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

Publication ,  Journal Article
McDowell, IC; Manandhar, D; Vockley, CM; Schmid, AK; Reddy, TE; Engelhardt, BE
Published in: PLoS Comput Biol
January 2018

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.

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Published In

PLoS Comput Biol

DOI

EISSN

1553-7358

Publication Date

January 2018

Volume

14

Issue

1

Start / End Page

e1005896

Location

United States

Related Subject Headings

  • Transcription Factors
  • Time Factors
  • Sequence Analysis, RNA
  • Oligonucleotide Array Sequence Analysis
  • Normal Distribution
  • Models, Biological
  • Lung Neoplasms
  • Hydrogen Peroxide
  • Hydrogen Bonding
  • Humans
 

Citation

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McDowell, I. C., Manandhar, D., Vockley, C. M., Schmid, A. K., Reddy, T. E., & Engelhardt, B. E. (2018). Clustering gene expression time series data using an infinite Gaussian process mixture model. PLoS Comput Biol, 14(1), e1005896. https://doi.org/10.1371/journal.pcbi.1005896
McDowell, Ian C., Dinesh Manandhar, Christopher M. Vockley, Amy K. Schmid, Timothy E. Reddy, and Barbara E. Engelhardt. “Clustering gene expression time series data using an infinite Gaussian process mixture model.PLoS Comput Biol 14, no. 1 (January 2018): e1005896. https://doi.org/10.1371/journal.pcbi.1005896.
McDowell IC, Manandhar D, Vockley CM, Schmid AK, Reddy TE, Engelhardt BE. Clustering gene expression time series data using an infinite Gaussian process mixture model. PLoS Comput Biol. 2018 Jan;14(1):e1005896.
McDowell, Ian C., et al. “Clustering gene expression time series data using an infinite Gaussian process mixture model.PLoS Comput Biol, vol. 14, no. 1, Jan. 2018, p. e1005896. Pubmed, doi:10.1371/journal.pcbi.1005896.
McDowell IC, Manandhar D, Vockley CM, Schmid AK, Reddy TE, Engelhardt BE. Clustering gene expression time series data using an infinite Gaussian process mixture model. PLoS Comput Biol. 2018 Jan;14(1):e1005896.

Published In

PLoS Comput Biol

DOI

EISSN

1553-7358

Publication Date

January 2018

Volume

14

Issue

1

Start / End Page

e1005896

Location

United States

Related Subject Headings

  • Transcription Factors
  • Time Factors
  • Sequence Analysis, RNA
  • Oligonucleotide Array Sequence Analysis
  • Normal Distribution
  • Models, Biological
  • Lung Neoplasms
  • Hydrogen Peroxide
  • Hydrogen Bonding
  • Humans