Mixture Models For The Analysis Of Gene Expression

Author : Johannes M. Freudenberg
Publisher :
ISBN 13 :
Total Pages : 112 pages
Book Rating : 4.:/5 (679 download)

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Download or read book Bayesian Infinite Mixture Models for Gene Clustering and Simultaneous Context Selection Using High-throughput Gene Expression Data written by Johannes M. Freudenberg and published by . This book was released on 2009 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applying clustering algorithms to identify groups of co-expressed genes is an important step in the analysis of high-throughput genomics data in order to elucidate affected biological pathways and transcriptional regulatory mechanisms. As these data are becoming ever more abundant the integration with both, existing biological knowledge and other experimental data becomes as crucial as the ability to perform such analysis in a meaningful but virtually unsupervised fashion. Clustering analysis often relies on ad-hoc methods such as k-means or hierarchical clustering with Euclidean distance but model-based methods such as the Bayesian Infinite Mixtures approach have been shown to produce better, more reproducible results. Further improvements have been accomplished by context-specific gene clustering algorithms designed to determine groups of co-expressed genes within a given subset of biological samples termed context. The complementary problem of finding differentially co-expressed genes given two or more contexts has been addressed but relies on the a priori definition of contexts and has not been used to facilitate the clustering of biological samples. Here we describe a new computational method using Bayesian infinite mixture models to cluster genes simultaneously utilizing the concept of differential co-expression as a unique similarity measure to find groups of similar samples. We compute a novel per-gene differential co-expression score that is reproducible and biologically meaningful. To evaluate, annotate, and display clustering results we present the integrated software package CLEAN which contains functionality for performing Clustering Enrichment Analysis, a method to functionally annotate clustering results and to assign a novel gene-specific functional coherence score. We apply our method to a number of simulated datasets comparing it to other commonly used clustering algorithms, and we re-analyze several breast cancer studies. We find that our unsupervised method determines patient groupings highly predictive of clinically relevant factors such as estrogen receptor status, tumor grade, and disease specific survival. Integrating these data with computationally and literature-derived information by applying CLEAN to the corresponding clusterings as well as the DCS signature substantiates these findings. Our results demonstrate the range of applications our methodology provides, offering a comprehensive analysis tool to study gene co-expression and differential co-expression patterns specific to the biological conditions of interest while simultaneously determining subsets of such biological conditions using a unique similarity measure that is complementary to the currently existing methods. It allows us to further our understanding of highly complex diseases such as breast cancer, and it has the potential to greatly facilitate research in many other, not yet as intensively studied areas.