Pan Tong1, Kevin R Coombes. 1. Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
Abstract
MOTIVATION: Identifying genes altered in cancer plays a crucial role in both understanding the mechanism of carcinogenesis and developing novel therapeutics. It is known that there are various mechanisms of regulation that can lead to gene dysfunction, including copy number change, methylation, abnormal expression, mutation and so on. Nowadays, all these types of alterations can be simultaneously interrogated by different types of assays. Although many methods have been proposed to identify altered genes from a single assay, there is no method that can deal with multiple assays accounting for different alteration types systematically. RESULTS: In this article, we propose a novel method, integration using item response theory (integIRTy), to identify altered genes by using item response theory that allows integrated analysis of multiple high-throughput assays. When applied to a single assay, the proposed method is more robust and reliable than conventional methods such as Student's t-test or the Wilcoxon rank-sum test. When used to integrate multiple assays, integIRTy can identify novel-altered genes that cannot be found by looking at individual assay separately. We applied integIRTy to three public cancer datasets (ovarian carcinoma, breast cancer, glioblastoma) for cross-assay type integration which all show encouraging results. AVAILABILITY AND IMPLEMENTATION: The R package integIRTy is available at the web site http://bioinformatics.mdanderson.org/main/OOMPA:Overview. CONTACT: kcoombes@mdanderson.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Identifying genes altered in cancer plays a crucial role in both understanding the mechanism of carcinogenesis and developing novel therapeutics. It is known that there are various mechanisms of regulation that can lead to gene dysfunction, including copy number change, methylation, abnormal expression, mutation and so on. Nowadays, all these types of alterations can be simultaneously interrogated by different types of assays. Although many methods have been proposed to identify altered genes from a single assay, there is no method that can deal with multiple assays accounting for different alteration types systematically. RESULTS: In this article, we propose a novel method, integration using item response theory (integIRTy), to identify altered genes by using item response theory that allows integrated analysis of multiple high-throughput assays. When applied to a single assay, the proposed method is more robust and reliable than conventional methods such as Student's t-test or the Wilcoxon rank-sum test. When used to integrate multiple assays, integIRTy can identify novel-altered genes that cannot be found by looking at individual assay separately. We applied integIRTy to three public cancer datasets (ovarian carcinoma, breast cancer, glioblastoma) for cross-assay type integration which all show encouraging results. AVAILABILITY AND IMPLEMENTATION: The R package integIRTy is available at the web site http://bioinformatics.mdanderson.org/main/OOMPA:Overview. CONTACT: kcoombes@mdanderson.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Xiwei Wu; Tibor A Rauch; Xueyan Zhong; William P Bennett; Farida Latif; Dietmar Krex; Gerd P Pfeifer Journal: Cancer Res Date: 2010-03-16 Impact factor: 12.701
Authors: Xin Victoria Wang; Roel G W Verhaak; Elizabeth Purdom; Paul T Spellman; Terence P Speed Journal: PLoS One Date: 2011-03-11 Impact factor: 3.240