Bockmayr M

Budczies J

Denkert C

Győrffy, Balázs

Klauschen F

BMC Systems Biology

Semmelweis Repository

New network topology approaches reveal differential correlation patterns in breast cancer

Springer - Publisher Connector

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 ( and ) that identify differential correlation patterns by comparing the local or global topology of correlation networks. Construction of networks from correlation structures requires fixing of a correlation threshold. Instead of a single cutoff, the algorithms systematically investigate a series of correlation thresholds and permit to detect different kinds of correlation changes at the same level of significance: strong changes of a few genes and moderate changes of many genes. Comparing the correlation structure of 208 ER- breast carcinomas and 208 ER+ breast carcinomas,  detected 770 differentially correlated genes with a FDR of 12.8%, while  detected 630 differentially correlated genes with a FDR of 12.1%. In two-fold cross-validation, the reproducibility of the list of the top 5% differentially correlated genes in 140 ER- tumors and in 140 ER+ tumors was 49% for  and 33% for .
          
          
            Conclusions
            We developed two correlation network topology based algorithms for the detection of differential correlations in different disease states. Clusters of differentially correlated genes could be interpreted biologically and included the marker genes hydroxyprostaglandin dehydrogenase (PGDH) and acyl-CoA synthetase medium chain 1 (ACSM1) of invasive apocrine carcinomas that were differentially correlated, but not differentially expressed. Using random subsampling and cross-validation,  and  were shown to identify specific and reproducible lists of differentially correlated genes.
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Michael Bockmayr

Frederick Klauschen

Balazs Györffy

Carsten Denkert

Jan Budczies

Crossref

http://repo.lib.semmelweis.hu/bitstream/handle/123456789/400/New%20network%20topology%20approaches.pdf?sequence=1

New network topology approaches reveal differential correlation patterns in breast cancer

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Semmelweis Repository

Springer - Publisher Connector

Crossref

Springer - Publisher Connector