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Sex-biased transcriptomic response of the reproductive axis to stress

This venn diagram is from this paper

library(ggplot2)
library(dplyr)
library(cowplot)
#library(ggrepel)

knitr::opts_chunk$set(fig.path = './', echo = T, message = F)

venn <- read.csv("venn.csv")

venn$tissue <- factor(venn$tissue, levels = c("hyp", "pit", "gon"))
venn$sex <- factor(venn$sex, levels = c("female", "both", "male"))
levels(venn$direction) <-  c("down", "up")
#levels(venn$tissue) <-  c("hypothalamus", "pituitary", "gonads")


venn$directionsex <- as.factor(paste(venn$direction, venn$sex, sep = "\n"))


mycolors <- c("down\nboth" = "#838383",
                "down\nfemale" = "#19A400", 
                 "down\nmale"  = "#516DFF",
                 "up\nboth"  = "#D9D9D9",
                 "up\nfemale"  = "#7EDC00",
                 "up\nmale" = "#8AC5F9")  


venn$directionsex <- factor(venn$directionsex, levels = c("down\nfemale", 
                                                          "down\nmale",
                                                          "down\nboth",
                                                          "up\nboth",
                                                          "up\nmale",
                                                            "up\nfemale"))

p2 <- ggplot(data=venn, aes(x=tissue, y = count,  fill = reorder(directionsex, desc(directionsex)))) + 
  geom_bar(stat="identity") +
  labs(y = "total DEGs", x = NULL) +
  scale_fill_manual(values = mycolors) +
  theme_minimal() +
    theme(legend.title = element_blank())
p2 

p1 <- ggdraw() + draw_image("venn-original.png")
plot_grid(p1, p2, nrow = 1, rel_widths = c(0.5, 0.5))

This weighted Venn diagram is the highest quality Venn diagram I’ve ever seen, but it still has limitations. If you only focus on the green pies, they seem to do a good job of conveying relative size, but if you look closely, you’ll notices that the size of the grey circles is misleading. The original Venn diagram conveys information about tissue (hypothalamus, pituitary, gonad), sex (male and female), and response (up- or down-regulation), so I used facetting and a combination of stacked and side-by-side bar plot to visualize all the complexity. I think that this bar chart does a better job of conveying the magnitude of differences between the sexes and across tissues and the bias toward up-regulation of gene expression.

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