Clustering high-throughput sequencing data with Poisson mixture models
Year: 2011
Type: report
Abstract: In recent years gene expression studies have increasingly made use of next generation sequencing technology. In turn, research concerning the appropriate statistical methods for the analysis of digital gene expression has flourished, primarily in the context of normalization and differential analysis. In this work, we focus on the question of clustering digital gene expression profiles as a means to discover groups of co-expressed genes. We propose two parameterizations of a Poisson mixture model to cluster expression profiles of high-throughput sequencing data. A set of simulation studies compares the performance of the proposed models with that of an approach developed for a similar type of data, namely serial analysis of gene expression. We also study the performance of these approaches on two real high-throughput sequencing data sets. The R package HTSCluster used to implement the proposed Poisson mixture models is available on CRAN.
Source: HAL
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W1161193816