VennDetail An R package for visualizing and extracting details of multi-sets intersection
Visualizing and extracting unique (disjoint) or overlapping subsets of multiple gene datasets are a frequently performed task for bioinformatics. Although various packages and web applications are available, no R package offering functions to extract and combine details of these subsets with user datasets in data frame is available. Moreover, graphical visualization is usually limited to six or less gene datasets and a novel method is required to properly show the subset details.We have developed VennDetail, an R package to generate high-quality Venn-Pie charts and to allow extraction of subset details from input datasets.
The package can be installed as
if (!requireNamespace("BiocManager"))
install.packages("BiocManager")
`BiocManager::install("VennDetail")
library(VennDetail)
data(T2DM)
T2DM data include three sets of differentially expressed genes (DEGs) from the publication by Hinder et al [1]. The three DEG datasets were obtained in three different tissues, kidney Cortex, kidney glomerula, and sciatic nerve, by comparing db/db diabetic mice and db/db mice with pioglitazone treatment. Differential expression was determined by using Cuffdiff with a false discovery rate (FDR) < 0.05.
ven <- venndetail(list(Cortex = T2DM$Cortex$Entrez, SCN = T2DM$SCN$Entrez,
Glom = T2DM$Glom$Entrez))
VennDetail supports three different types of Venn diagram display formats
##traditional venn diagram
plot(ven)
##Venn-Pie format
plot(ven, type = "vennpie")
## Warning: `select_()` was deprecated in dplyr 0.7.0.
## Please use `select()` instead.
## Warning: `filter_()` was deprecated in dplyr 0.7.0.
## Please use `filter()` instead.
## See vignette('programming') for more help
##Upset format
plot(ven, type = "upset")
– venndetail uses a list of vectors as input to construct the shared or disjoint subsets Venn object. venndetail accepts a list of vector as input and returns a Venn object for the following analysis. Users can also use merge function to merge two Venn objects together to save time.
– plot generates figures with different layouts with type parameter. plot function also provides lots of parameters for users to modify the figures.
– getSet function provides a way to extract subsets from the main result along with any available annotations. The parameter subset asks the users to give the subset names to extract. It accepts a vector of subset names. Here, we will show how the DEGs shared by all three tissues as well as those that are only included by SCN tissue can be extracted.
## List the subsets name
detail(ven)
## Shared SCN_Glom Cortex_Glom Glom Cortex_SCN SCN
## 8 75 80 562 68 497
## Cortex
## 413
head(getSet(ven, subset = c("Shared", "SCN")), 10)
## Subset Detail
## 1 Shared 229599
## 2 Shared 243385
## 3 Shared 99899
## 4 Shared 17001
## 5 Shared 18143
## 6 Shared 64136
## 7 Shared 117591
## 8 Shared 67866
## 9 SCN 68800
## 10 SCN 69784
– result function can be used to extract and export all of the subsets for further processing. We currently support two different formats of result (long and wide formats).
## long format: the first column lists the subsets name, and the second column
## shows the genes included in the subsets
head(result(ven))
## Subset Detail
## 1 Shared 229599
## 2 Shared 243385
## 3 Shared 99899
## 4 Shared 17001
## 5 Shared 18143
## 6 Shared 64136
## wide format: the first column lists all the genes, the following columns
## display the groups name (three tissues) and the last column is the total
## number of the gene shared by groups.
head(result(ven, wide = TRUE))
## Detail Cortex SCN Glom SharedSets
## 238 229599 1 1 1 3
## 258 243385 1 1 1 3
## 307 99899 1 1 1 3
## 355 17001 1 1 1 3
## 401 18143 1 1 1 3
## 468 64136 1 1 1 3
– vennpie creates a Venn-pie diagram with unique or common subsets in multiple ways such as highlighting unique or shared subsets. The following example illustrates how to show the unique subsets on the venn-pie plots.
vennpie(ven, any = 1, revcolor = "lightgrey")
The parameters any and group provide two different ways to highlight the
subsets. any determines the subsets to show up in the number of groups
(1: those included in just one group; 2: those shared by any two groups).
group asks users to specify the subsets to be highlighted. Users may check
the sets name by using detail function.
