Integrative analysis of histone ChIP-seq and gene expression microarray data using Bayesian mixture models

Histone modifications are a key epigenetic mechanism to activate or repress the expression of genes. Data sets of matched microarray expression data and histone modification data measured by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatic approach to detect genes that are differentially expressed between two conditions putatively caused by alterations in histone modification. We introduce a correlation measure for integrative analysis of ChIP-seq and gene expression data and demonstrate that a proper normalization of the ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene expression and histone modification. The method is applied to different data sets and its superiority to a naive separate analysis of both data types is demonstrated. This GEO series contains the expression data of the Cebpa example data set.

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Klein HU, Schäfer M, Porse BT, Hasemann MS, Ickstadt K, Dugas M