A Bootstrap Correspondence Analysis for Factorial Microarray Experiments with Replications

Characterized by simultaneous measurement of the effects of experimental factors and their interactions, the economic and efficient factorial design is well accepted in microarray studies. To date, the only statistical method for analyzing microarray data obtained using factorial design has been the analysis of variance (ANOVA) model which is a gene by gene approach and relies on multiple assumptions. We introduce a multivariate approach, the bootstrap correspondence analysis (BCA), to identify and validate genes that are significantly regulated in factorial microarray experiments and show the advantages over the traditional method. Applications of our method to two microarray experiments using factorial have detected genes that are up or downregulated due to the main experimental factors or as a result of interactions. Model comparison showed that although both BCA and ANOVA capture the main regulatory profiles in the data, our multivariate approach is more efficient in identifying genes with biological and functional significances.