immune heritability replotting

immune.R

# adding on to Mike Love's analysis of data here:
# https://twitter.com/mikelove/status/1118595655608483843

# Data source is:
# https://www.cell.com/cms/10.1016/j.cell.2014.12.020/attachment/fb539453-989f-49e5-b880-34cf88151add/mmc5.xlsx

library(readxl)

x <- read_excel("mmc5.xlsx", skip = 1)
names(x)[12:13] <- c("ci_95_low","ci_95_high")
names(x) <- sub(" ","_",names(x))

x$cell <- sub("_p.+","",x[[1]])
x$TF <- sub(".+(pSTAT.)_.+","\\1",x[[1]])
x$cytokine <- sub(".+_","",x[[1]])

library(ggplot2)

ggplot(data = x) +
  geom_tile(aes(x = cell,
                y = cytokine,
                fill = corr_h)) +
  facet_wrap(~ TF, nrow = 1) +
  scale_fill_viridis_c() +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   vjust = 0.3))

x$mean[x$cytokine == "unstim"] <- 1

ggplot(data = x) +
  geom_point(aes(x = cell,
                y = cytokine,
                fill = corr_h,
                size = mean),
             pch = 21) +
  facet_wrap(~ TF, nrow = 1) +
  scale_size_area() +
  scale_fill_viridis_c() +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   vjust = 0.3))