Research Article

IFGFA: Identification of featured genes from genomic data using factor analysis

Published: July 25, 2016
Genet. Mol. Res. 15(3): gmr8803 DOI: 10.4238/gmr.15038803

Abstract

In this study, a software tool (IFGFA) for identification of featured genes from gene expression data based on latent factor analysis was developed. Despite the availability of computational methods and statistical models appropriate for analyzing special genomic data, IFGFA provides a platform for predicting colon cancer-related genes and can be applied to other cancer types. The computational framework behind IFGFA is based on the well-established Bayesian factor and regression model and prior knowledge about the gene from OMIM. We validated the predicted genes by analyzing somatic mutations in patients. An interface was developed to enable users to run the computational framework efficiently through visual programming. IFGFA is executable in a Windows system and does not require other dependent software packages. This program can be freely downloaded at http://www.fupage.org/downloads/ifgfa.zip.

In this study, a software tool (IFGFA) for identification of featured genes from gene expression data based on latent factor analysis was developed. Despite the availability of computational methods and statistical models appropriate for analyzing special genomic data, IFGFA provides a platform for predicting colon cancer-related genes and can be applied to other cancer types. The computational framework behind IFGFA is based on the well-established Bayesian factor and regression model and prior knowledge about the gene from OMIM. We validated the predicted genes by analyzing somatic mutations in patients. An interface was developed to enable users to run the computational framework efficiently through visual programming. IFGFA is executable in a Windows system and does not require other dependent software packages. This program can be freely downloaded at http://www.fupage.org/downloads/ifgfa.zip.