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Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays.

Barrera L, Benner C, Tao YC, Winzeler E, Zhou Y - BMC Bioinformatics (2004)

Bottom Line: We employ parametric and nonparametric variants of two-way analysis of variance (ANOVA) on probe-level data to account for probe-level variation, and use the false-discovery rate (FDR) to account for simultaneous testing on thousands of genes (multiple testing problem).Using publicly available data sets, we systematically compared the performance of parametric two-way ANOVA and the nonparametric Mack-Skillings test to the t-test and Wilcoxon rank-sum test for detecting differentially expressed genes at varying levels of fold change, concentration, and sample size.Our results suggest that the two-way ANOVA methods using probe-level data are substantially more powerful tests for detecting differential gene expression than corresponding methods for probe-set level data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, California 92121, USA. lbarrera@bioinf.ucsd.edu

ABSTRACT

Background: To identify differentially expressed genes across experimental conditions in oligonucleotide microarray experiments, existing statistical methods commonly use a summary of probe-level expression data for each probe set and compare replicates of these values across conditions using a form of the t-test or rank sum test. Here we propose the use of a statistical method that takes advantage of the built-in redundancy architecture of high-density oligonucleotide arrays.

Results: We employ parametric and nonparametric variants of two-way analysis of variance (ANOVA) on probe-level data to account for probe-level variation, and use the false-discovery rate (FDR) to account for simultaneous testing on thousands of genes (multiple testing problem). Using publicly available data sets, we systematically compared the performance of parametric two-way ANOVA and the nonparametric Mack-Skillings test to the t-test and Wilcoxon rank-sum test for detecting differentially expressed genes at varying levels of fold change, concentration, and sample size. Using receiver operating characteristic (ROC) curve comparisons, we observed that two-way methods with FDR control on sample sizes with 2-3 replicates exhibits the same high sensitivity and specificity as a t-test with FDR control on sample sizes with 6-9 replicates in detecting at least two-fold change.

Conclusions: Our results suggest that the two-way ANOVA methods using probe-level data are substantially more powerful tests for detecting differential gene expression than corresponding methods for probe-set level data.

Show MeSH
Number of probe sets called significant versus resampling-based FDR for all methods. Dashed lines indicate the control versus control comparisons.
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Figure 2: Number of probe sets called significant versus resampling-based FDR for all methods. Dashed lines indicate the control versus control comparisons.

Mentions: The use of a resampling-based FDR-controlling procedure decreased the number of genes called differentially expressed by nearly half when using the two-way ANOVA and Mack-Skillings tests while not significantly altering the number called when using the t-test. Despite this decrease, the two-way ANOVA methods remained more sensitive at typical levels of control such as q = 0.05 (Fig. 2). This decrease can be attributed to the extreme sensitivity of the two-way methods and the large number of genes with differential gene expression limiting the success of resampling-based methods for estimating a good distribution.


Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays.

Barrera L, Benner C, Tao YC, Winzeler E, Zhou Y - BMC Bioinformatics (2004)

Number of probe sets called significant versus resampling-based FDR for all methods. Dashed lines indicate the control versus control comparisons.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC411067&req=5

Figure 2: Number of probe sets called significant versus resampling-based FDR for all methods. Dashed lines indicate the control versus control comparisons.
Mentions: The use of a resampling-based FDR-controlling procedure decreased the number of genes called differentially expressed by nearly half when using the two-way ANOVA and Mack-Skillings tests while not significantly altering the number called when using the t-test. Despite this decrease, the two-way ANOVA methods remained more sensitive at typical levels of control such as q = 0.05 (Fig. 2). This decrease can be attributed to the extreme sensitivity of the two-way methods and the large number of genes with differential gene expression limiting the success of resampling-based methods for estimating a good distribution.

Bottom Line: We employ parametric and nonparametric variants of two-way analysis of variance (ANOVA) on probe-level data to account for probe-level variation, and use the false-discovery rate (FDR) to account for simultaneous testing on thousands of genes (multiple testing problem).Using publicly available data sets, we systematically compared the performance of parametric two-way ANOVA and the nonparametric Mack-Skillings test to the t-test and Wilcoxon rank-sum test for detecting differentially expressed genes at varying levels of fold change, concentration, and sample size.Our results suggest that the two-way ANOVA methods using probe-level data are substantially more powerful tests for detecting differential gene expression than corresponding methods for probe-set level data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, California 92121, USA. lbarrera@bioinf.ucsd.edu

ABSTRACT

Background: To identify differentially expressed genes across experimental conditions in oligonucleotide microarray experiments, existing statistical methods commonly use a summary of probe-level expression data for each probe set and compare replicates of these values across conditions using a form of the t-test or rank sum test. Here we propose the use of a statistical method that takes advantage of the built-in redundancy architecture of high-density oligonucleotide arrays.

Results: We employ parametric and nonparametric variants of two-way analysis of variance (ANOVA) on probe-level data to account for probe-level variation, and use the false-discovery rate (FDR) to account for simultaneous testing on thousands of genes (multiple testing problem). Using publicly available data sets, we systematically compared the performance of parametric two-way ANOVA and the nonparametric Mack-Skillings test to the t-test and Wilcoxon rank-sum test for detecting differentially expressed genes at varying levels of fold change, concentration, and sample size. Using receiver operating characteristic (ROC) curve comparisons, we observed that two-way methods with FDR control on sample sizes with 2-3 replicates exhibits the same high sensitivity and specificity as a t-test with FDR control on sample sizes with 6-9 replicates in detecting at least two-fold change.

Conclusions: Our results suggest that the two-way ANOVA methods using probe-level data are substantially more powerful tests for detecting differential gene expression than corresponding methods for probe-set level data.

Show MeSH