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Analysis methods, like TS_TopFeatures, assume that there are no errors in the data matrix (i.e., that all bad values have been filtered out of the dataset, using TS_normalize). There should be better checks on this, to avoid the zeros in TS_DataMat being treated as actual zeros (rather than error symbols in TS_Quality. Best solution would be to use data in TS_Quality to restrict the computation to good values (where meaningful analysis is possible), e.g., in the case of TS_TopFeatures.
The text was updated successfully, but these errors were encountered:
Analysis methods, like
TS_TopFeatures
, assume that there are no errors in the data matrix (i.e., that all bad values have been filtered out of the dataset, usingTS_normalize
). There should be better checks on this, to avoid the zeros inTS_DataMat
being treated as actual zeros (rather than error symbols inTS_Quality
. Best solution would be to use data inTS_Quality
to restrict the computation to good values (where meaningful analysis is possible), e.g., in the case ofTS_TopFeatures
.The text was updated successfully, but these errors were encountered: