Abstract
A major challenge in life science research is understanding the mechanism involved in a given phenotype. The ability to identify the correct mechanisms is needed in order to understand fundamental and very important phenomena such as mechanisms of disease, immune systems responses to various challenges, and mechanisms of drug action. The current data analysis methods focus on the identification of the differentially expressed (DE) genes using their fold change and/or p-values. Major shortcomings of this approach are that: i) it does not consider the interactions between genes; ii) its results are sensitive to the selection of the threshold(s) used, and iii) the set of genes produced by this approach is not always conducive to formulating mechanistic hypotheses. Here we present a method that can construct networks of genes that can be considered putative mechanisms. The putative mechanisms constructed by this approach are not limited to the set of DE genes, but also considers all known and relevant gene-gene interactions. We analyzed three real datasets for which both the causes of the phenotype, as well as the true mechanisms were known. We show that the method identified the correct mechanisms when applied on microarray datasets from mouse. We compared the results of our method with the results of the classical approach, showing that our method produces more meaningful biological insights.
Highlights
Identifying the mechanism involved in a particular phenotype is an essential step in understanding the biological phenomena that lead to the phenotype
“putative” because the mechanisms we identify here are not mechanistically proven, but rather proposed mechanisms compatible with all gene expression changes measured throughout the system
In this paper we describe a method able to identify which specific gene-gene interactions and signals from an existing pathway may constitute the putative mechanism associated with a phenotype of interest
Summary
Identifying the mechanism involved in a particular phenotype is an essential step in understanding the biological phenomena that lead to the phenotype. Mechanism identification became more and more feasible with the availability of high-throughput biological data, which makes it possible to measure the expression of thousands of genes at once, and with the expanding knowledge of interactions between biological entities such as genes and proteins. The concept of mechanism has several meanings [1]. We are interested in the causal mechanism which is defined as “a step-by-step explanation of the mode of operation of a causal process that gives rise to a phenomenon of interest.” [1]. We will use the term mechanism to refer to the causal mechanism.
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