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Clustering gene expression time series data using an infinite Gaussian process mixture model

Ian C. McDowell, Dinesh Manandhar, Christopher M. Vockley, Amy K. Schmid, Timothy E. Reddy, View ORCID ProfileBarbara E. Engelhardt
doi: https://doi.org/10.1101/131151
Ian C. McDowell
1Computational Biology & Bioinformatics Graduate Program, Duke University, Durham, United States;
2Center for Genomic & Computational Biology, Duke University, Durham, United States;
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Dinesh Manandhar
1Computational Biology & Bioinformatics Graduate Program, Duke University, Durham, United States;
2Center for Genomic & Computational Biology, Duke University, Durham, United States;
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Christopher M. Vockley
2Center for Genomic & Computational Biology, Duke University, Durham, United States;
3Department of Biostatistics & Bioinformatics, Duke University Medical Center, Durham, United States;
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Amy K. Schmid
2Center for Genomic & Computational Biology, Duke University, Durham, United States;
4Biology Department, Duke University, Durham, United States;
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Timothy E. Reddy
1Computational Biology & Bioinformatics Graduate Program, Duke University, Durham, United States;
2Center for Genomic & Computational Biology, Duke University, Durham, United States;
3Department of Biostatistics & Bioinformatics, Duke University Medical Center, Durham, United States;
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  • For correspondence: tim.reddy@duke.edu bee@princeton.edu
Barbara E. Engelhardt
5Department of Computer Science, Princeton University, Princeton, United States;
6Center for Statistics and Machine Learning, Princeton University, Princeton, United States
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  • ORCID record for Barbara E. Engelhardt
  • For correspondence: tim.reddy@duke.edu bee@princeton.edu
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Abstract

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 cluster number with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison with 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 novel regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 26, 2017.
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Clustering gene expression time series data using an infinite Gaussian process mixture model
Ian C. McDowell, Dinesh Manandhar, Christopher M. Vockley, Amy K. Schmid, Timothy E. Reddy, Barbara E. Engelhardt
bioRxiv 131151; doi: https://doi.org/10.1101/131151
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Clustering gene expression time series data using an infinite Gaussian process mixture model
Ian C. McDowell, Dinesh Manandhar, Christopher M. Vockley, Amy K. Schmid, Timothy E. Reddy, Barbara E. Engelhardt
bioRxiv 131151; doi: https://doi.org/10.1101/131151

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