RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.

ABSTRACT: Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When expression data of multiple (p) genes in multiple (K) conditions (e.g., treatments, tissues, strains) are available, joint estimation of networks harnessing shared information across them can significantly increase the power of analysis. In addition, examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. Condition adaptive fused graphical lasso (CFGL) is an existing method that incorporates condition specificity in a fused graphical la

SUBMITTER: Seal S 

PROVIDER: S-EPMC9821764 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature