Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays.
|Title||Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays.|
|Publication Type||Journal Article|
|Year of Publication||2004|
|Authors||Barrera L, Benner C, Tao Y-C, Winzeler E, Zhou Y|
|Date Published||2004 Apr 20|
|Keywords||Analysis of Variance, Benchmarking, Cell Line, Computational Biology, DNA Probes, Fibroblasts, Gene Expression Profiling, Genetic Variation, Humans, Nucleic Acid Conformation, Oligonucleotide Array Sequence Analysis, Reproducibility of Results, Research Design, Sample Size, Sensitivity and Specificity|
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.
|Alternate Title||BMC Bioinformatics|