Clustering gene expression time series data using an infinite Gaussian process mixture model.
PLoS Comput Biol
; 14(1): e1005896, 2018 01.
Article
em En
| MEDLINE
| ID: mdl-29337990
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Análise por Conglomerados
/
Regulação Neoplásica da Expressão Gênica
/
Neoplasias Pulmonares
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article