BACKGROUND Observed 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. RESULTS We 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. CONCLUSIONS We 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.

中文翻译：

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用于调节ES细胞多能性的贝叶斯混合物回归分析。

背景技术观察到的基因表达水平强烈依赖于DNA结合转录因子（TFs）的活性和通过不同组蛋白修饰（HMs）的染色质状态。为了恢复局部染色质状态，TF结合和观察到的基因表达水平之间的功能关系，已证明回归方法是有用的工具。它们已成功应用于从全基因组实验数据预测mRNA水平的过程中，并提供了与背景相关的基因调控机制的见解。但是，由于基因组特异的调节相互作用而产生的异质性常常被忽略。结果我们显示，通过使用TF的实验推导ChIP-seq图谱预测基因表达的回归模型可以通过混合建模得到显着改善。为了找到生物学上相关的基因簇，我们采用了贝叶斯分配程序，该程序允许我们整合其他生物学信息，例如染色体的三维核组织和基因功能。数据整合程序涉及将附加数据转换为基因相似性值。我们提出了一种通用相似性度量，该度量特别适用于附加数据为连续和离散类型的情况，并在混合建模的情况下将其性能与相似度量进行比较。结论我们将提出的方法应用于小鼠胚胎干细胞（ESC）的数据。我们发现，包括其他数据会导致混合物成分表现出生物学上有意义的基因簇，