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The rank product is a biologically motivated test for the detection of differentially expressed genes in replicated microarray experiments. It is a simple non-parametric statistical method based on ranks of fold changes. In addition to its use in expression profiling, it can be used to combine ranked lists in various application domains, including proteomics, metabolomics, statistical meta-analysis, and general feature selection.
Given n genes and k replicates, let e_{g,i} be the fold change and r_{g,i} the rank of gene g in the i-th replicate.
Compute the rank product via the geometric mean:
Simple permutation-based estimation is used to determine how likely a given RP value or better is observed in a random experiment.
Permutation re-sampling requires a computationally demanding number of permutations to get reliable estimates of the p-values for the most differentially expressed genes, if n is large. Eisinga, Breitling and Heskes (2013) provide the exact probability mass distribution of the rank product statistic. Calculation of the exact p-values offers a substantial improvement over permutation approximation, most significantly for that part of the distribution rank product analysis is most interested in, i.e., the tin right tail. However, exact statistical significance of large rank products may take unacceptable long amounts of time to compute. Heskes, Eisinga and Breitling (2014) provide a method to determine accurate approximate p-values of the rank product statistic in a computationally fast manner.
Mass spectrometry, Genomics, Bioinformatics, Systems biology, Elisa
Statistics, Effect size, Systematic review, Regression analysis, Epidemiology
Skin, Machine learning, Molecular biology, Gene expression, Cytochrome P450
Statistics, Parametric statistics, Probability distribution, Statistical inference, Parameter