GIT_HOME="/research/users/ppolonen/git_home/ImmunogenomicLandscape-BloodCancers/"
source(file.path(GIT_HOME, "common_scripts/visualisation/plotting_functions.R"))
# Plot CGA expression dot plots for (Figure S6C)
library(parallel)
library(gridExtra)
library(ggplot2)
library(ggrepel)
library(cowplot)
# load Hemap gene expression data
data = get(load("data9544_with_gene_symbols.RData"))
# load Hemap annotations
annot = get(load("Hemap_immunology_Annotations.Rdata"))
data=data[annot[,1],]
# plotting functions
FUN_PLOT=function(gene, logicalVectors, namesLV, data=NULL, matrix=NULL, col=NULL, ORDER=F, RANGE=NULL) {
if(is.null(matrix)&is.null(data))stop("No data to plot, check data/matrix")
GNAME=gsub("N:....:|:::::|DUFVA_", "", gene)
GNAME=gsub("_", " ", GNAME)
namesLV=gsub("Cancer_", " ", namesLV)
cols <- read.table("colors_hemap_immunology.tsv", header = TRUE, sep = "\t", comment.char = " ")
samples <- gsub("LCH", "MDS", gsub("AITL|PTCLNOS|ALCL", "TCL", gsub("FL|MALT", "BCL", gsub("Healthy", "NonCancerHealthy", gsub("_", "", names(logicalVectors))))))
if(is.null(col)){
col=as.character(cols[match(samples, cols$sample),2])
}
if(!is.null(matrix)){
gene2=ifelse(grepl("GEXP", gene), gene, paste0("'N:GEXP:", gene, ":::::'"))
D=as.numeric(read.delim(pipe(paste0("grep -Fw ", gene2, " ", matrix)), row.names = 1, header=F))
}
if(!is.null(data)){
D = as.numeric(data[,colnames(data)%in%gene])
}
bplot_list=lapply(logicalVectors, function(v){
D[v]
})
names(bplot_list)=gsub("_", " ", namesLV)
if(ORDER){
ord=sapply(bplot_list, median)
col=col[order(ord, decreasing = T)]
bplot_list=bplot_list[order(ord, decreasing = T)]
}
plots=FUNCTION_PLOT_LIST(bplot_list, gene, col, ORDER, RANGE)
return(plots)
}
FUNCTION_PLOT_LIST=function(bplot_list, GNAME, col, ORDER, RANGE){
df=melt(bplot_list)
df$class <- factor(df[,2], levels = unique(as.character(df[,2])),ordered = TRUE)
df$Expression=as.numeric(as.vector(df[,1]))
p <- ggplot(data=df, aes(x=class, y=Expression, color=class)) +
geom_jitter(width = 0.25, size = 0.1) +
scale_color_manual(values = col)
p2 <- p +
#theme with white background
theme_bw() +
# titles
theme(plot.title = element_text(face="italic", color="black", size=16, hjust=0)) +
theme(axis.title = element_text(color="black", face=NULL, size=12,angle = 90)) +
theme(axis.title.y = element_text(size = 14, angle = 90, color="black", face=NULL)) +
guides(color = FALSE) +
ylab("Expression (log2)") +
xlab("") +
labs(title=GNAME) +
#eliminates background, gridlines, and chart border
theme(plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())+
theme(panel.border= element_blank())+
#draws x and y axis line
theme(axis.line = element_line(),
axis.line.x = element_line(color="black", size = 0.5),
axis.line.y = element_line(color="black", size = 0.5)) +
# X - axis text
theme(axis.text.x = element_text(angle=45, hjust=1, color="black", size = 14, face=NULL),
axis.text.y = element_text(hjust=1, color="black", size = 12, face=NULL))+
# if want to limit to range
if(!is.null(RANGE))scale_y_continuous(breaks=seq(2,14,2), limits = RANGE)
return(p2)
}
boxplots_grid_topcga_subtypes <- function(x){
p.all=lapply(c("MAGEC1", "MAGEC2", "MORC1", "DSCR8", "MAGEB1", "MAGEB2", "ADAM29", "DMRT1", "SAGE1"), FUN_PLOT, logicalVectors, namesLV=names(logicalVectors), data=data, ORDER=F)
ggsave(paste0(GENELIST, ".pdf"), do.call(marrangeGrob, append(list(grobs=p.all, nrow=3, ncol=3),list(top=NULL))), width = 350 , height = 250, units = "mm", dpi=250)
}
# make logical vector with cancer subtypes and healthy sample
annot$logicalvector <- annot$Sample.type
annot$logicalvector[annot$Sample.type!="NonCancerHealthy"] <- annot$Category.specifying.subtype[annot$Sample.type!="NonCancerHealthy"]
annot$logicalvector[annot$Sample.type=="NonCancerHealthy"] <- "Healthy"
annot$logicalvector[annot$Category.specifying.lineage.tumor.origin=="AML"] <- "AML"
annot$logicalvector[annot$Category.specifying.lineage.tumor.origin=="MDS"] <- "MDS"
annot$logicalvector[annot$Category.specifying.lineage.tumor.origin=="CLL"] <- "CLL"
annot$logicalvector[annot$Category.specifying.subtype=="AILT"] <- "AITL"
annot$logicalvector[annot$Category.specifying.subtype=="LC"] <- "LCH"
# plot selected genes
logicalVectors=get.logical(annovector = list(annot$logicalvector), filterv = annot$Sample.type%in%c("Cancer", "Prolif", "NonCancerHealthy"), PREFIX = "")
logicalVectors=logicalVectors[paste0(c("MM", "DLBCL", "MCL", "CHL", "FL", "MALT", "ALCL", "PTCLNOS", "AITL", "T-ALL", "pre-B-ALL", "AML", "MDS", "LCH", "CML", "CLL", "Healthy"), "_")]
GENELIST="FigureS6C_Hemap_CGA_dotplots"
boxplots_grid_topcga_subtypes()
Datasets
Models
