You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Hi,
I want to use metagenomeSeq for DA analysis for a dataset that has already been normalized according to an internal standard in the library. That means, that I am interested not only in relative changes but also in absolute changes of reads (abundances) between different treatments in my experiment. I would like to know if the model implemented in fitFeatureModel() can be used with this quantitative approach of feature counts without any normalization. Since fitFeatureModel() requires the normFactors slot to be filled, would it be appropriate for my approach to add a constant integer of size equal to number of samples? (eg, normFactors(obj) <- rep(1, ncol(obj)))
Thanks
The text was updated successfully, but these errors were encountered:
Hi,
I want to use metagenomeSeq for DA analysis for a dataset that has already been normalized according to an internal standard in the library. That means, that I am interested not only in relative changes but also in absolute changes of reads (abundances) between different treatments in my experiment. I would like to know if the model implemented in
fitFeatureModel()
can be used with this quantitative approach of feature counts without any normalization. SincefitFeatureModel()
requires thenormFactors
slot to be filled, would it be appropriate for my approach to add a constant integer of size equal to number of samples? (eg,normFactors(obj) <- rep(1, ncol(obj))
)Thanks
The text was updated successfully, but these errors were encountered: