Multi-sample test for determining if any of the means of several groups are significantly different from each other.
Output: A numerical columns is added containing the p value. In addition there is a categorical column added in which it is indicated by a '+' when the row is significant with respect to the specified criteria.
Selected categorical row that defines the grouping of columns that should be used in the test (default: first categorical row in the matrix).
Defines what kind of test should be applied (default: ANOVA). The test can be selected from a predefined list:
Artificial within groups variance (default: 0). It controls the relative importance of t-test p-value and difference between means. At s0=0 only the p-value matters, while at nonzero s0 also the difference of means plays a role. See Tusher, Tibshirani and Chu (2001) PNAS 98, pp5116-21 for details.
Defines on what value the truncation is based on (default: Permutation-based FDRFalse Discovery Rate). Choose here whether the truncation should be based on the p-values, on permutation-based FRD values or, if the Benjamini-Hochberg correction for multiple hypothesis testing should be applied.
This parameter is just relevant, if the parameter “Use for truncation” is set to “P-value”. Rows with a test result below this value are reported as significant (default: 0.05).
Specifies the number of randomizations that should be applied (default: 250).
Defines, whether the grouping specified in a categorical row should be preserved in the randomizations (default: <None>). It can be selected from a list including all available groupings of the matrix.
If checked, -Log10(test value) is reported in the output matrix (default). Otherwise the test-value is reported.
The entered suffix will be attached to newly generated columns (default: empty). That way columns from multiple runs of the test can be distinguished more easily.