Dsfdr numpy fix #312
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Dsfdr numpy fix #312
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There is a problem with the matrix multiplication used for for the fast mean calculation in dsFDR (using the default method='meandiff'). This results in small (1E-9) differences in means that should be the same. This problem is then propagated to the dsFDR calculation (that relies on exact equality for the ranking), which eventually leads to dsFDR not ignoring enough the features present in a very small number of samples (i.e. reducing the power).
To fix this, we normalize and round the means to 9 decimal points before performing the dsFDR routine.
Also added a new test to validate the correctness by comparing to the slower (and more exact) method=ca.dsfdr.meandiff