feature counts by sample? and how to do correct rarefy

[Mehrbod_Estaki]

Hi @moonlight,

@moonlight

I assign taxonomy first and check my feature table.

Unless you are using the taxa collapse action, when you assign taxonomy by the typical methods in feature-classifier you are simply creating a new taxonomy artifact, not changing your feature-table. This is an important concept to be aware of in QIIME 2, in that you are keeping these objects separate.

@moonlight

Do you think my order matters or not?

Generally speaking, removing taxa from your feature-table without a very specific reason is not a good idea. I typically do all my analyses at the ASV level and call their taxonomy when needed and avoid collapsing my ASVs down to taxonomy since you lose some resolution there.
Which taxa do you not want? Is there a specific reason why you need to remove some? I ask because, this can bias your results and is not a typical approach, unless you are removing something like chloroplast or mitochondria.

@moonlight

if you have 2X300bp data and there are 50bp overlapped, you could join them into ~550bp reads?

Yes, but you don't get to control that overlap region, rather that is dictated by the primer sets you used. If the primers flank a region that are 550 in length and you have a 2x300 bp run then yes that would mean they theoretically will merge with 50 bp overlap.

@moonlight

Can I choose not to join F and R reads. Just simply concatenate F and R reads? (I found I have a lot of reads that I can’t join)

The option of justConcatenate is not available in Qiime2 as there is in native DADA2 in R, and I would advise against doing something like this in general. See this recent thread on the topic. ITS analysis has some specific considerations that you need to be aware of, see this ITS tutorial for more details. If you are failing to merge these reads, you may consider just using your forward reads only.

@moonlight

1> How did you generate a table like this?

Please see my initial answer from above:

@Mehrbod_Estaki

If you would like the raw .biom table (like QIIME 1) to use outside of QIIME 2 for some reason, the .biom table under the hood, can be export out using this exporting tutorial.

In other words, you already have this table in this format within feature-table.qza.

@moonlight

The tutorial table means feature counts, which means how many features find in each sample. In QIIME one it called OTUs, right?

@moonlight

It can only give a table like this

Again, answered previously:

@Mehrbod_Estaki

When you use feature-table summarize, this doesn’t give you the exact content of the table but rather a summary of some useful information about the future table.

So, this is a summary of your feature-table, and in fact this holds some of the information you were asking for. For example: L4S137 has a total frequency count of 9820 across however many features. You can think of this as the total number of reads of that sample, not total number of unique features.

@moonlight

The tutorial table means feature counts, which means how many features find in each sample. In QIIME one it called OTUs, right?

No, the Feature Count value in that table corresponds to sum of all counts across all features in that sample. Yes, you can think of features, ASV, and OTUs as the same units, though they are produced differently.

@moonlight

I think we would say how many reads/seqs assign to each OTU in QIIME 1.

Sure, like I said this feature-table in QIIME2 and OTU table in QIIME 1 are the same.

@moonlight

When I do rarefy, I would like to rarefy to equal number of frequencies (reads) across samples

Yes this is in fact the only definition of rarefying and the plugin does exactly that. It randomly throws away reads (regardless of their feature designation) from each sample to reach the set threshold.

@moonlight

These 3000 features should be correlated to 30,000 reads.

Why? I'm not following this logic.

@moonlight

I don’t know how can I check the number of reads in my each sample in my final feature table

By using the feature-table summarize action as you have before. This is exactly the info you need.

@moonlight

The final feature table only logs the number of features across samples but not the number of reads across samples.

I think this is again stemming from your misconception of what Feature Count is referring to in that table. See above answers.

A couple of additional comments, a lot of the answers I've provided here are easily found in those tutorials that you have seen, again I would advise you to read through those carefully. Also, it is always a good idea to search through this forum first with regards to questions you have, in that it is very likely that the same or related question has been answered already. This also brings me to my last point, in that, in the future please limit your questions to one per thread, or at least keep all questions related that topic. If you have additional, unrelated questions, please start a new topic for those. This helps us keep the forum organized and easy to search through for questions.