A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants.
A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants.
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DOI：
10.1111/biom.12303
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发表时间：
2015-09
期刊：
影响因子：
1.9
通讯作者：
Vannucci M
中科院分区：
文献类型：
--
作者：
Cassese A;Guindani M;Antczak P;Falciani F;Vannucci M
在这篇文章中，我们提出了一个贝叶斯层次模型来识别暴露在化学物质，特别是水中的弹药污染物中的大型水蚤生物的差异表达基因。我们提出的模型是对水净化的生物效应进行严格建模的第一次尝试之一。我们有从净化系统获得的数据，该系统包括四个连续的净化阶段，我们将其称为“池塘”，这些阶段的水受到越来越多的污染。我们将池塘中一个基因的预期表达建模为前一个池塘中相同基因的平均值加上特定于池塘的基因差异的总和。我们结合了一个变量选择机制来识别差异表达式，以及关于改变概率的先验分布，该概率解释了关于水中存在的化合物浓度的可用信息。我们通过MCMC随机搜索技术进行后验推断。在应用中，我们基于KEGG途径数据库，通过对基因功能特征进行分组，降低了数据的复杂性。这也增加了结果的生物学可解释性。我们的模型成功地识别了一些在连续的纯化阶段之间显示差异表达的途径。我们还发现，转录反应的变化与某些化合物的存在有更强的相关性，其余的影响较小。我们讨论了这些结果对衡量先验信息对后验推断影响的模型参数的敏感性。
In this paper we propose a Bayesian hierarchical model for the identification of differentially expressed genes in Daphnia Magna organisms exposed to chemical compounds, specifically munition pollutants in water. The model we propose constitutes one of the very first attempts at a rigorous modeling of the biological effects of water purification. We have data acquired from a purification system that comprises four consecutive purification stages, which we refer to as “ponds”, of progressively more contaminated water. We model the expected expression of a gene in a pond as the sum of the mean of the same gene in the previous pond plus a gene-pond specific difference. We incorporate a variable selection mechanism for the identification of the differential expressions, with a prior distribution on the probability of a change that accounts for the available information on the concentration of chemical compounds present in the water. We carry out posterior inference via MCMC stochastic search techniques. In the application, we reduce the complexity of the data by grouping genes according to their functional characteristics, based on the KEGG pathway database. This also increases the biological interpretability of the results. Our model successfully identifies a number of pathways that show differential expression between consecutive purification stages. We also find that changes in the transcriptional response are more strongly associated to the presence of certain compounds, with the remaining contributing to a lesser extent. We discuss the sensitivity of these results to the model parameters that measure the influence of the prior information on the posterior inference.