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

4 Scopus citations

Abstract

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
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2017

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

Fingerprint Dive into the research topics of 'Genomic regression analysis of coordinated expression'. Together they form a unique fingerprint.

Cite this