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Clustering gene expression time series data using an infinite Gaussian process mixture model.
- Source :
-
PLoS computational biology [PLoS Comput Biol] 2018 Jan 16; Vol. 14 (1), pp. e1005896. Date of Electronic Publication: 2018 Jan 16 (Print Publication: 2018). - Publication Year :
- 2018
-
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.
- Subjects :
- A549 Cells
Algorithms
Cell Line, Tumor
Computational Biology
Computer Simulation
Dexamethasone chemistry
Gene Expression Profiling
Glucocorticoids chemistry
Histones chemistry
Humans
Hydrogen Bonding
Hydrogen Peroxide chemistry
Lung Neoplasms drug therapy
Models, Biological
Normal Distribution
Oligonucleotide Array Sequence Analysis
Sequence Analysis, RNA
Time Factors
Transcription Factors chemistry
Cluster Analysis
Gene Expression Regulation, Neoplastic
Lung Neoplasms genetics
Subjects
Details
- Language :
- English
- ISSN :
- 1553-7358
- Volume :
- 14
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- PLoS computational biology
- Publication Type :
- Academic Journal
- Accession number :
- 29337990
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1005896