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Quinnr,
All features are seen in the results table which pass the minfrac cut off. Minfrac is a robustness filter to make sure that the feature is seen reproducibly across the class group. If minfrac is set at 50% (ie 0.5) then we have to see feature X in at least 50% of the samples for class 1. For example, if we have 3 classes WT, MUT-1, MUT-2. Each class has 6 samples each. A feature will appear in the report if the feature is seen/detected in at least 3 samples (50%) of any one class. So, we could see feature X in the samples of WT 3 times but not see them in the MUT-1 samples or the MUT-2 samples and the feature X would still appear in the report. However, if feature Y was seen 2 times in the samples of class WT, 1 sample of MUT-1 and in 2 samples of MUT-2 then it would not pass the minfrac filter.
Hope this helps and sorry for the delay in response.
Paul
Chantal,
If you use the downloaded results, there is a column called 'npeaks' next to this column is each class label. You can then uses these column to filter out the features that are present in just one class, two classes etc. You can of course start to filter with more filters eg a p-value and or fold change filter as well.
Cheers,
Paul