Mutual information for detecting multi-class biomarkers when integrating multiple bulk or single-cell transcriptomic studies.

ABSTRACT:

Motivation

Biomarker detection plays a pivotal role in biomedical research. Integrating omics studies from multiple cohorts can enhance statistical power, accuracy, and robustness of the detection results. However, existing methods for horizontally combining omics studies are mostly designed for two-class scenarios (e.g. cases versus controls) and are not directly applicable for studies with multi-class design (e.g. samples from multiple disease subtypes, treatments, tissues, or cell types).

Results

We propose a statistical framework, namely Mutual Information Concordance Analysis (MICA), to detect biomarkers with concordant multi-class expression pattern across multiple omics studies from an information theoretic perspective. Our approach first detects biomarkers with conco

SUBMITTER: Zou J 

PROVIDER: S-EPMC11629966 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature