Dataset Information

Phantom: investigating heterogeneous gene sets in time-course data.

ABSTRACT:

Motivation

Gene set analysis is a powerful tool to study the coordinative change of time-course data. However, most existing methods only model the overall change of a gene set, yet completely overlooked heterogeneous time-dependent changes within sub-sets of genes.

Results

We have developed a novel statistical method, Phantom, to investigate gene set heterogeneity. Phantom employs the principle of multi-objective optimization to assess the heterogeneity inside a gene set, which also accounts for the temporal dependency in time-course data. Phantom improves the performance of gene set based methods to detect biological changes across time.

Availability and implementation

Phantom webpage can be accessed at: http://www.baylorhealth.edu/Phantom . R package of Phantom is available at https://cran.r-project.org/web/packages/phantom/index.html .

Contact

jinghua.gu@bswhealth.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Gu J

PROVIDER: S-EPMC5870667 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Publications

Phantom: investigating heterogeneous gene sets in time-course data.

Gu Jinghua J Wang Xuan X Chan Jinyan J Baldwin Nicole E NE Turner Jacob A JA

Bioinformatics (Oxford, England) 20170901 18

<h4>Motivation</h4>Gene set analysis is a powerful tool to study the coordinative change of time-course data. However, most existing methods only model the overall change of a gene set, yet completely overlooked heterogeneous time-dependent changes within sub-sets of genes.<h4>Results</h4>We have developed a novel statistical method, Phantom, to investigate gene set heterogeneity. Phantom employs the principle of multi-objective optimization to assess the heterogeneity inside a gene set, which a ...[more]

PMID: 28595310

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Project description:BACKGROUND:Longitudinally collected gene expression data provides an opportunity to investigate the dynamic behavior of gene expression and is crucial for establishing causal links between changes on a molecular level and disease development and progression. In terms of the analysis of such data, clustering of subjects based on time-course expression data may improve our understanding of temporal expression patterns that result in disease phenotypes. Although there are numerous existing methods for clustering subjects using gene expression data, most are not suitable when expression measurements are repeatedly collected over a time-course. METHODS:We present a modified version of the recursively partitioned mixture model (RPMM) for clustering subjects based on longitudinally collected gene expression data. In the proposed time-course RPMM (TC-RPMM), subjects are clustered on the basis of their temporal profiles of gene expression using a mixture of mixed effects models framework. This framework captures changes in gene expression over time and models the autocorrelation between repeated gene expression measurements for the same subject. We assessed the performance of TC-RPMM using extensive simulation studies and a dataset from a multi-center research study of inflammation and response to injury (www.gluegrant.org), which consisted of time-course gene expression data for 140 subjects. RESULTS:Our simulation studies encompassed several different scenarios and were aimed at assessing the ability of TC-RPMM to correctly recover true class memberships when the expression trajectories that characterized those classes differed. Overall, our simulation studies revealed favorable performance of TC-RPMM compared to competing approaches, however clustering performance was observed to be highly dependent on the proportion of class discriminating genes used in clustering analysis. When applied to real epidemiologic data with repeated-measures, longitudinal gene expression measurements, TC-RPMM identified clusters that had strong biological and clinical significance. CONCLUSIONS:Methods for clustering subjects based on temporal gene expression profiles is a high priority for molecular biology and bioinformatics research. Along these lines, the proposed TC-RPMM represents a promising new approach for analyzing time-course gene expression data.

Dataset Information

Phantom: investigating heterogeneous gene sets in time-course data.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

Phantom: investigating heterogeneous gene sets in time-course data.

Similar Datasets

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