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| f_image_output <- function(fileName, image, width=1920, height=1080, lc_pdf=T, lc_resolution=72){
if(lc_pdf){
width = width / lc_resolution
height = height / lc_resolution
pdf(paste(fileName, ".pdf", sep=""), width = width, height = height)
}else{
png(paste(fileName, ".png", sep=""), width = width, height = height)
}
print(image)
dev.off()
}
library(dplyr)
f_cDB_order_sequence <- function(lc_df){
da <- data.frame()
df <- subset(lc_df, receptor_a == 'True' & receptor_b == 'False' receptor_a == 'False' & receptor_b == 'True')
for(i in 1:length(df$gene_a)){
sub_data <- df[i, ]
if(sub_data$receptor_b=='False'){
if(sub_data$receptor_a=='True'){
old_names <- colnames(sub_data)
my_list <- strsplit(old_names[-c(1:11)], split="\\")
my_character <- paste(sapply(my_list, '[[', 2L), sapply(my_list, '[[', 1L), sep='')
new_names <- c(names(sub_data)[1:4], 'gene_b', 'gene_a', 'secreted', 'receptor_b', 'receptor_a', "annotation_strategy", "is_integrin", my_character)
sub_data = dplyr::select(sub_data, new_names)
# print('Change sequence!!!')
names(sub_data) <- old_names
da = rbind(da, sub_data)
}
}else{
da = rbind(da, sub_data)
}
}
return(da)
}
f_cDB_mergePandM <- function(means_order, pvals_order){
means_sub <- means_order[, c('interacting_pair', colnames(means_order)[-c(1:11)])]
pvals_sub <- pvals_order[, c('interacting_pair', colnames(means_order)[-c(1:11)])]
means_gather <- tidyr::gather(means_sub, celltype, mean_expression, names(means_sub)[-1])
pvals_gather <- tidyr::gather(pvals_sub, celltype, pval, names(pvals_sub)[-1])
mean_pval <- dplyr::left_join(means_gather, pvals_gather, by = c('interacting_pair', 'celltype'))
mean_pval
}
f_readcellphoneDB <- function(lc_dir){
res = list()
res$pvals <- f_cDB_order_sequence(read.delim(file.path(lc_dir, "out","pvalues.txt"), check.names = FALSE))
res$means <- f_cDB_order_sequence(read.delim(file.path(lc_dir, "out", "means.txt"), check.names = FALSE))
res$s_means <- read.delim(file.path(lc_dir, "out", "significant_means.txt"), check.names = FALSE)
res$m_p <- f_cDB_mergePandM(res$means, res$pvals)
lc_tp <- res$m_p %>% dplyr::select(interacting_pair, celltype, pval) %>% tidyr::spread(key=celltype, value=pval)
lc_sig_pairs <- lc_tp[which(rowSums(lc_tp<=0.05)!=0), ]
res$s_m_p <- subset(res$m_p, interacting_pair %in% lc_sig_pairs$interacting_pair)
res
}
f_cDB_dotplot <- function(lc_m_p){
lc_m_p %>% ggplot(aes(x=interacting_pair, y=celltype)) +
# geom_point(aes(color=log2(mean_expression), size=pval)) +
# scale_size(trans = 'reverse') +
geom_point(aes(color=log2(mean_expression), size=-log10(pval+1*10^-3)) ) +
guides(colour = guide_colourbar(order = 1),size = guide_legend(order = 2)) +
labs(x='', y='') +
scale_color_gradientn(name='Expression level \n(log2 mean expression \nmolecule1, molecule2)', colours = terrain.colors(100)) +
# scale_color_gradient2('Expression level \n(log2 mean expression \nmolecule1, molecule2)', low = 'blue', mid = 'yellow', high = 'red') +
theme(axis.text.x= element_text(angle=45, hjust=1)) +
# coord_flip() +
theme(
panel.border = element_rect(color = 'black', fill = NA),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks = element_blank()
# plot.title = element_text(hjust = 0.5),
# legend.position = 'bottom' # guides(fill = guide_legend(label.position = "bottom"))
# legend.position = "bottom"
# axis.text.y.right = element_text(angle=270, hjust=0.5)
) +
theme(legend.key.size = unit(0.4, 'cm'), #change legend key size
# legend.key.height = unit(1, 'cm'), #change legend key height
# legend.key.width = unit(1, 'cm'), #change legend key width
legend.title = element_text(size=9), #change legend title font size
legend.text = element_text(size=8)) #change legend text font size
}
br_d <- f_readcellphoneDB('Neuron_br')
tp_img <- f_cDB_dotplot(subset(br_d $s_m_p, pval<0.05))
f_image_output('Neuron_br',tp_img, width = 1080,height = 1080)
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