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In this post, we will learn how to create venn diagrams for gene lists and how to retrieve the genes present in each venn compartment with R.

In this particular example, we will generate random gene lists using the **molbiotools gene set generator** but you can use your own gene lists if you prefer. Specifically, we will generate a random list of** 257 genes** to represent those that are upregulated in condition and another list of **1570 genes** to represent those that are upregulated in condition B.

Then, we will sort and paste the gene lists in an excel document we will save as **randomGeneLists.xlsx**.

Now, let’s load the data into R using the gdata package.

library("gdata") geneLists <- read.xls("randomGeneLists.xlsx", sheet=1, stringsAsFactors=FALSE, header=FALSE) head(geneLists) # Notice there are empty strings to complete the data frame in column 1 (V1) tail(geneLists) # To convert this data frame to separate gene lists with the empty strings removed we can use lapply() with our home made function(x) x[x != ""] geneLS <- lapply(as.list(geneLists), function(x) x[x != ""]) # If this is a bit confusing you can also write a function and then use it in lapply() removeEMPTYstrings <- function(x) { newVectorWOstrings <- x[x != ""] return(newVectorWOstrings) } geneLS2 <- lapply(as.list(geneLists), removeEMPTYstrings) # You can print the last 6 entries of each vector stored in your list, as follows: lapply(geneLS, tail) lapply(geneLS2, tail) # Both methods return the same results # We can rename our list vectors names(geneLS) <- c("ConditionA", "ConditionB") # Now we can plot a Venn diagram with the VennDiagram R package, as follows: require("VennDiagram") VENN.LIST <- geneLS venn.plot <- venn.diagram(VENN.LIST , NULL, fill=c("darkmagenta", "darkblue"), alpha=c(0.5,0.5), cex = 2, cat.fontface=4, category.names=c("A", "B"), main="Random Gene Lists") # To plot the venn diagram we will use the grid.draw() function to plot the venn diagram grid.draw(venn.plot) # To get the list of gene present in each Venn compartment we can use the gplots package require("gplots") a <- venn(VENN.LIST, show.plot=FALSE) # You can inspect the contents of this object with the str() function str(a) # By inspecting the structure of the a object created, # you notice two attributes: 1) dimnames 2) intersections # We can store the intersections in a new object named inters inters <- attr(a,"intersections") # We can summarize the contents of each venn compartment, as follows: # in 1) ConditionA only, 2) ConditionB only, 3) ConditionA & ConditionB lapply(inters, head)

Now you are ready, to review the genes in each section of the venn diagram separately. Alternatively, you can always use Venny web tool that is a great way to start looking at your data and then write a modified version of this R script to make a more exhaustive figure or facilitate downstream analysis in your script.

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