Rank product 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 .Calculation of the rank product Given n genes and k replicates, let the rank of gene g in the i -th replicate. Compute the rank product via the geometric mean:Determination of significance levels Simple permutation-based estimation is used to determine how likely a given RP value or better is observed in a random experiment . generate p permutations of k rank lists of length n . calculate the rank products of the n genes in the p permutations. count how many times the rank products of the genes in the permutations are smaller or equal to the observed rank product. Set c to this value. calculate the average expected value for the rank product by:. calculate the percentage of false positives as : where is the rank of gene g in a list of all n genes sorted by increasing. 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 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 thin right tail . However, exact statistical significance of large rank products may take unacceptable long amounts of time to compute. Heskes, Eisinga and Breitling provide a method to determine accurate approximate p -values of the rank product statistic in a computationally fast manner.
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