TY - JOUR
T1 - Genomic regression analysis of coordinated expression
AU - Cai, Ling
AU - Li, Qiwei
AU - Du, Yi
AU - Yun, Jonghyun
AU - Xie, Yang
AU - Deberardinis, Ralph J.
AU - Xiao, Guanghua
N1 - Funding Information:
The authors would like to thank the BioHPC team in UT Southwestern Medical Center for help with web deployment and providing computational resources. We also thank Fang Huang from the DeBerardinis lab and Xiaowei Zhan from the QBRC for constructive suggestions for the GRACE web database, Jessie Norris for proofreading the manuscript, Xiaowei Zhan and Tao Wang for critical reading of the manuscript. This study was supported by the American Association for Cancer Research (AACR) Basic Cancer Research Fellowship (15-40-01-CAIL) awarded to L.C.; and grants from the National Cancer Institute (CA220449 to R.J.D., CA172211-01 to G.X., P50CA70907 to Y. X. and L.C.) and Cancer Prevention Research Institute of Texas (RP130272 to R.J.D., RP120732 to Y.X.).
Publisher Copyright:
© 2017 The Author(s).
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41467-017-02181-0
DO - 10.1038/s41467-017-02181-0
M3 - Article
C2 - 29259170
AN - SCOPUS:85038613030
VL - 8
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
M1 - 2187
ER -