Primer on feature classifiers for taxonomy and use?

[Nicholas_Bokulich]

@ben

using the pre-trained classifier from the tutorial fit our work pretty well (I understand that this isn’t ideal).

If you are using the same primers, then this is ideal.

@ben

This equivalent step as I understand it is in QIIME 1.9.1 open/closed OTU picking using a greengenes.

Not exactly. Clustering and taxonomy assignment should be kept separate. Even with closed-ref OTU picking you should still attempt to reassign taxonomy, as closed-ref OTU picking is just aligning to the top hit and does not take any kind of confidence metric into account (e.g., other near-top-hits could have different taxonomic labels!). As @colinbrislawn said:

@colinbrislawn

That’s correct, clustering is first, then taxonomy assignment is a second independent step. This was also true in Qiime 1 (pick_otus.py then assign_taxonomy.py).

@ben

• If I wanted to re-train, would the training for classifiers be on an per sequence run basis? per project basis (multiple sequencing runs)? or per project (only sequences from the project samples from multiple sequencing runs)?

• If I wanted to re-train, what is the expectation on the improvement on taxonomic classification compared to pre-trained classifiers? 5%-10%? 50%?

See @colinbrislawn's excellent answers.

@ben

• Feature classifiers are all different, there are several questions as to which is the best, would evaluating each and every classifier for the experiment be important? Or is it as important as just working and sticking to a method?

Define "best". Unless if you have a mock community with known composition in your run, you do not know the true taxonomic composition and cannot assess whether one classification is better than another. So you are best any one of the classifiers in q2-feature-classifier — see here. For parameters, just use the default settings unless if you have some particular goals in mind (in which case see the alternative parameter recommendations in Table 2 of the paper you linked to).

@ben

• Once I train a feature classifier, could I reference it for other projects? Let’s say a feature classifier I train for mouse sets, could I use that same feature classifier for microbiome human data?

The classifier is trained on a specific set of reference sequences, and I imagine you will use the same reference sequences for both human and mouse samples, unless if you have some kind of host-specific reference sequences.

As Colin said:

@colinbrislawn

You need to retrain the sklearn-classifier on a per-database basis

So same database, same classifier.

Do you mean that you want to cite the classifier that you trained for a separate project? Cite the reference database and cite the classification method that you used, but don't cite the other project.

@ben

1. If I used the pre-trained classifiers, I lose a little resolution (e.g., some sequences won’t be annotated correctly because I haven’t “trained” the classifiers to my primers?)

If you used the same primers, then you have nothing to worry about. See the notes here.