The analysis took unexpectedly longer more than a day to run- is this usual? and surprisingly, I did not find a single significant taxon. These results made me think maybe I am not doing it right.
Can someone point me in a right direction or have any thoughts about it?
Thank you in advance for your time.
The two issues are unrelated. In the second, the issue is with your metadata and your feature table not lining up. Double check the table you're passing and your metadata.
The first tells me you're testing a lot of features, and that's why it takes so long to run. My guess based on your provenance is that you have a lot of singletons and not a lot of samples. My recommendation would be to be relatively stringent. Personally, I tend to try for 10% of samples to have a feature at whatever I've defined as "present". I would also check your beta diversity: do you see a significant difference in any of your metrics?
I double-checked everything- it is the metadata I need to pass. I used qiime feature-table summarize --i-table table.qza --m-sample-metadata-file WA_16S_0331.tsv --o-visualization table.qzv
and it turned out just fine. table.qzv (2.4 MB)
This explain it is not the metadata issue as far as I understand.
The steps I am using is
qiime feature-table filter-features --i-table table_2k.qza --p-min-frequency 50 --p-min-samples 4 --o-filtered-table table_2k_abund.qza
qiime composition add-pseudocount --i-table table_2k_abund.qza --o-composition-table table2k_abund_comp.qza
@jwdebelius looks like the command does not like the blanks in metadata. I then use Filtering data — QIIME 2 2022.2.0 documentation to filter out my MOA column in metadata which as MOA1, MOA2, and MOA3 data using qiime feature-table filter-samples --i-table table.qza --m-metadata-file WA_16S_0331.tsv --p-where "[MOA] IN ('MOA1', 'MOA2', 'MOA3')" --o-filtered-table MOA_filtered_table.qza and rerun everything again. did not work either
I'm glad you sorted out the missing metadata issue; this varies between commands and can be somewhat hard to predict.
I'd like to repeat my earlier questions, though:
Do you see a difference in beta diversity?
Do you think you're filtering enough?
The parameters from the PD mouse tutorial may not be appropriate for your data. That's a 16 sample dataset (so 4/16 = 25% prevalence) and the average average depth is around 2500 sequences/sample, so those parameters might not be appropriate for you. I would suggest looking at your filtering options in the q2-feature-table documentation directly since there are some functions there which are not covered in the tutorials.