alopgar
commented
5 years ago Hello. I just ran into the same issue and saw this open post.
When I use the MRfulltable function with that eff = .5 argument, the resultin table has all my features.
But when I use this code:
ZIG <- fitZig(obj = normtest, mod = mod, control = settings, useCSSoffset = F)
efec <- calculateEffectiveSamples(ZIG)
valid = which(efec >= quantile(efec, p = 0.5, na.rm = TRUE))
The resulting output filters me around half of the samples.
Is there any clue about what could be happening? Thanks in advance.
jeffkimbrel
Greetings to the devs.
From the documentation:
I've tried a number of values for the eff='x' argument, which should filter out features represented in less than 'x' samples: however the output of MRcoef etc. shows features with any number (1 and up) of effective samples.
Additionally, I've tried excising features with less than my threshold number of samples:
This reduces the number of features, and accordingly calculateEffectiveSamples on the respective fit shows values above my threshold - however, when I export this object via MRfulltable, I again have features (700 out of 1700) with +samples below the threshold (+samples <6).
This is reproducible (but to a lesser extent) with the lungData set also, running the code as per page 16 and then calling:
write.table(MRfulltable(lungres1, **eff=0.5**, number=2000), 'lung.tsv', sep='\t' )This gives features with sample counts from 30 upwards, although the lungData set has 78 samples.
The only thing I can think of (other than being plain wrong) is that standardisation via ZIG is bringing some border-line abundances below 0, leaving those samples less effective?