NEpiC: a network-assisted algorithm for epigenetic studies using mean and variance combined signals.
DNA methylation plays an important role in many biological processes. Existing epigenome-wide association studies (EWAS) have successfully identified aberrantly methylated genes in many diseases and disorders with most studies focusing on analysing methylation sites one at a time. Incorporating prior biological information such as biological networks has been proven to be powerful in identifying disease-associated genes in both gene expression studies and genome-wide association studies (GWAS) but has been under studied in EWAS. Although recent studies have noticed that there are differences in methylation variation in different groups, only a few existing methods consider variance signals in DNA methylation studies. Here, we present a network-assisted algorithm, NEpiC, that combines both mean and variance signals in searching for differentially methylated sub-networks using the protein-protein interaction (PPI) network. In simulation studies, we demonstrate the power gain from using both the prior biological information and variance signals compared to using either of the two or neither information. Applications to several DNA methylation datasets from the Cancer Genome Atlas (TCGA) project and DNA methylation data on hepatocellular carcinoma (HCC) from the Columbia University Medical Center (CUMC) suggest that the proposed NEpiC algorithm identifies more cancer-related genes and generates better replication results.
School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.
Peifeng
P
Ruan
Department of Environmental Health Science, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
Jing
J
Shen
Department of Environmental Health Science, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
Regina M
RM
Santella
School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.
Shuigeng
S
Zhou
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA sw2206@columbia.edu.
Shuang
S
Wang
D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics; D003198:Computer Simulation; D019175:DNA Methylation / Q000235:genetics; D044127:Epigenesis, Genetic; D057890:Epigenomics; D005260:Female; D056726:Genetic Association Studies; D055106:Genome-Wide Association Study; D006801:Humans; D009361:Neoplasm Invasiveness