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integIRTy: a method to identify genes altered in cancer by accounting for multiple mechanisms of regulation using
1Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
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.
Insights
A new method called integIRTy integrates multiple cancer gene alteration assays for more robust identification of disease-related genes. This approach surpasses individual assay analyses and conventional statistical tests, revealing novel cancer-associated genes.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Identifying cancer-associated genes is vital for understanding carcinogenesis and developing targeted therapies.
- Gene dysfunction arises from various mechanisms like copy number changes, methylation, and mutations, detectable by multiple assay types.
- Existing methods for identifying altered genes are limited to single assay types, lacking systematic integration of diverse molecular data.
Purpose of the Study:
- To introduce a novel computational method, integIRTy (integration using item response theory), for the integrated analysis of multiple high-throughput assays.
- To provide a robust and reliable approach for identifying genes altered in cancer, surpassing conventional methods when applied to single assays.
- To enable the discovery of novel cancer-associated genes by systematically integrating data from diverse molecular assays.
Main Methods:
- Developed the integIRTy method based on item response theory for integrated analysis of multiple high-throughput assays.
- Evaluated integIRTy's performance against conventional methods like Student's t-test and Wilcoxon rank-sum test for single-assay analysis.
- Applied integIRTy to integrate multiple assay types across public cancer datasets (ovarian carcinoma, breast cancer, glioblastoma).
Main Results:
- integIRTy demonstrates superior robustness and reliability compared to conventional methods on single assays.
- The integrated analysis using integIRTy successfully identified novel cancer-associated genes missed by individual assay analyses.
- Application to ovarian carcinoma, breast cancer, and glioblastoma datasets yielded encouraging results, highlighting the method's broad applicability.
Conclusions:
- The integIRTy method offers a powerful framework for the integrated analysis of multi-assay cancer data.
- This approach enhances the identification of cancer-related genes, contributing to a deeper understanding of cancer mechanisms.
- integIRTy facilitates the discovery of novel therapeutic targets and biomarkers by leveraging diverse molecular information.
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