metagenomeSeq 1.16.0: unique features give NA's for logFC / p-val?
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handibles ▴ 10
@handibles-12652
Last seen 4.3 years ago

Hey Devs,

   Thanks for the package and it's upkeep. I've hit a snag comparing two conditions (A=6 samples, B=9 samples), wherein feature counts in A = 0 or 1, counts in B =100 - 15,000, but the logFC and p-values are NA/NaN's. +samples typically are all of one group, 1-0 of the other.

   I would expect 1:15000 to be a significant signal, and can only think of one thing, the paragraph in the metagenome.pdf, which states

_"Warning: The user should restrict significant features to those with a minimum number of positive samples. What this means is that one should not claim features are significant unless the effective number of samples is above a particular percentage. For example, fold-change estimates might be unreliable if an entire group does not have a positive count for the feature in question."_

   is this what I'm seeing with this data? Can produce MRE at a later point if necessary. 

Thanks for the assist

example:

NA +samples in group 0 +samples in group 1 counts in group 0 counts in group 1 logFC se pvalues adjPvalues
AB669249 1 7 1 15584 NA NA NaN NaN
JN052751. 0 9 0 4443 NA NA NaN NaN
AB2745058 1 8 1 4311 NA NA NaN NaN
AB274517 0 9 0 2881 NA NA NaN NaN
CU918909 0 6 0 2643 NA NA NaN NaN
FN56316 0 9 0 2240 NA NA NaN NaN
CU92752 0 8 0 2023 NA NA NaN NaN
KM67594 0 9 0 1645 NA NA NaN NaN
AJ50619 1 9 1 1262 NA NA NaN NaN
CU9189 0 6 0 1236 NA NA NaN NaN
metagenomeseq • 541 views
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@joseph-nathaniel-paulson-6442
Last seen 4.6 years ago
United States

The fitFeatureModel will return NA p-values for any features where there are less than 3 positive samples in a group. The full model is not estimable otherwise.

 

Will modify this later once I have a bit more time.

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