Genomic regression analysis of coordinated expression

Ling Cai, Qiwei Li, Yi Du, Jonghyun Yun, Yang Xie, Ralph J. Deberardinis, Guanghua Xiao

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Co-expression analysis is widely used to predict gene function and to identify functionally related gene sets. However, co-expression analysis using human cancer transcriptomic data is confounded by somatic copy number alterations (SCNA), which produce co-expression signatures based on physical proximity rather than biological function. To better understand gene-gene co-expression based on biological regulation but not SCNA, we describe a method termed "Genomic Regression Analysis of Coordinated Expression" (GRACE) to adjust for the effect of SCNA in co-expression analysis. The results from analyses of TCGA, CCLE, and NCI60 data sets show that GRACE can improve our understanding of how a transcriptional network is re-wired in cancer. A user-friendly web database populated with data sets from The Cancer Genome Atlas (TCGA) is provided to allow customized query.

Original languageEnglish (US)
Article number2187
JournalNature communications
Issue number1
StatePublished - Dec 1 2017

ASJC Scopus subject areas

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy


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