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Motivation: Gene class testing (GCT) is a statistical approach to determine whether some functionally predefined classes of genes express differently under two experimental conditions. GCT computes the P-value of each gene class based on the null distribution and the gene classes are ranked for importance in accordance with their P-values. Currently, two null hypotheses have been considered: the Q1 hypothesis tests the relative strength of association with the phenotypes among the gene classes, and the Q2 hypothesis assesses the statistical significance. These two hypotheses are related but not equivalent., Method: We investigate three one-sided and two two-sided test statistics under Q1 and Q2. The null distributions of gene classes under Q1 are generated by permuting gene labels and the null distributions under Q2 are generated by permuting samples., Results: We applied the five statistics to a diabetes dataset with 143 gene classes and to a breast cancer dataset with 508 GO (Gene Ontology) terms. In each statistic, the null distributions of the gene classes under Q1 are different from those under Q2 in both datasets, and their rankings can be different too. We clarify the one-sided and two-sided hypotheses, and discuss some issues regarding the Q1 and Q2 hypotheses for gene class ranking in the GCT. Because Q1 does not deal with correlations among genes, we prefer test based on Q2., Supplementary Information: Supplementary data are available at Bioinformatics online.

Motivation: A microarray experiment is a multi-step process, and each step is a potential source of variation. There are two major sources of variation: biological variation and technical variation. This study presents a variance-components approach to investigating animal-to-animal, between-array, within-array and day-to-day variations for two data sets. The first data set involved estimation of technical variances for pooled control and pooled treated RNA samples. The variance components included between-array, and two nested within-array variances: between-section (the upper- and lower-sections of the array are replicates) and within-section (two adjacent spots of the same gene are printed within each section). The second experiment was conducted on four different weeks. Each week there were reference and test samples with a dye-flip replicate in two hybridization days. The variance components included week-to-week, animal-to-animal and between-array and within-array variances., Results: We applied the linear mixed-effects model to quantify different sources of variation. In the first data set, we found that the between-array variance is greater than the between-section variance, which, in turn, is greater than the within-section variance. In the second data set, for the reference samples, the week-to-week variance is larger than the between-array variance, which, in turn, is slightly larger than the within-array variance. For the test samples, the week-to-week variance has the largest variation. The animal-to-animal variance is slightly larger than the between-array and within-array variances. However, in a gene-by-gene analysis, the animal-to-animal variance is smaller than the between-array variance in four out of five housekeeping genes. In summary, the largest variation observed is the week-to-week effect. Another important source of variability is the animal-to-animal variation. Finally, we describe the use of variance-component estimates to determine optimal numbers of animals, arrays per animal and sections per array in planning microarray experiments.