Streamline assay joining with joinAssays()

[cvanderaa]

Curently, joinAssays() combines multiple sets into a new set by matching rownames. However, in some cases, the rownames may not be meaningful for joining and I would prefer to join based on a rowData variable. For instance, DIANN data provides a Precuror.Id column that hold unique precursor identifier within each set. See here for example:

library(QFeatures)
x <- read.delim(MsDataHub::Report.Derks2022.plexDIA.tsv())
x[["File.Name"]] <- x[["Run"]]
qf <- readQFeaturesFromDIANN(x)
## Check if any Precursor.Id is duplicated
anyDups <- sapply(names(qf), function(i) {
    any(duplicated(rowData(qf)[[i]]$Precursor.Id))
})
table(anyDups) ## Precursor.Id is unique within each set

This means that the sets in qf could immediately be joined using joinAssays(). However, this is not possible because the rownames do not contain meaningfull information. So currently, the solution is to manually change the rownames:

for (i in names(qf)) {
    rownames(qf[[i]]) <- rowData(qf[[i]])$Precursor.Id
}

I though this could be streamlined within joinAssays() through the addition of a by argument. Eg:

qf <- joinAssays(qf, i = names(qf), name = "precursor", by = "Precursor.Id")