5.5 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 highly 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 replicate groups, respectively. 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()	The replicate groups to compare.
relativeTo	unique()	Only return unique features compared to these replicate groups (NULL for all). Replicate groups in which are ignored.
outer	unique()	If TRUE then only return features which are also unique among the compared replicates groups.
exclusive	overlap	Only keep features that only overlap between the compared replicate groups.

Some examples:

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

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

In addition, several plotting functions are discussed in the visualization section that visualize overlap and uniqueness of features.