Human Gene Coexpression Landscape: Confident Network Derived from Tissue Transcriptomic Profiles

Human Gene Coexpression Landscape: Confident Network Derived from Tissue Transcriptomic Profiles

BACKGROUND Analysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global "omic" scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. M...

Coexpression profiles reveal hidden gene networks.

The large-scale automation of neuroscience has enabled the construction of genome-wide atlases, of which the Allen Brain Atlas (ABA), which allows the 3D visualization of the expression profile of 21,500 genes in the male mouse brain down to single-cell level resolution, is the most comprehensive (1). In PNAS, Mahfouz et al. (2) use the ability of the ABA to pinpoint the anatomical locations of...

Geometric Interpretation of Gene Coexpression Network Analysis

THE MERGING OF NETWORK THEORY AND MICROARRAY DATA ANALYSIS TECHNIQUES HAS SPAWNED A NEW FIELD: gene coexpression network analysis. While network methods are increasingly used in biology, the network vocabulary of computational biologists tends to be far more limited than that of, say, social network theorists. Here we review and propose several potentially useful network concepts. We take advan...

Methods and tools for mining the transcriptomic landscape of human tissue and disease

Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network

Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene ex...