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.
## Shared SCN_Glom Cortex_Glom Glom Cortex_SCN SCN
## 8 75 80 562 68 497
## Cortex
## 413## 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.
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). .
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.
## 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.
