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hla_oncoplot.R
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hla_oncoplot.R
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suppressMessages(library(ggplot2))
suppressMessages(library(reshape2))
suppressMessages(library(dplyr))
suppressMessages(library(RColorBrewer))
suppressMessages(library(ComplexHeatmap))
suppressMessages(library(optparse))
hla_oncoplot <- function(hla, exprn, meta, groups) {
### separte the classI and classII
class1 <- c("A1", "A2", "B1", "B2", "C1", "C2")
class2 <- c("DQA11", "DQA12", "DQB11", "DQB12", "DRB11", "DRB12")
class <- c(class1, class2)
#detect whether there are missing value
hla <- hla[c("subject", class)]
for (i in class) {
hla[[i]] <- sub("P","", hla[[i]])
}
#hla_table=as.data.frame(hla)
hla[hla==""] <- "n"
#remove the duplicates
if (nrow(meta) > length(unique(meta$PatName))){
patein_id <- meta["PatName"]
patein_id$subject <- rownames(patein_id)
tmp_1 <- merge(hla, patein_id, by = "subject", all = TRUE)
new_hla <- NULL
for (i in unique(tmp_1$PatName)) {
tmp <- subset(tmp_1, tmp_1$PatName == i)
tmp$quality <- rowSums(tmp == "n")
tmp <- subset(tmp, tmp$quality == min(tmp$quality))
if (nrow(tmp) > 1) {
tmp <- tmp[1,]
}
tmp <- tmp[c("PatName", class1, class2, "subject")]
new_hla <- rbind(new_hla, tmp)
}
new_meta <- new_hla[c("subject", "PatName")]
new_hla <- new_hla[-14]
colnames(new_hla)[1] <- "subject"
hla <- new_hla
}
###count hla allele frequency for all samples
tem = 1
for (i in c(1:2)) {
if (i == 1){
i = append("subject", class1)
} else {
i = append("subject", class2)
}
hla.tem <- hla[colnames(hla)%in%i]
hla.melt <- melt(hla.tem, id.vars = c("subject")) %>%
mutate_at(.vars = c("subject","variable"), .funs = as.character)
hla.cast <- dcast(hla.melt, value~subject, value.var = "variable", fun.aggregate = function(x) paste(x, collapse = ";"))
rownames(hla.cast) <- hla.cast$value
hla.cast <- hla.cast[,-1]
hla.cast <- hla.cast[!row.names(hla.cast)%in%"n",]
if (tem == 1){
hla.class1 <- hla.cast
##gene order
alter.perc <- apply(hla.class1, 1, function(x)length(x[x != ""])) #+ length(grep(";", unlist(x)))
ge.order1 <- names(sort(alter.perc[alter.perc != 0],decreasing = TRUE))
if (length(ge.order1 > 20)) {
ge.order1 <- head(ge.order1, 20)
}
hla.class1 <- hla.class1[ge.order1,]
} else {
hla.class2 <- hla.cast
##gene order
alter.perc <- apply(hla.class2, 1, function(x)length(x[x != ""])) #+ length(grep(";", unlist(x)))
ge.order2 <- names(sort(alter.perc[alter.perc != 0],decreasing = TRUE))
if (length(ge.order2 > 20)) {
ge.order2 <- head(ge.order2, 20)
}
hla.class2 <- hla.class2[ge.order2,]
}
tem = tem + 1
}
alter_fun = list(
background = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h-unit(0.5, "mm"), gp = gpar(fill = "#CCCCCC", col = NA))
},
A1 = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h*0.5, vjust = 0, gp = gpar(fill = "#A6CEE3", col = NA))
},
A2 = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h*0.5, vjust = 1, gp = gpar(fill = "#1F78B4", col = NA))
},
B1 = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h*0.5, vjust = 0, gp = gpar(fill = "#B2DF8A", col = NA))
},
B2 = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h*0.5, vjust = 1, gp = gpar(fill = "#33A02C", col = NA))
},
C1 = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h*0.5, vjust = 0, gp = gpar(fill = "#FB9A99", col = NA))
},
C2 = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h*0.5, vjust = 1, gp = gpar(fill = "#E31A1C", col = NA))
},
DQA11 = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h*0.5, vjust = 0, gp = gpar(fill = "#FDBF6F", col = NA))
},
DQA12 = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h*0.5, vjust = 1, gp = gpar(fill = "#FF7F00", col = NA))
},
