5.7 Unique and overlapping features

Often an analysis batch is composed of different sample groups, such as different treatments, influent/effluent etc. In such scenarios it may be interesting to evaluate uniqueness or overlap between these samples. Furthermore, extracting overlapping or unique features is a simple but effective prioritization strategy.

The overlap() and unique() functions can be used to extract overlapping and unique features between analyses or replicates. Both functions return a subset of the given featureGroups object. An overview of their arguments is given below.

Argument	Function(s)	Remarks
which	unique(), overlap()	Which data is compared (e.g. names of replicates). Can be NULL for overlap to include all data.
aggregate	unique(), overlap()	What should be compared: analyses (aggregate=FALSE), replicates (aggregate=TRUE) or other groups of analyses based on their metadata (discussed later).
relativeTo	unique()	Uniqueness of features is ony determined by these analyses, replicates, … (NULL for all). Entries in which are ignored.
outer	unique()	If TRUE then only return features which are also unique among the compared data selected in which.
exclusive	overlap	Only keep features that only overlap between the compared data selected by which.

Some examples:

# only keep features uniquely present in replicate "repl1"
fGroupsUn1 <- unique(fGroups, which = "repl1", aggregate = TRUE)
# only keep features in repl1/repl2 which are not in repl3
fGroupsUn2 <- unique(fGroups, which = c("repl1", "repl2"), aggregate = TRUE,
                     relativeTo = "repl3")
# only keep features that are only present in repl1 OR repl2
fGroupsUn3 <- unique(fGroups, which = c("repl1", "repl2"), aggregate = TRUE,
                     outer = TRUE)

# only keep features overlapping in repl1/repl2
fGroupsOv1 <- overlap(fGroups, which = c("repl1", "repl2"), aggregate = TRUE)
# only keep features overlapping in repl1/repl2 AND are not present in any other
# replicate
fGroupsOv2 <- overlap(fGroups, which = c("repl1", "repl2"), aggregate = TRUE,
                      exclusive = TRUE)

Note that several plotting functions that are discussed in the visualization section can also assist in determining overlap and uniqueness of features.