overLapper | R Documentation |
Function for computing Venn intersects or standard intersects among large
numbers of label sets provided as list
of vectors
. The resulting
intersect objects can be used for plotting 2-5 way Venn diagrams or intersect
bar plots using the functions vennPlot
or olBarplot
, respectively.
The overLapper
function scales to 2-20 or more label vectors for Venn
intersect calculations and to much larger sample numbers for standard
intersects. The different intersect types are explained below under the
definition of the type
argument. The upper Venn limit around 20 label
sets is unavoidable because the complexity of Venn intersects increases
exponentially with the label set number n
according to this
relationship: 2^n - 1
. The current implementation of
the plotting function vennPlot
supports Venn diagrams for 2-5 label
sets. To visually analyze larger numbers of label sets, a variety of intersect
methods are introduced in the olBarplot
help file. These methods are
much more scalable than Venn diagrams, but lack their restrictive intersect
logic.
overLapper(setlist, complexity = "default", sep = "_", cleanup = FALSE, keepdups = FALSE, type)
setlist |
Object of class |
complexity |
Complexity level of intersects specified as integer vector. For Venn intersects
it needs to be assigned |
sep |
Character used to separate set labels. |
cleanup |
If set to |
keepdups |
By default all duplicates are removed from the label sets. The setting
|
type |
With the default setting |
Additional Venn diagram resources are provided by the packages limma
,
gplots
, vennerable
, eVenn
and VennDiagram
, or
online resources such as shapes, Venn Diagram Generator and Venny.
overLapper
returns standard intersect and Venn intersect results as
INTERSECTset
or VENNset
objects, respectively. These S4 objects
contain the following components:
setlist |
Original label sets accessible with |
intersectmatrix |
Present-absent matrix accessible with |
complexitylevels |
Complexity levels accessible with |
vennlist |
Venn intersects for |
intersectlist |
Standard intersects for |
The functions provided here are an extension of the Venn diagram resources on this site: http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual#TOC-Venn-Diagrams
Thomas Girke
See examples in 'The Electronic Journal of Combinatorics': http://www.combinatorics.org/files/Surveys/ds5/VennSymmExamples.html
vennPlot
, olBarplot
## Sample data
setlist <- list(A=sample(letters, 18), B=sample(letters, 16),
C=sample(letters, 20), D=sample(letters, 22),
E=sample(letters, 18), F=sample(letters, 22))
## 2-way Venn diagram
vennset <- overLapper(setlist[1:2], type="vennsets")
vennPlot(vennset)
## 3-way Venn diagram
vennset <- overLapper(setlist[1:3], type="vennsets")
vennPlot(vennset)
## 4-way Venn diagram
vennset <- overLapper(setlist[1:4], type="vennsets")
vennPlot(list(vennset, vennset))
## Pseudo 4-way Venn diagram with circles
vennPlot(vennset, type="circle")
## 5-way Venn diagram
vennset <- overLapper(setlist[1:5], type="vennsets")
vennPlot(vennset)
## Alternative Venn count input to vennPlot (not recommended!)
counts <- sapply(vennlist(vennset), length)
vennPlot(counts)
## 6-way Venn comparison as bar plot
vennset <- overLapper(setlist[1:6], type="vennsets")
olBarplot(vennset, mincount=1)
## Bar plot of standard intersect counts
interset <- overLapper(setlist, type="intersects")
olBarplot(interset, mincount=1)
## Accessor methods for VENNset/INTERSECTset objects
names(vennset)
names(interset)
setlist(vennset)
intersectmatrix(vennset)
complexitylevels(vennset)
vennlist(vennset)
intersectlist(interset)
## Coerce VENNset/INTERSECTset object to list
as.list(vennset)
as.list(interset)
## Pairwise intersect matrix and heatmap
olMA <- sapply(names(setlist),
function(x) sapply(names(setlist),
function(y) sum(setlist[[x]] %in% setlist[[y]])))
olMA
heatmap(olMA, Rowv=NA, Colv=NA)
## Presence-absence matrices for large numbers of sample sets
interset <- overLapper(setlist=setlist, type="intersects", complexity=2)
(paMA <- intersectmatrix(interset))
heatmap(paMA, Rowv=NA, Colv=NA, col=c("white", "gray"))
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