Prioritizing biological pathways by recognizing context in time-series gene expression data

Abstract

The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While much more powerful pathway analysis method is necessary, a readily available alternative way is to incorporate the literature information. In this study, I propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, it can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then researcher should rely on the results from the pathway analysis tools, which is termed as significance in this paper. I realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. My method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets. My novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at http://biohealth.snu.ac.kr/software/contextTRAP.