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Identification of differentially expressed gene modules between two-class DNA microarray data.

ABSTRACT: Identifying biologically useful genes from massive gene expression data is a critical issue in DNA microarray data analysis. Recent studies on gene module discovery have shown a substantial effect on identifying transcriptional regulatory networks involved in complex diseases for different sample subsets. These have targeted a single disease class, but discovering discriminative modules in different classes has remained to be addressed. In this paper, we propose a novel method that can discover differentially expressed gene modules from two-class DNA microarray data. The proposed method is applied to breast cancer and leukemia datasets, and the biological functions of the extracted modules are evaluated by functional enrichment analysis. As a result, we show that our method can extract genes well reflecting known biological functions compared to a traditional t-test-based approach.

SUBMITTER: Okada Y

PROVIDER: S-EPMC2825599 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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Identification of differentially expressed gene modules between two-class DNA microarray data.

Okada Yoshifumi Y Inoue Terufumi T

Bioinformation 20091011 4

Identifying biologically useful genes from massive gene expression data is a critical issue in DNA microarray data analysis. Recent studies on gene module discovery have shown a substantial effect on identifying transcriptional regulatory networks involved in complex diseases for different sample subsets. These have targeted a single disease class, but discovering discriminative modules in different classes has remained to be addressed. In this paper, we propose a novel method that can discover ...[more]

PMID: 20198188

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Project description:BackgroundMicroarrays 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.ResultsIn 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 approximately 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.ConclusionIn 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.

Dataset Information

Identification of differentially expressed gene modules between two-class DNA microarray data.

Publications

Identification of differentially expressed gene modules between two-class DNA microarray data.

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