Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers.

ABSTRACT: Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to con

SUBMITTER: Hsiao TH 

PROVIDER: S-EPMC4789788 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature