Adaptive multi-view information bottleneck for multi-omics data clustering.

ABSTRACT:

Motivation

Recent advances in single-cell sequencing have transformed precise measurement of gene expression at cellular resolution, enabling unprecedented dissection of cellular heterogeneity and intricate biological processes. The accumulation of multi-omics data offers new avenues for cell clustering-a critical foundation for cell-type identification and downstream analyses. However, substantial challenges persist in simultaneously achieving effective integration of complementary information in multi-omics data and their appropriate weight allocation.

Results

Here, we propose an Adaptive Multi-View clustering framework with the Information Bottleneck principle to solve the multi-omics data clustering task (named scAMVIB). The proposed model could learn multi-view omics re

SUBMITTER: Tian Z 

PROVIDER: S-EPMC12796825 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature