At the top, you can select which values to analyze (see Selecting transformed and normalized values for analysis).
Below you can select to add two kinds of corrected p-values to the analysis (in addition to the standard p-value produced for the test statistic):
- Bonferroni corrected.
- FDR corrected.
The Bonferroni corrected p-values handle the multiple testing problem by controlling the 'family-wise error rate': the probability of making at least one false positive call. They are calculated by multiplying the original p-values by the number of tests performed. The probability of having at least one false positive among the set of features with Bonferroni corrected p-values below 0.05, is less than 5%. The Bonferroni correction is conservative: there may be many genes that are differentially expressed among the genes with Bonferroni corrected p-values above 0.05, that will be missed if this correction is applied.
Instead of controlling the family-wise error rate we can control the false discovery rate: FDR. The false discovery rate is the proportion of false positives among all those declared positive. We expect 5 % of the features with FDR corrected p-values below 0.05 to be false positive. There are many methods for controlling the FDR - the method used in CLC Genomics Workbench is that of [Benjamini and Hochberg, 1995].
Click Finish to start the tool.
Note that if you have already performed statistical analysis on the same values, the existing one will be overwritten.