Clustering gene expression time series data using an infinite Gaussian process mixture model.
Clustering gene expression time series data using an infinite Gaussian process mixture model.
复制标题
使用无限高斯工艺混合模型将基因表达时间序列数据聚类。
DOI：
10.1371/journal.pcbi.1005896
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发表时间：
2018-01
影响因子：
4.3
通讯作者：
Engelhardt BE
中科院分区：
文献类型：
--
作者：
McDowell IC;Manandhar D;Vockley CM;Schmid AK;Reddy TE;Engelhardt BE
转录组广泛的时间序列表达谱被用来表征细胞对环境扰动的反应。分析转录反应数据的第一步通常是将具有相似反应的基因聚在一起。在这里，我们提出了一种基于非参数模型的方法，Dirichlet过程高斯过程混合模型(DPGP)，它联合建模数据簇和Dirichlet过程以及时间依赖和高斯过程。我们使用数百个模拟数据集与最先进的方法相比较，证明了DPGP的准确性。为了进一步测试我们的方法，我们将DPGP应用于已发表的暴露于压力下的微生物模型有机体的微阵列数据，以及暴露于糖皮质激素地塞米松的人类细胞系的新型RNA-SEQ数据。我们通过检查局部转录因子结合和组蛋白修饰来验证我们的簇。我们的结果表明，联合建模簇数和时间依赖可以揭示共享的调节机制。DPGP软件可在线免费获得，网址为https://github.com/PrincetonUniversity/DP_GP_cluster.对基因表达动态的转录组范围的测量可以揭示控制细胞如何对环境变化做出反应的调节机制。这样的测量可能识别出成百上千个响应基因。具有相似动态的基因聚类揭示了一组较小的响应类型，然后可以为不同的功能进行探索和分析。聚类时间序列基因表达数据的两个挑战是选择聚类的数量和建模时间点之间基因表达水平的依赖关系。我们提出了一种方法，DPGP，其中Dirichlet过程将基因表达水平的轨迹随时间聚类，其中轨迹使用高斯过程建模。我们通过各种模拟数据展示了DPGP与最先进的时间序列聚类方法的性能比较。我们将DPGP应用于已发表的微生物表达数据，发现它以最少的用户输入概括了已知的表达调控。然后，我们使用DPGP来识别对广泛使用的合成糖皮质激素地塞米松的新的人类基因表达反应。我们发现通过考虑转录因子结合和组蛋白修饰的簇间差异来验证反应转录本的不同簇。这些结果表明，DPGP可以用于基因表达时间序列的探索性数据分析，以揭示生物医学上重要的基因调控过程的新见解。
Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster. Transcriptome-wide measurement of gene expression dynamics can reveal regulatory mechanisms that control how cells respond to changes in the environment. Such measurements may identify hundreds to thousands of responsive genes. Clustering genes with similar dynamics reveals a smaller set of response types that can then be explored and analyzed for distinct functions. Two challenges in clustering time series gene expression data are selecting the number of clusters and modeling dependencies in gene expression levels between time points. We present a methodology, DPGP, in which a Dirichlet process clusters the trajectories of gene expression levels across time, where the trajectories are modeled using a Gaussian process. We demonstrate the performance of DPGP compared to state-of-the-art time series clustering methods across a variety of simulated data. We apply DPGP to published microbial expression data and find that it recapitulates known expression regulation with minimal user input. We then use DPGP to identify novel human gene expression responses to the widely-prescribed synthetic glucocorticoid hormone dexamethasone. We find distinct clusters of responsive transcripts that are validated by considering between-cluster differences in transcription factor binding and histone modifications. These results demonstrate that DPGP can be used for exploratory data analysis of gene expression time series to reveal novel insights into biomedically important gene regulatory processes.
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影响因子：
30.8
作者：
He, Housheng Hansen;Meyer, Clifford A.;Shin, Hyunjin;Bailey, Shannon T.;Wei, Gang;Wang, Qianben;Zhang, Yong;Xu, Kexin;Ni, Min;Lupien, Mathieu;Mieczkowski, Piotr;Lieb, Jason D.;Zhao, Keji;Brown, Myles;Liu, X. Shirley
通讯作者：
Liu, X. Shirley
影响因子：
64.8
作者：
Heintzman, Nathaniel D.;Hon, Gary C.;Hawkins, R. David;Kheradpour, Pouya;Stark, Alexander;Harp, Lindsey F.;Ye, Zhen;Lee, Leonard K.;Stuart, Rhona K.;Ching, Christina W.;Ching, Keith A.;Antosiewicz-Bourget, Jessica E.;Liu, Hui;Zhang, Xinmin;Green, Roland D.;Lobanenkov, Victor V.;Stewart, Ron;Thomson, James A.;Crawford, Gregory E.;Kellis, Manolis;Ren, Bing
通讯作者：
Ren, Bing
DOI：
10.1080/10253890802506409
发表时间：
2009-05
期刊：
Stress (Amsterdam, Netherlands)
影响因子：
--
作者：
Biddie SC;Hager GL
通讯作者：
Hager GL
影响因子：
16
作者：
Cho, RJ;Campbell, MJ;Davis, RW
通讯作者：
Davis, RW
DOI：
10.1198/016214505000000187
发表时间：
2006-03-01
影响因子：
3.7
作者：
Heard, NA;Holmes, CC;Stephens, DA
通讯作者：
Stephens, DA