Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments

• Published in: FEBS letters Vol. 573; no. 1; pp. 83 - 92
• Main Authors: Breitling, Rainer, Armengaud, Patrick, Amtmann, Anna, Herzyk, Pawel
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 27.08.2004
• Subjects: Acute Disease; Algorithms; Animals; Arabidopsis - drug effects; Arabidopsis - genetics; Differentially expressed gene; FC, fold-change; Gene Expression Profiling - methods; Gene Expression Regulation - genetics; Genes - genetics; HDL, high-density lipoprotein; Humans; Leukemia - genetics; Lipoproteins, HDL - deficiency; Mice; Microarray analysis; Models, Animal; Oligonucleotide Array Sequence Analysis - methods; Potassium - pharmacology; Reproducibility of Results; Research Design; RNA, Messenger - analysis; RNA, Messenger - genetics; RP, rank product; SAM, significance analysis of microarrays; RP, rank product; FC, fold-change; Differentially expressed gene; HDL, high-density lipoprotein; SAM, significance analysis of microarrays; Microarray analysis
• ISSN: 0014-5793; 1873-3468
• DOI: 10.1016/j.febslet.2004.07.055

One of the main objectives in the analysis of microarray experiments is the identification of genes that are differentially expressed under two experimental conditions. This task is complicated by the noisiness of the data and the large number of genes that are examined simultaneously. Here, we present a novel technique for identifying differentially expressed genes that does not originate from a sophisticated statistical model but rather from an analysis of biological reasoning. The new technique, which is based on calculating rank products (RP) from replicate experiments, is fast and simple. At the same time, it provides a straightforward and statistically stringent way to determine the significance level for each gene and allows for the flexible control of the false-detection rate and familywise error rate in the multiple testing situation of a microarray experiment. We use the RP technique on three biological data sets and show that in each case it performs more reliably and consistently than the non-parametric t-test variant implemented in Tusher et al.'s significance analysis of microarrays (SAM). We also show that the RP results are reliable in highly noisy data. An analysis of the physiological function of the identified genes indicates that the RP approach is powerful for identifying biologically relevant expression changes. In addition, using RP can lead to a sharp reduction in the number of replicate experiments needed to obtain reproducible results.