Hello!
I am so happy to finally have successfully processed my data with an ASV classifier! Thank you to all those that were a huge help along the way ( @Keegan-Evans, @jwdebelius, @lizgehret )!
However, now I am faced with the task of interpreting my results. I have x questions about this.
What exactly is meant by 'feature' as is listed as Feature ID in the rep-sequences label?
How is it that I can have, for example, 34 input sequences, 28 of which were filtered (what does filtering mean exactly?) and only 1 denoised? Does this imply that all of the remaining 6 sequences (34-28) were ASV with the exception of 1? Refer to the first row below:
I believe most of your questions are answered in the denoising lecture from our workshop videos, if there are any questions that are not answered in it, I am sure we can get you answers here!
Perfect! Thank you for this resource. After reviewing it, my questions have indeed been addressed. However, I am wondering if the fact that my frequency table has so many zero entires is concerning or not. What would you make of this?
Also, from my rep-seq.qzv it appears that there are 3,678 entries (see below) or ASVs - does this count seem low to you? I started with around 2 million demultiplexed reads.
It is totally fine, it is simply a sparse representation rather than a dense one. That is, it stores a count of each feature in each sample, regardless of whether or not it was found in that sample.
This thread has some great discussion regarding the evaluation of denoising results, unfortunately there is not really a "right" answer, but you should consider the questions raised there and be able to justify the results you use in light of these considerations sorry if that does not feel like a very helpful answer, I know there is nothing quite so unhelpful as "it depends" but in this case it really does. But your results do seem at least reasonable
Okay, I am glad it is not too shocking to you. I have started to dig around in other studies and I am seeing similar results. Its reassuring.
Thank you for all the support!
)!
sorry if that does not feel like a very helpful answer, I know there is nothing quite so unhelpful as "it depends" but in this case it really does. But your results do seem at least reasonable 