Hello, I am trying to get a relative abundance table similar to summarize_taxa.py in QIIME1 where each sample (and the metadata for each sample is included) has the relative abundance of each taxa. The answers I have seen on the forum get me part way, but not all the way there or maybe I am missing…

Hi @kmkalanetra --- I might be misunderstanding your question, but have you had a chance to look at taxa barplot --- specifically the CSV export? [52 PM] The CSV looks like this: index,Unassigned,k__Archaea,k__Bacteria,BarcodeSequence,LinkerPrimerSequence,BodySite,Year,Month,Day,Subject,Reported…

Hi @kmkalanetra , I think you're looking for qiime taxa collapse . But I'm not sure what you mean by this: [image] kmkalanetra: where each sample (and the metadata for each sample is included) has the relative abundance of each taxa Specifically the metadata part. Could you elaborate a little…

Hi Evan, Thank you for your response. I have used qiime taxa collapse, but it does not compile the information exactly the same way as summarize_taxa.py - so if I want a spreadsheet that I can use with something like LEfSe how would I make that in QIIME2? What I'm talking about is a spreadsheet wit…

Hey @kmkalanetra , I think this post should accomplish what you need! Although you'll probably need to transpose it in your favourite spreadsheet program to match your screenshot. Also if it's important that the data be relative abundance, then you can use qiime feature-table relative-frequency to c…

Hi, That worked, but the result doesn't include taxonomic IDs, but OTU IDs. For me, part of the strength of the results of the summariza_taxa.py was the way the taxonomy was broken down. Is there a way to get this information in combination with the feature-table? [03 PM] Thank you! Karen

No, that's exactly what I was looking for. Thank you!

I do have to say that I still like the taxa format of the summarize_taxa.py better than this way. Just saying........ :slight_smile: Thanks for everyone's help, Karen

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How do I get a table of frequencies of each feature per sample?

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