A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data

• Published in: PloS one Vol. 11; no. 2; p. e0148977
• Main Authors: Baur, Brittany, Bozdag, Serdar
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.02.2016; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Bioinformatics; Biological activity; Biology and Life Sciences; Breast cancer; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Cancer; Cell Line, Tumor; Classification; Computer science; Decision trees; Deoxyribonucleic acid; DNA; DNA Methylation; DNA Probes; Epigenesis, Genetic; Epigenetic inheritance; Female; Gene Expression; Gene Expression Regulation, Neoplastic; Gene Ontology; Genes; Genetic algorithms; Genetic aspects; Genomes; Humans; Investigations; Kinases; Medicine and Health Sciences; Methylation; Multigene Family; Physical Sciences; Physiological aspects; Probes; Recursive methods; Research and Analysis Methods; Social Sciences; Studies; Support vector machines; Transcription factors; United States; United States > US; Wisconsin
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0148977

DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes.