Closed antagomir closed 3 months ago
From those 3 suggestions, "mergeFeaturesByCluster" is already supported in a level that we want to support this "standardization", i.e., creating wrapper for this might not give extra value.
tse <- addCluster(tse, ...)
tse <- agglomerateByVariable(tse, by = "rows", f = "cluster")
The others, no immediate need now I assume.
Consider feature / sample agglomeration methods as useful dimension reduction methods.
First issue to consider is whether it is necessary to have separate functions for Features vs. Samples, or could there by just one merge function with a margin argument. In the latter case we could drop "Features" out from the function names.
These are closely related to:
Then we could have added:
cluster()
function, based on co-abundances)See https://rdrr.io/github/mikemc/speedyseq/man/tree_glom.html
Naming could follow the one suggested in #392 . Note that the TreeSummarizedExperiment package sometimes refers to the finest level features as (tree) leaves. Consider the naming on the same go.