Bayesian mixture regression analysis for regulation of Pluripotency in ES cells

Abstract

BackgroundObserved levels of gene expression strongly depend on both activity of DNA binding transcription factors (TFs) and chromatin state through different histone modifications (HMs). In order to recover the functional relationship between local chromatin state, TF binding and observed levels of gene expression, regression methods have proven to be useful tools. They have been successfully applied to predict mRNA levels from genome-wide experimental data and they provide insight into context-dependent gene regulatory mechanisms. However, heterogeneity arising from gene-set specific regulatory interactions is often overlooked.ResultsWe show that regression models that predict gene expression by using experimentally derived ChIP-seq profiles of TFs can be significantly improved by mixture modelling. In order to find biologically relevant gene clusters, we employ a Bayesian allocation procedure which allows us to integrate additional biological information such as three-dimensional nuclear organization of chromosomes and gene function. The data integration procedure involves transforming the additional data into gene similarity values. We propose a generic similarity measure that is especially suitable for situations where the additional data are of both continuous and discrete type, and compare its performance with similar measures in the context of mixture modelling.ConclusionsWe applied the proposed method on a data from mouse embryonic stem cells (ESC). We find that including additional data results in mixture components that exhibit biologically meaningful gene clusters, and provides valuable insight into the heterogeneity of the regulatory interactions.

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“…We constructed the similarity matrix based on the number of days between the posting dates of tweets. The impact of this measure on the clustering in mixture models is studied in an earlier paper [ 24 ] where several similarity measures are compared.…”

Latent class distributional regression for the estimation of non-linear reference limits from contaminated data sources

“…Finite mixture distributions have quite a long history [19,20], are comprehensively described in the statistical literature [21][22][23] and continue to provide useful approaches to a wide range of applications (e.g. [24,25]). The most common strategy to estimate the unknown parameters is the expectation maximization algorithm [26].…”