eVenn (version 2.4)

evenn: A Powerful Tool to Quickly Compare Huge Lists and Draw Venn Diagrams

Description

Compare lists (from 2 to infinite) and plot the results in a Venn diagram if (N<=4) with regulation details. It allows to produce a complete annotated file, merging the annotations of the compared lists. It is also possible to compute an overlaps table to show the overlaps proportions of all the couples of lists and draw proportional Venn diagrams.

Usage

evenn(annot=FALSE, matLists="", pathRes="", pathLists="", ud=FALSE, prop=FALSE, noms="",
	 overlaps=FALSE, f=0, display=FALSE, couleurs="", VennBar=FALSE, CompName="",
	 transp=0.5, filled=TRUE, Profils=FALSE, OnlyVariable=FALSE, colBlack=FALSE,
	 ColorTxt="", Ptest=FALSE, tUD=NULL, tUDp=NULL, tnoUD=NULL, Gtype="png", title="",
	 lw=1, NutShell=TRUE, VennClust=FALSE, OnlyVenn=FALSE)

Arguments

annot

If the compared files got more than one column (not only IDs).

matLists

Matrix, list of matrix or list of lists to be compared.

pathRes

Path used to save the results. The working directory is used by default.

pathLists

Path of the data folder where are placed the files to be compared. The files must be text tabulated or csv format.

ud

If the source files comport a ratio or fold column the details of up and down regulated genes will be shown.

prop

If the surfaces of the disks should be proportional to the size of the lists.

noms

Custom short names for each list.

overlaps

Compute the proportions of the overlaps for each couple of lists in two ways (A in B and B in A). A heatmap is created if at least three lists are processed.

f

Set a minimum overlap to produce a filtered overlaps matrix. Default f=0 is set to no filter.

display

Print commentaries during the process.

couleurs

Vector of custom colours to be used in the graphics. Default colours: c("blue", "red", "green", "orange")

VennBar

Represent the zones of a venn diagram with a bars chart even for more than 4 lists.

CompName

Custom name for the main folder.

transp

Percentage of transparency of the disks.

filled

No transparancy, disks are filled with solid colours.

Profils

This option is valid only if the modulations (UD) are available (ie ratio column or each list) and a DataMoy.txt file containing the means of each group for all the items. The items will be separated by modulations profiles.

OnlyVariable

evenn expect a variable in (matList) and produce a binary matrix. (=> BinaryMatrixOut = evenn(matList=DataIn))

colBlack

No color, only Black on a white background.

ColorTxt

Custom color of the counts.

Ptest

Proportional test (Fisher's exact) of the non null areas.

tUD

Font size of the count and profile names of specific zones.

tUDp

Font size of the count and profile names of common zones.

tnoUD

Font size of the total count of a zone.

Gtype

File format of the graphic (png or svg).

title

Main title of the graphic.

lw

Line width relative to the default (default=1). 2 is twice as wide.

NutShell

All the Venn zones and UD profiles in a nutshell (count table).

VennClust

Creates two clusterings (using Spearman correlation similarity measure and average or Ward.D2 linkage algorithm) to model links between the lists.

OnlyVenn

Draw only the venn diagram without any text outside (remove the total and lists names).

Value

All the results are saved in a folder names with date and hour. The comparison binary matrix is saved as a tabulated text file "venn_matrix.txt". If annot is True, the resulting file (same as venn matrix with annotation given by the source lists) is saved as a text file "venn_annot.txt". The venn diagram is saved in a png file "venn_diagram.png".

References

http://blog.mrbioinfo.com/

Examples

# NOT RUN {
library(eVenn)

#	Text tabulated files placed in a specific folder
#	evenn(pathLists="./SpecificFolder", ud=TRUE, annot=TRUE)

#	Text tabulated files placed in multiple folders placed in a main folder=> multiple diagrams
#	autoevenn(FolderPath="./MainFolder", ud=TRUE, annot=TRUE)

#	YNdisplay = TRUE	# Allows commentaries and display of the main steps of the process

#	Matrix of binary data
#   data(Data_Binary_Matrix)
#   evenn(matLists=Data_Binary_Matrix, display=YNdisplay, CompName="Binary_Matrix")

#	Matrix of folds
#   data(Data_Matrix_Of_Folds)
#   evenn(matLists=Data_Matrix_Of_Folds[,c(1,2)], display=YNdisplay, CompName="Matrix_Of_Folds")

#	Matrix of ratios
#   data(Data_Matrix_Of_Ratios)
#   evenn(matLists=Data_Matrix_Of_Ratios, display=YNdisplay, CompName="Matrix_Of_Ratios")

#	List of 2, 3 or 4 matrix w/wo modulations and w/wo profils data

#   data(Data_Lists)

#   evenn(matLists=Data_Lists[c("List_1", "List_2", "List_3", "List_4")], annot=TRUE,
#    display=YNdisplay, CompName="Lists_4")

#   evenn(matLists=Data_Lists[c("List_1", "List_2", "List_3", "List_4")], ud=TRUE,
#    annot=TRUE, display=YNdisplay, CompName="Lists_4_UD")

#   evenn(matLists=Data_Lists[c("List_1", "List_2", "List_3", "List_4", "DataMoy")],
#    ud=TRUE, annot=TRUE, Profils=TRUE, display=YNdisplay, CompName="Lists_4_UD_Profils")
# }