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Robust microarray meta-analysis identifies differentially expressed genes for clinical prediction.

Phan JH, Young AN, Wang MD - ScientificWorldJournal (2012)

Bottom Line: Combining multiple microarray datasets increases sample size and leads to improved reproducibility in identification of informative genes and subsequent clinical prediction.Although microarrays have increased the rate of genomic data collection, sample size is still a major issue when identifying informative genetic biomarkers.Results indicate that rank average meta-analysis consistently performs well compared to five other meta-analysis methods.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.

ABSTRACT
Combining multiple microarray datasets increases sample size and leads to improved reproducibility in identification of informative genes and subsequent clinical prediction. Although microarrays have increased the rate of genomic data collection, sample size is still a major issue when identifying informative genetic biomarkers. Because of this, feature selection methods often suffer from false discoveries, resulting in poorly performing predictive models. We develop a simple meta-analysis-based feature selection method that captures the knowledge in each individual dataset and combines the results using a simple rank average. In a comprehensive study that measures robustness in terms of clinical application (i.e., breast, renal, and pancreatic cancer), microarray platform heterogeneity, and classifier (i.e., logistic regression, diagonal LDA, and linear SVM), we compare the rank average meta-analysis method to five other meta-analysis methods. Results indicate that rank average meta-analysis consistently performs well compared to five other meta-analysis methods.

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Related in: MedlinePlus

Study design diagram. We compare the predictive performance of meta-analysis-based feature selection (FS) methods by designing a study that considers five components: (1) basic FS methods that are the building blocks of some of the meta-analysis methods, (2) meta-analysis-based FS methods, (3) clinical application, (4) microarray data platform, and (5) classifier (logistic regression, diagonal LDA and linear SVM). Since the “best” meta-analysis-based FS method may be dataset- or application-specific, assessing performance over a wide variety of factors enables an evaluation of the method's robustness.
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fig1: Study design diagram. We compare the predictive performance of meta-analysis-based feature selection (FS) methods by designing a study that considers five components: (1) basic FS methods that are the building blocks of some of the meta-analysis methods, (2) meta-analysis-based FS methods, (3) clinical application, (4) microarray data platform, and (5) classifier (logistic regression, diagonal LDA and linear SVM). Since the “best” meta-analysis-based FS method may be dataset- or application-specific, assessing performance over a wide variety of factors enables an evaluation of the method's robustness.

Mentions: We develop the rank average method, a simple meta-analysis-based FS method, for identifying DEGs from multiple microarray datasets and design a study (Figure 1) to compare rank average to five other meta-analysis-based FS methods. We focus on the predictive ability of genes emerging from meta-analysis and show that rank average meta-analysis is robust with respect to three factors. These three factors are (1) clinical application (i.e., breast, renal, and pancreatic cancer diagnosis or subtyping), (2) data platform heterogeneity (i.e., combining different microarray platforms), and (3) classifier. Using a comprehensive factorial analysis, we rate each meta-analysis-based FS method relative to its peers. In terms of identifying genetic features with reproducible predictive performance and in terms of robustness to multiple factors, results indicate that rank average meta-analysis performs consistently well in comparison to five other meta-analysis-based FS methods.


Robust microarray meta-analysis identifies differentially expressed genes for clinical prediction.

Phan JH, Young AN, Wang MD - ScientificWorldJournal (2012)

Study design diagram. We compare the predictive performance of meta-analysis-based feature selection (FS) methods by designing a study that considers five components: (1) basic FS methods that are the building blocks of some of the meta-analysis methods, (2) meta-analysis-based FS methods, (3) clinical application, (4) microarray data platform, and (5) classifier (logistic regression, diagonal LDA and linear SVM). Since the “best” meta-analysis-based FS method may be dataset- or application-specific, assessing performance over a wide variety of factors enables an evaluation of the method's robustness.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3539384&req=5

fig1: Study design diagram. We compare the predictive performance of meta-analysis-based feature selection (FS) methods by designing a study that considers five components: (1) basic FS methods that are the building blocks of some of the meta-analysis methods, (2) meta-analysis-based FS methods, (3) clinical application, (4) microarray data platform, and (5) classifier (logistic regression, diagonal LDA and linear SVM). Since the “best” meta-analysis-based FS method may be dataset- or application-specific, assessing performance over a wide variety of factors enables an evaluation of the method's robustness.
Mentions: We develop the rank average method, a simple meta-analysis-based FS method, for identifying DEGs from multiple microarray datasets and design a study (Figure 1) to compare rank average to five other meta-analysis-based FS methods. We focus on the predictive ability of genes emerging from meta-analysis and show that rank average meta-analysis is robust with respect to three factors. These three factors are (1) clinical application (i.e., breast, renal, and pancreatic cancer diagnosis or subtyping), (2) data platform heterogeneity (i.e., combining different microarray platforms), and (3) classifier. Using a comprehensive factorial analysis, we rate each meta-analysis-based FS method relative to its peers. In terms of identifying genetic features with reproducible predictive performance and in terms of robustness to multiple factors, results indicate that rank average meta-analysis performs consistently well in comparison to five other meta-analysis-based FS methods.

Bottom Line: Combining multiple microarray datasets increases sample size and leads to improved reproducibility in identification of informative genes and subsequent clinical prediction.Although microarrays have increased the rate of genomic data collection, sample size is still a major issue when identifying informative genetic biomarkers.Results indicate that rank average meta-analysis consistently performs well compared to five other meta-analysis methods.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.

ABSTRACT
Combining multiple microarray datasets increases sample size and leads to improved reproducibility in identification of informative genes and subsequent clinical prediction. Although microarrays have increased the rate of genomic data collection, sample size is still a major issue when identifying informative genetic biomarkers. Because of this, feature selection methods often suffer from false discoveries, resulting in poorly performing predictive models. We develop a simple meta-analysis-based feature selection method that captures the knowledge in each individual dataset and combines the results using a simple rank average. In a comprehensive study that measures robustness in terms of clinical application (i.e., breast, renal, and pancreatic cancer), microarray platform heterogeneity, and classifier (i.e., logistic regression, diagonal LDA, and linear SVM), we compare the rank average meta-analysis method to five other meta-analysis methods. Results indicate that rank average meta-analysis consistently performs well compared to five other meta-analysis methods.

Show MeSH
Related in: MedlinePlus