Discovery of Intermediary Genes between Pathways Using Sparse Regression

PloS One
Kuo-Ching LiangKenta Nakai

Abstract

The use of pathways and gene interaction networks for the analysis of differential expression experiments has allowed us to highlight the differences in gene expression profiles between samples in a systems biology perspective. The usefulness and accuracy of pathway analysis critically depend on our understanding of how genes interact with one another. That knowledge is continuously improving due to advances in next generation sequencing technologies and in computational methods. While most approaches treat each of them as independent entities, pathways actually coordinate to perform essential functions in a cell. In this work, we propose a methodology based on a sparse regression approach to find genes that act as intermediary to and interact with two pathways. We model each gene in a pathway using a set of predictor genes, and a connection is formed between the pathway gene and a predictor gene if the sparse regression coefficient corresponding to the predictor gene is non-zero. A predictor gene is a shared neighbor gene of two pathways if it is connected to at least one gene in each pathway. We compare the sparse regression approach to Weighted Correlation Network Analysis and a correlation distance based approach using time...Continue Reading

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Citations

Mar 2, 2016·Phytomedicine : International Journal of Phytotherapy and Phytopharmacology·Lai Yen FongZuraini Ahmad
Sep 24, 2015·The Journal of Arthroplasty·Joshua B HoltNicolas O Noiseux

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Methods Mentioned

BETA
RNA-Seq
gene-knockout
gene knockout
gene knockouts
nucleotide exchange

Software Mentioned

Cytoscape
Tophat2
Bowtie1
AmiGO
elastic
R
Cuffdiff
net
WGCNA
FastQC

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