Discovery of Intermediary Genes between Pathways Using Sparse Regression.

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-course RNA-Seq data for dendritic cell from wild type, MyD88-knockout, and TRIF-knockout mice, and a set of RNA-Seq data from 60 Caucasian individuals. For the sparse regression approach, we found overrepresented functions for shared neighbor genes between TLR-signaling pathway and antigen processing and presentation, apoptosis, and Jak-Stat pathways that are supported by prior research, and compares favorably to Weighted Correlation Network Analysis in cases where the gene association signals are weak.