DQB11 = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h*0.5, vjust = 0, gp = gpar(fill = "#CAB2D6", col = NA))
},
DQB12 = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h*0.5, vjust = 1, gp = gpar(fill = "#6A3D9A", col = NA))
},
DRB11 = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h*0.5, vjust = 0, gp = gpar(fill = "#808080", col = NA))
},
DRB12 = function(x, y, w, h) {
grid.rect(x, y, w-unit(0.5, "mm"), h*0.5, vjust = 1, gp = gpar(fill = "#A9A9A9", col = NA))
}
)
##gene order again
hla.cast <- rbind(hla.class1, hla.class2)
alter.perc <- apply(hla.cast, 1, function(x)length(x[x != ""])) #+ length(grep(";", unlist(x)))
ge.order <- names(sort(alter.perc[alter.perc != 0],decreasing = TRUE))
##sample order based on averaged HLA expression with acending order
hla.expr <- exprn[c("HLA-A","HLA-B","HLA-C", "HLA-DQA1", "HLA-DQB1", "HLA-DRB1"), ]
hla.expr.aver <- colMeans(hla.expr)
id.order <- as.character(new_meta$subject)
ex.order <- names(sort(hla.expr.aver))
ex.order <- intersect(ex.order, id.order)
id.order <- merge(ex.order,new_meta, by = 1, sort = FALSE)
id.order <- as.character(id.order$PatName)
#by default, grouping by the response column
if ("Responder"%in%colnames(meta)) {
meta$Responder <- sub("^$", "unkown", meta$Responder)
test <- meta["Responder"]
rownames(new_meta) <- new_meta$subject
new_meta <- merge(new_meta, test, by = 0, all = FALSE)
new_meta <- new_meta[order(new_meta$Responder, decreasing = FALSE),]
ex.order <- as.data.frame(ex.order)
ex.order$order <- c(1:nrow(ex.order))
ex.order_2 <- NULL
for (o in unique(new_meta$Responder)) {
tmp_order <- subset(new_meta, new_meta$Responder == o)
tmp_order <- merge(tmp_order, ex.order, by = 1, all = FALSE)
tmp_order <- tmp_order[order(tmp_order$order, decreasing = FALSE),]
ex.order_1 <- as.character(tmp_order$subject)
ex.order_2 <- append(ex.order_2, ex.order_1)
}
ex.order <- ex.order_2
id.order <- merge(ex.order,new_meta, by = 1, sort = FALSE)
id.order <- as.character(id.order$PatName)
rownames(new_meta) <- new_meta$PatName
anno.df <- new_meta[id.order, "Responder", drop = FALSE]
} else {
##top annotations with group informations
rownames(new_meta) <- new_meta$subject
new_meta <- new_meta[-2]
new_meta <- merge(new_meta, meta, by = 0, all = FALSE)
rownames(new_meta) <- new_meta$PatName
anno.df <- new_meta[id.order, groups]
}
DefaulfColorPalette <- c(
"#E58606", "#5D69B1", "#52BCA3", "#99C945", "#CC61B0", "#24796C",
"#DAA51B", "#2F8AC4", "#764E9F", "#ED645A", "#A5AA99", "#BCBD22",
"#B279A2", "#EECA3B", "#17BECF", "#FF9DA6", "#778AAE", "#1B9E77",
"#A6761D", "#526A83", "#B82E2E", "#80B1D3", "#68855C", "#D95F02",
"#BEBADA", "#AF6458", "#D9AF6B", "#9C9C5E", "#625377", "#8C785D"
)
col.list <- list()
for(col in groups){
col.len <- length(unique(meta[,col]))
cols <- DefaulfColorPalette[1:col.len]
names(cols) <- as.character(unique(meta[,col]))
col.list[[col]] <- cols
DefaulfColorPalette <- DefaulfColorPalette[-c(1:col.len)]
}
col_fun = structure(names = c("A1","A2","B1", "B2", "C1", "C2", "DQA11", "DQA12", "DQB11", "DQB12", "DRB11", "DRB12"),
brewer.pal(n=12, name = "Paired"))
col_fun[c(11,12)] <- c("#808080", "#C0C0C0")
ht_list <- oncoPrint(hla.cast[ge.order, id.order], get_type = function(x) strsplit(x, ";")[[1]],
alter_fun = alter_fun, col = col_fun, row_order = ge.order, column_order = id.order, width = unit(nrow(hla)*0.5, "cm"),
row_names_gp = gpar(fontsize = 10,fontface = "bold"), pct_gp = gpar(fontsize = 10,fontface = "bold"),
remove_empty_columns = FALSE,#remove_empty_rows = TRUE,
show_column_names = FALSE,
top_annotation = HeatmapAnnotation(df = anno.df,
col = col.list,
HLA_Expression = anno_points(hla.expr.aver[ex.order])), #
heatmap_legend_param = list(title = "Allele", at = names(col_fun), labels = names(col_fun)))
return(ht_list)
}