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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.

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Effect of sample size on the log FDR value for representative probe sets. (a) 1024_at (b) 36085_at. Due to the precision of the Matlab routines used for this study, log FDR values below -16 were cut off at -16.
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Figure 7: Effect of sample size on the log FDR value for representative probe sets. (a) 1024_at (b) 36085_at. Due to the precision of the Matlab routines used for this study, log FDR values below -16 were cut off at -16.

Mentions: We show the effect of sample size on the resulting FDRs given by each test on some representative probe sets representing spiked genes in Fig. 7. Similar plots for all spiked probe sets are available as Supplementary Material . Note the expected decrease in FDR with increasing sample size using all methods, and the slight difference between Fig. 7(a) and Fig. 7(b) due to the effect of the maximum and minimum concentration on the absolute magnitude of the two-fold change to be detected.


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)

Effect of sample size on the log FDR value for representative probe sets. (a) 1024_at (b) 36085_at. Due to the precision of the Matlab routines used for this study, log FDR values below -16 were cut off at -16.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 7: Effect of sample size on the log FDR value for representative probe sets. (a) 1024_at (b) 36085_at. Due to the precision of the Matlab routines used for this study, log FDR values below -16 were cut off at -16.
Mentions: We show the effect of sample size on the resulting FDRs given by each test on some representative probe sets representing spiked genes in Fig. 7. Similar plots for all spiked probe sets are available as Supplementary Material . Note the expected decrease in FDR with increasing sample size using all methods, and the slight difference between Fig. 7(a) and Fig. 7(b) due to the effect of the maximum and minimum concentration on the absolute magnitude of the two-fold change to be detected.

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