Hi, I am using the Moving Pictures tutorial as a guidelines for analyzing my microbiome data. When trying to perform the ANCOM analysis at the end, I get strange results for ANCOM. When I look at the results filtered to level 2, all W values are 0 (none significantly different). I don't necessaril…

Hi @adrian , thanks for writing! I am going to ping @mortonjt on this one, he might have some thoughts on the matter. Thanks!

I not exactly sure what is going on - would you like to attach your dataset? There are many possibilities that could explain this, namely Low resolution from level taxonomy summarization could obscure the signal, particularly if only one species is changing Low counts / zeros could cause false po…

Thanks for replying! I attached the relevant .qzv files- is that informative or did you want another form/step of data? Filtered for only female (results I detailed above): l6-ancom-Treatment-female.qzv (35.4 KB) l2-ancom-Treatment-female.qzv (32.1 KB) Not to make this more complicated, but I al…

Thanks @adrian . Could you also send over the metadata and the tables? That way, we can sanity check to see how many zero / low count entries there are.

Here you go, thank you for looking into this! metadata.tsv (1.7 KB) female-filtered-table.qza (91.0 KB) femaletable.qzv (401.6 KB) Full Set: table.qza (134.3 KB) table.qzv (429.2 KB)

@mortonjt , we are also seeing this "lots-o-zeros" situation crop up on a dataset that we are currently analyzing. Feel free to ping me directly to coordinate, if you wish. Thanks!

Ok, here's a summary of your data in table.qzv In [1]: import qiime2 In [2]: table = qiime2.Artifact('table.qza').view(pd.DataFrame) In [3]: (table>0).sum(axis=0).sort_values().value_counts() Out[28]: 1 1343 2 411 3 271 4 175 5 119 6 98 7 61 8 44 24 4…

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ANCOM giving strange W values

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