Since the example datasets used in this vignette include only a small number of
shared genes all across three sets (n=8), it may be a little hard to see the
shared subset (grey), particularly in the Cortex group (the inner-most
circle).
.
vennpie(ven, log = TRUE)
When we have five datasets, we can use vennpie to show the sets
include elements from at least four datasets. Below show the reults with five
datasets as input.
set.seed(123)
A <- sample(1:1000, 400, replace = FALSE)
B <- sample(1:1000, 600, replace = FALSE)
C <- sample(1:1000, 350, replace = FALSE)
D <- sample(1:1000, 550, replace = FALSE)
E <- sample(1:1000, 450, replace = FALSE)
venn <- venndetail(list(A = A, B = B, C= C, D = D, E = E))
vennpie(venn, min = 4)
– getFeature allows users to combine the details of any or all subsets from
the main result with users’ other datasets, containing a list of data frames,
and to export the combined data as a data frame. In the following example, we
will demonstrate how to add other available annotation in the input data (T2DM)
such as log2FC and FDR values for the shared genes among these three tissues.
head(getFeature(ven, subset = "Shared", rlist = T2DM))
## Subset Detail Cortex_Entrez Cortex_Symbol
## 1 Shared 229599 229599 Gm129
## 2 Shared 243385 243385 Gprin3
## 3 Shared 99899 99899 Ifi44
## 4 Shared 17001 17001 Ltc4s
## 5 Shared 18143 18143 Npas2
## 6 Shared 64136 64136 Sdf2l1
## Cortex_Annotation Cortex_log2FC Cortex_FDR SCN_Entrez
## 1 predicted gene 129 4.851041 0.00156529 229599
## 2 GPRIN family member 3 2.588754 0.00156529 243385
## 3 interferon-induced protein 44 -2.186102 0.00156529 99899
## 4 leukotriene C4 synthase 3.916510 0.00156529 17001
## 5 neuronal PAS domain protein 2 -3.527904 0.00156529 18143
## 6 stromal cell-derived factor 2-like 1 -2.723979 0.00156529 64136
## SCN_Symbol SCN_Annotation SCN_log2FC SCN_FDR
## 1 Gm129 predicted gene 129 3.638130 0.000772111
## 2 Gprin3 GPRIN family member 3 2.942612 0.002032400
## 3 Ifi44 interferon-induced protein 44 -2.042164 0.012997000
## 4 Ltc4s leukotriene C4 synthase 2.852832 0.000772111
## 5 Npas2 neuronal PAS domain protein 2 -2.219165 0.015590600
## 6 Sdf2l1 stromal cell-derived factor 2-like 1 -2.092271 0.000772111
## Glom_Entrez Glom_Symbol Glom_Annotation Glom_log2FC
## 1 229599 Gm129 predicted gene 129 2.223499
## 2 243385 Gprin3 GPRIN family member 3 -2.186954
## 3 99899 Ifi44 interferon-induced protein 44 -2.146200
## 4 17001 Ltc4s leukotriene C4 synthase 2.471602
## 5 18143 Npas2 neuronal PAS domain protein 2 -11.845227
## 6 64136 Sdf2l1 stromal cell-derived factor 2-like 1 -2.875391
## Glom_FDR
## 1 0.025568700
## 2 0.000962798
## 3 0.000962798
## 4 0.011659400
## 5 0.000962798
## 6 0.000962798
– dplot shows the details of these subsets with bar-plot.
dplot(ven, order = TRUE, textsize = 4)
A shiny web application is here: VennDetail Note: Only support five input datasets now
For any questions please contact guokai8@gmail.com
[1] Hinder LM, Park M, Rumora AE, Hur J, Eichinger F, Pennathur S, Kretzler M, Brosius FC 3rd, Feldman EL.Comparative RNA-Seq transcriptome analyses reveal distinct metabolic pathways in diabetic nerve and kidney disease. J Cell Mol Med. 2017 Sep;21(9):2140-2152. doi: 10.1111/jcmm.13136. Epub 2017 Mar 8.