Abstract
High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called "ReactomeFIViz", which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.
Highlights
High-throughput experiments, which generate large and complex data sets, are routinely performed in modern biological and clinical studies to unravel mechanisms underlying complex diseases, such as cancer
We describe a software tool called ReactomeFIViz, which can be used to perform pathway- and network-based data analysis for data generated from high-throughput experiments
Based on a list of genes loaded from a file, the user can construct a subnetwork, perform network clustering to search for network modules related to patient clinical or other phenotypic information, annotate network modules, perform pathway enrichment analysis, and even model pathway activities based on probabilistic graphical models[9]
Summary
2. Nikolaus Schultz, Memorial Sloan-Kettering Cancer Center, New York, USA B. Any reports and responses or comments on the article can be found at the end of the article. This article is included in the Cytoscape gateway. This article is included in the International Society for Computational Biology Community Journal gateway. The major changes we have made to this version according to suggestions from reviewers are below: 1). Added a new paragraph in the “Results” section. Added a new reference as Reference 8.
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