The best people to answer this question are probably @cmartino and @mortonjt, who developed DEICODE and Songbird respectively! Since they’re currently unavailable, you get me instead
l would like to know whether DEIcode can provide a list of features rank like differential.tsv in songbird.
The answer is: sort of! When you run DEICODE, you get an
ordination file (along with a distance matrix).1 The
ordination file contains sample loadings and feature loadings. The feature loadings are similar to the
differential output you get from Songbird, in that you can rank them from smallest to highest for each feature. The general takeaway from these feature loadings is that highly-ranked or very lowly-ranked features seem to be somehow associated with variation in the dataset.
The DEICODE paper (should be open access ) goes into detail about these loadings: see figure 2F and figure 5 for examples of looking at feature loadings. If you want to do this sort of thing yourself—looking at your feature loadings in order to compare log-ratios of features with sample metadata—this is possible using Qurro (in QIIME 2 this is possible through the
qiime qurro loading-plot visualizer). (Qurro doesn’t support visualizing DEICODE sample loadings alongside feature log-ratios yet, like in fig. 5 of the DEICODE paper, but that’s an open issue I’d like to add eventually.)
Of course, something to note is that DEICODE doesn’t know anything about your sample metadata (all you pass in to DEICODE when you run
qiime deicode rpca is a feature table)—so unlike Songbird differentials, which are generated using the feature table along with the
formula and sample metadata you pass in, the feature loadings in DEICODE output don’t necessarily mean anything about your sample metadata.
1 You can use this ordination file to create a fancy biplot in Emperor, if you want! It’s possible to combine this with a Qurro visualization of the feature loadings; see the Qurro tutorial for an example.
l would like to know whether differential rank by songbird is not appropriate dealing with table.qza which is collapsed to genus level as deicode.
I’m not an expert here, but my feeling is that it is best to use the uncollapsed table for Songbird. I do know that DEICODE explicitly recommends against using collapsed tables, and if you want to do stuff like compare your DEICODE and Songbird results then it definitely seems best to just use uncollapsed tables for both.
ls there another way to pick out meaningful ASVs from rank results apart from the top five or bottom five，you know they are always exists.
This is a complicated question! Generally speaking, these high-ranked/low-ranked features (in the context of DEICODE feature loadings or Songbird differentials) are ranked that way because they seem to be somehow associated with variation in some way. I guess you could filter out the top/bottom ranked features and then rerun DEICODE or Songbird, but I wouldn’t recommend doing that.
For further information about how to interpret these rankings, I’d recommend looking over the Songbird paper (should also be open access ) – in particular, I’ve found the section on “Interpreting ranks” useful when looking at feature rankings.
This isn’t super related to the “ranking” side of things, but you might also want to check out this cool open-access paper, which proposes a different way of automatically selecting log-ratios of features.
Hope this helps answer some of your questions!