AJGM: joint learning of heterogeneous gene networks with adaptive graphical model.

ABSTRACT:

Motivation

Inferring gene networks provides insights into biological pathways and functional relationships among genes. When gene expression samples exhibit heterogeneity, they may originate from unknown subtypes, prompting the utilization of mixture Gaussian graphical model (GGM) for simultaneous subclassification and gene network inference. However, this method overlooks the heterogeneity of network relationships across subtypes and does not sufficiently emphasize shared relationships. Additionally, GGM assumes data follows a multivariate Gaussian distribution, which is often not the case with zero-inflated scRNA-seq data.

Results

We propose an Adaptive Joint Graphical Model (AJGM) for estimating multiple gene networks from single-cell or bulk data with unknown heterogenei

SUBMITTER: Yang S 

PROVIDER: S-EPMC11937957 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature