2.4 Main Functions
– 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
## 413head(getSet(ven, subset = c("Shared", "SCN")), 10)– 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))## 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))– 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))– dplot shows the details of these subsets with bar-plot.
dplot(ven, order = TRUE, textsize = 4)