New network topology approaches reveal differential correlation patterns in breast cancer

BMC Systems Biology

Jan Budczies¹  Carsten Denkert¹  Balazs Györffy²  Frederick Klauschen¹  Michael Bockmayr¹ 

[1] Institute for Pathology, Charité University Hospital Berlin, Charitéplatz 1, 10117 Berlin, Germany;Joint Research Laboratory of the Hungarian Academy of Sciences and the Semmelweis University, Semmelweis University 1st Dept of Pediatrics, 1083 Budapest, Hungary

关键词: Differential co-expression;    Molecular subtypes;    Breast cancer;    Microarray data;    Differential correlation;   

Others  :  1142347
DOI  :  10.1186/1752-0509-7-78

 received in 2013-04-15, accepted in 2013-08-06,  发布年份 2013

【 摘 要 】

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 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, DCloc detected 770 differentially correlated genes with a FDR of 12.8%, while DCglob 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 DCloc and 33% for DCglob.

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, DCloc and DCglob were shown to identify specific and reproducible lists of differentially correlated genes.

【 授权许可】

   
2013 Bockmayr et al.; licensee BioMed Central Ltd.

【 图 表 】

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