Dataset Information

EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes.

ABSTRACT:

Motivation

A major goal of biomedical research is to identify molecular features associated with a biological or clinical class of interest. Differential expression analysis has long been used for this purpose; however, conventional methods perform poorly when applied to data with high within class heterogeneity.

Results

To address this challenge, we developed EMDomics, a new method that uses the Earth mover's distance to measure the overall difference between the distributions of a gene's expression in two classes of samples and uses permutations to obtain q-values for each gene. We applied EMDomics to the challenging problem of identifying genes associated with drug resistance in ovarian cancer. We also used simulated data to evaluate the performance of EMDomics, in terms of sensitivity and specificity for identifying differentially expressed gene in classes with high within class heterogeneity. In both the simulated and real biological data, EMDomics outperformed competing approaches for the identification of differentially expressed genes, and EMDomics was significantly more powerful than conventional methods for the identification of drug resistance-associated gene sets. EMDomics represents a new approach for the identification of genes differentially expressed between heterogeneous classes and has utility in a wide range of complex biomedical conditions in which sample classes show within class heterogeneity.

Availability and implementation

The R package is available at http://www.bioconductor.org/packages/release/bioc/html/EMDomics.html.

SUBMITTER: Nabavi S

PROVIDER: S-EPMC4743632 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Publications

EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes.

Nabavi Sheida S Schmolze Daniel D Maitituoheti Mayinuer M Malladi Sadhika S Beck Andrew H AH

Bioinformatics (Oxford, England) 20151029 4

<h4>Motivation</h4>A major goal of biomedical research is to identify molecular features associated with a biological or clinical class of interest. Differential expression analysis has long been used for this purpose; however, conventional methods perform poorly when applied to data with high within class heterogeneity.<h4>Results</h4>To address this challenge, we developed EMDomics, a new method that uses the Earth mover's distance to measure the overall difference between the distributions of ...[more]

PMID: 26515818

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Project description:Microarrays have been widely used for the analysis of gene expression and several commercial platforms are available. The combined use of multiple platforms can overcome the inherent biases of each approach, and may represent an alternative that is complementary to RT-PCR for identification of the more robust changes in gene expression profiles. In this paper, we combined statistical and functional analysis for the cross platform validation of two oligonucleotide-based technologies, Affymetrix (AFFX) and Applied Biosystems (ABI), and for the identification of differentially expressed genes. In this study, we analysed differentially expressed genes after treatment of an ovarian carcinoma cell line with a cell cycle inhibitor. Treated versus control RNA was analysed for expression of 16425 genes represented on both platforms. We assessed reproducibility between replicates for each platform using CAT plots, and we found it high for both, with better scores for AFFX. We then applied integrative correlation analysis to assess reproducibility of gene expression patterns across studies, bypassing the need for normalizing expression measurements across platforms. We identified 930 genes as differentially expressed on AFFX and 908 on ABI, with ~80% common to both platforms. Despite the different absolute values, the range of intensities of the differentially expressed genes detected by each platform was similar. ABI showed a slightly higher dynamic range in FC values, which might be associated with its detection system. 62/66 genes identified as differentially expressed by Microarray were confirmed by RT-PCR. In this study we present a cross-platform validation of two oligonucleotide-based technologies, AFFX and ABI. We found good reproducibility between replicates, and showed that both platforms can be used to select differentially expressed genes with substantial agreement. Pathway analysis of the affected functions identified themes well in agreement with those expected for a cell cycle inhibitor, suggesting that this procedure is appropriate to facilitate the identification of biologically relevant signatures associated with compound treatment. The high rate of confirmation found for both common and platform-specific genes suggests that the combination of platforms may overcome biases related to probe design and technical features, thereby accelerating the identification of trustworthy differentially expressed genes. accepted for publication on BMC Bioinformatics Keywords: platform comparison

Dataset Information

EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes.

Motivation

Results

Availability and implementation

Publications

EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes.

Similar Datasets

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