New network topology approaches reveal differential correlation patterns in breast cancer.

ABSTRACT:

Background

Analysis of genome-wide data is often carried out using standard methods such as differential expression analysis, clustering analysis and heatmaps. Beyond that, differential correlation analysis was suggested to identify changes in the correlation patterns between disease states. The detection of differential correlation is a demanding task, as the number of entries in the gene-by-gene correlation matrix is large. Currently, there is no gold standard for the detection of differential correlation and statistical validation.

Results

We developed two untargeted algorithms (DCloc and DCglob) that identify differential correlation patterns by comparing the local or global topology of correlation networks. Construction of networks from correlation structures requires f

SUBMITTER: Bockmayr M 

PROVIDER: S-EPMC3848818 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature