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A cumulative Mann-Kendall trend test.Calculating p-values for monotonic trend of ordinal data sequences(e.g. proteins) by a compound test based on Kendall's rank correlation.Ref.: The DeltaProt toolbox at http://services.cbu.uib.no/software/deltaprot/Use: pval = cumKendallTest(Q,pop,tail)returns p-values for testing the hypothesis of no correlation, against the alternative that there is a non-zero correlation between the columns in the (MxN)-matrix Q and the M-vector pop.If pval is small, say less than 0.01,then the trend is considered significantly different from zero.The test treats NaNs in Q or pop as missing values, and removes them.The case N=1 is the classical Mann-Kendall test.Input:Q: MxN-matris with M independent observation seriespop: M-vector with population membership defined by an ordinalvariable e.g. optimal growth temperature for each of the M observation series.tail: The alternative hypothesis against which to compute p-values. Choices are: TAIL: Alternative Hypothesis --------------------------------- 'ne': trend is not zero (default) 'gt': trend is greater than zero 'lt': trend is less than zeroOutput:pval: P-value. Remark: - or + sign used here to indicate direction of trendcumKendall computes p-values using large-sample approximations.Unless data is very short (small sample size), this approximation is adequate.When there are ties in the data, the null distribution of Kendall's testmay not be symmetric. Computing a two-tailed p-value in such cases isnot well-defined, and p-values for the two-tailed test is computed bydoubling the more significant of the two one-tailed p-values.Please, use the following reference:Thorvaldsen, S. , FldoDE, T. and Willassen, N.P. (2010) DeltaProt: a software toolbox for comparative genomics. BMC Bioinformatics 2010, Vol 11:573.See http://www.biomedcentral.com/1471-2105/11/573
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Albina Merfyn - I find this to be really easy to use. I can...
