Dataset Information

Integration of partial least squares and Monte Carlo gene expression analysis in coronary artery disease.

ABSTRACT: Coronary artery disease (CAD) is the most common type of cardiovascular disease and leading cause of mortality worldwide. Microarray technology for gene expression analysis has facilitated the identification of the molecular mechanism that underlies the pathogenesis of CAD. Previous studies have primarily used variance or regression analysis, without considering array specific factors. Thus, the aim of the present study was to investigate the mechanism of CAD using partial least squares (PLS)-based analysis, which was integrated with the Monte Carlo technique. Microarray analysis was performed with a data set of 110 CAD patients and 111 controls obtained from the Gene Expression Omnibus database. A total of 390 dysregulated genes were acquired. Significantly increased representations of dysregulated genes in Gene Ontology items, including transforming growth factor ?-activated receptor activity and acyl-CoA oxidase activity, were identified. Network analysis revealed three hub genes with a degree of >10, including ESR1, ITGA4 and ARRB2. The results of the present study provide novel information on the gene expression signatures of CAD patients and offer further theoretical support for future therapeutic study.

SUBMITTER: Zhang H

PROVIDER: S-EPMC3991499 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Integration of partial least squares and Monte Carlo gene expression analysis in coronary artery disease.

Zhang Huan H Li Tao T Wu Guanji G Ma Feng F

Experimental and therapeutic medicine 20140307 5

Coronary artery disease (CAD) is the most common type of cardiovascular disease and leading cause of mortality worldwide. Microarray technology for gene expression analysis has facilitated the identification of the molecular mechanism that underlies the pathogenesis of CAD. Previous studies have primarily used variance or regression analysis, without considering array specific factors. Thus, the aim of the present study was to investigate the mechanism of CAD using partial least squares (PLS)-ba ...[more]

PMID: 24940402

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Project description:High-throughput gene expression data are often obtained from pure or complex (heterogeneous) biological samples. In the latter case, data obtained are a mixture of different cell types and the heterogeneity imposes some difficulties in the analysis of such data. In order to make conclusions on gene expresssion data obtained from heterogeneous samples, methods such as microdissection and flow cytometry have been employed to physically separate the constituting cell types. However, these manual approaches are time consuming when measuring the responses of multiple cell types simultaneously. In addition, exposed samples, on many occasions, end up being contaminated with external perturbations and this may result in an altered yield of molecular content. In this paper, we model the heterogeneous gene expression data using a Bayesian framework, treating the cell type proportions and the cell-type specific expressions as the parameters of the model. Specifically, we present a novel sequential Monte Carlo (SMC) sampler for estimating the model parameters by approximating their posterior distributions with a set of weighted samples. The SMC framework is a robust and efficient approach where we construct a sequence of artificial target (posterior) distributions on spaces of increasing dimensions which admit the distributions of interest as marginals. The proposed algorithm is evaluated on simulated datasets and publicly available real datasets, including Affymetrix oligonucleotide arrays and national center for biotechnology information (NCBI) gene expression omnibus (GEO), with varying number of cell types. The results obtained on all datasets show a superior performance with an improved accuracy in the estimation of cell type proportions and the cell-type specific expressions, and in addition, more accurate identification of differentially expressed genes when compared to other widely known methods for blind decomposition of heterogeneous gene expression data such as Dsection and the nonnegative matrix factorization (NMF) algorithms. MATLAB implementation of the proposed SMC algorithm is available to download at https://github.com/moyanre/smcgenedeconv.git.

Dataset Information

Integration of partial least squares and Monte Carlo gene expression analysis in coronary artery disease.

Publications

Integration of partial least squares and Monte Carlo gene expression analysis in coronary artery disease.

Similar Datasets

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