Identifying biologically relevant putative mechanisms in a given phenotype comparison

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.

CASP-Model Sepsis Triggers Systemic Innate Immune Responses Revealed by the Systems-Level Signaling Pathways

“…p -value is used to control the false discovery rate (FDR) for multiple testing ( 48 , 49 ). Most importantly, the PathwayKO package has an advantage to conduct automatic selection of differentially expressed genes (DEGs) because it adapts the change-point method ( 44 ) based on HES threshold ( 50 ) that are automatically determined on the distribution of edge scores of each data, into which the fold-change and probability values of genes at the systems-level have been integrated.…”

PathwayKO: An integrated platform for deciphering the systems-level signaling pathways

“…The PathwayKO platform (Figure 1) currently incorporates and drives a set of internal and external packages (Figure 1A) coupled with diverse dependencies (Figure 1B) to pursue the integrative (I) preprocessing, (II) ROC-AUC calculating, (III) statistics analyzing, and (IV) visualizing processes. All external packages are adapted from the literature (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). The PathwayKO platform as systems software has integrated state-of-the-art methods of pathway enrichment analysis, statistics analysis, and visualizing analysis (Figures 1A, B), which work in a pipeline fashion (Figures 1C, D).…”

“…The main module pathwayko can automatically select DEGs through enabling the HES (high-edge-score) approach (17), in addition to classical approaches (5,7,8). The main module pathwayko drives the external changepoint package (18), by which the change-point analysis method (see Figure 1) makes a statistical decision on choosing the differentially expressed genes (DEGs) based on the distribution of edge scores (Figure 2) that are constructed from data (17).…”

“…The main module pathwayko can automatically select DEGs through enabling the HES (high-edge-score) approach (17), in addition to classical approaches (5,7,8). The main module pathwayko drives the external changepoint package (18), by which the change-point analysis method (see Figure 1) makes a statistical decision on choosing the differentially expressed genes (DEGs) based on the distribution of edge scores (Figure 2) that are constructed from data (17). The resulting output directory will be automatically created and named after the given data; the directory contains a set of output files including the list of DEGs, the list of knockout (KO) KEGG pathways, and the KO geneassociated subnetwork.…”

“…These arbitrary cutoffs to select DEGs hinder a fair and reasonable comparisons under the same context, as discussed in the literature (5,7). Meanwhile, approaches to evaluating performance differences among pathway analysis methods have recently evolved from traditional disease-target-pathways (7) toward known-KO pathways (5,17). High-throughput transcriptomes accumulated in gene-KO experiments have offered unique opportunities to decipher target KO signaling pathways that are truly impaired by KO genes at the systems-level (1,5).…”