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MetaDCN: meta-analysis framework for differential co-expression network detection with an application in breast cancer.

par Zhu, Li;Ding, Ying;Chen, Cho-Yi;Wang, Lin;Huo, Zhiguang;Kim, SungHwan;Sotiriou, Christos ;Oesterreich, Steffi;Tseng, George GC
Référence Bioinformatics, 33, 8, page (1121-1129)
Publication Publié, 2017-04-15

Article révisé par les pairs

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Résumé :

Gene co-expression network analysis from transcriptomic studies can elucidate genegene interactions and regulatory mechanisms. Differential co-expression analysis helps further detect alterations of regulatory activities in case/control comparison. Co-expression networks estimated from single transcriptomic study is often unstable and not generalizable due to cohort bias and limited sample size. With the rapid accumulation of publicly available transcriptomic studies, co-expression analysis combining multiple transcriptomic studies can provide more accurate and robust results.