noise, and clustering accuracy constrained by independent clustering methods. These issues have restricted improvements in the accuracy of cell type identification and hindered the application of such methods to large-scale da

SUBMITTER: Zhang T 

PROVIDER: S-EPMC12543095 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature