Abstract
In recent years, analyzing genome-wide expression profiles to find genetic markers has received much attention as a challenging field of research aiming at unveiling biological mechanisms behind complex disorders. The identification of reliable and reproducible markers has lately been achieved by integrating genome-scale functional relationships and transcriptome datasets, and a number of algorithms have been developed to support this strategy. In this paper, we present a promising and easily applicable tool to accomplish this goal, namely CytoGTA, which is a Cytoscape plug-in that relies on an optimistic game theoretic approach (GTA) for identifying subnetwork markers. Given transcriptomic data of two phenotype classes and interactome data, this plug-in offers discriminative markers for the two classes. The high performance of CytoGTA would not have been achieved if the strategy of GTA was not implemented in Cytoscape. This plug-in provides a simple-to-use platform, convenient for biological researchers to interactively work with and visualize the structure of subnetwork markers. CytoGTA is one of the few available Cytoscape plug-ins for marker identification, which shows superior performance to existing methods.
Highlights
It is commonly acknowledged that genetic perturbations in human cells are the main reason of cancer initiation and progression[1]
As we aimed to provide an easy-to-use tool for identifying potential subnetwork markers, CytoGTA has been developed as a Cytoscape app, written in Java programming language
CytoGTA takes as input a weighted protein-protein interaction networks (PPINs) and a gene expression profile, and results in numbers of connected subnetworks, which are highly associated with the given expression profile
Summary
It is commonly acknowledged that genetic perturbations in human cells are the main reason of cancer initiation and progression[1]. As an objective evidence for the diagnosis and monitoring cancer in earlier stages, provides a valuable opportunity for researchers to detect, cure, or at least delay the progression of cancer in human body [2]. Genetic mutations may bring diverse consequences, including conformational alteration in protein structure, loss or serious changes in protein function and deregulation of gene expression. There have been numerous studies suggesting differentially expressed genes (DEGs) in cancer versus normal samples, as biomarkers [3,4,5]. The efficiency and reproducibility of identified biomarkers rely extensively on sample size, data quality and heterogeneity of experimental platforms utilized for analysis[6].
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