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 Engelhardt Martin

Research output: Contribution to journalArticle

5 Citations (Scopus)

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 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.

Original languageEnglish (US)
Article numbere1005896
JournalPLoS computational biology
Volume14
Issue number1
DOIs
StatePublished - Jan 1 2018

Fingerprint

Dirichlet Process
Gene Expression Data
Time Series Data
Mixture Model
Gaussian Process
Gene expression
Process Model
gene expression
Cluster Analysis
Time series
time series analysis
Clustering
time series
Gene Expression
Histone Code
Multigene Family
Transcriptome
Dexamethasone
Glucocorticoids
Transcription Factors

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

McDowell, Ian C. ; Manandhar, Dinesh ; Vockley, Christopher M. ; Schmid, Amy K. ; Reddy, Timothy E. ; Engelhardt Martin, Barbara. / Clustering gene expression time series data using an infinite Gaussian process mixture model. In: PLoS computational biology. 2018 ; Vol. 14, No. 1.
@article{92e8b30674a54225ad1f686d2b35d27e,
title = "Clustering gene expression time series data using an infinite Gaussian process mixture model",
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 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.",
author = "McDowell, {Ian C.} and Dinesh Manandhar and Vockley, {Christopher M.} and Schmid, {Amy K.} and Reddy, {Timothy E.} and {Engelhardt Martin}, Barbara",
year = "2018",
month = "1",
day = "1",
doi = "10.1371/journal.pcbi.1005896",
language = "English (US)",
volume = "14",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "1",

}

Clustering gene expression time series data using an infinite Gaussian process mixture model. / McDowell, Ian C.; Manandhar, Dinesh; Vockley, Christopher M.; Schmid, Amy K.; Reddy, Timothy E.; Engelhardt Martin, Barbara.

In: PLoS computational biology, Vol. 14, No. 1, e1005896, 01.01.2018.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Clustering gene expression time series data using an infinite Gaussian process mixture model

AU - McDowell, Ian C.

AU - Manandhar, Dinesh

AU - Vockley, Christopher M.

AU - Schmid, Amy K.

AU - Reddy, Timothy E.

AU - Engelhardt Martin, Barbara

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85041379913&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85041379913&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1005896

DO - 10.1371/journal.pcbi.1005896

M3 - Article

VL - 14

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 1

M1 - e1005896

ER -