Correlations with TIL densities
diversity_cols <- c("reads", "diversity", "observedDiversity_mean", "shannonWienerIndex_mean",
"inverseSimpsonIndex_mean")
dat <- list(tcr = tcr_diversity, bcr = bcr_diversity)
diversity <- lapply(names(dat), function(segment) {
x <- dat[[segment]]
x <- subset(x, select = c("condensed_id", "patient_id", diversity_cols))
colnames(x) <- mapvalues(colnames(x), from = diversity_cols, to = paste(segment,
diversity_cols, sep = "_"))
return(x)
})
names(diversity) <- names(dat)
df <- Reduce(f = function(x, y) merge(x, y, by = c("condensed_id", "patient_id")),
c(diversity, list(ihc_table_subset)))
df_melted <- melt(df, id.vars = colnames(df)[!colnames(df) %in% tiltypes], measure.vars = tiltypes,
variable.name = "tiltype", value.name = "density")
pval_plot <- function(df_melted, variable) {
pvals <- setNames(ddply(df_melted, .(tiltype), function(x) {
df <- as.data.frame(x)
corres <- cor.test(df[, "density"], df[, variable], method = "spearman")
pval <- corres$p.value
eq <- substitute(italic(P) == p, list(p = format(pval, digits = 3)))
return(as.character(as.expression(eq)))
}), c("tiltype", "p.value"))
ggplot(df_melted, aes_string(x = "density", y = variable)) + geom_point(aes(colour = patient_id)) +
facet_wrap(~tiltype, scales = "free") + theme_bw() + theme_Publication() +
scale_color_manual(values = pal_patient) + geom_text(data = pvals, aes(x = Inf,
y = Inf, label = p.value), hjust = 1.1, vjust = 1.5, size = 3, parse = TRUE) +
ggtitle(variable)
}
pval_plot(df_melted, "tcr_reads")
pval_plot(df_melted, "bcr_reads")
pval_plot(df_melted, "tcr_diversity")
pval_plot(df_melted, "bcr_diversity")
pval_plot(df_melted, "tcr_shannonWienerIndex_mean")
pval_plot(df_melted, "bcr_shannonWienerIndex_mean")