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helper_functions.oncoplot.R
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helper_functions.oncoplot.R
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filter_maf <- function(maf_file, flag_genes="default",save_name=NULL,no_filter=F,
norm_alt_max=1,t_alt_min=1,t_depth_min=20,
tumor_freq_min=0.05, norm_freq_max=0.02,
gnomAD_AF_max=0.001, AF_max=0.001, ExAC_AF_max=0.001,
n_callers=2, variant_caller=NULL) {
# browser()
if (length(flag_genes)==0) {
flag_genes <- c()
} else if (flag_genes[1]=="default") {
flag_genes <- c("TTN","MUC16","OBSCN","AHNAK2","SYNE1","FLG","MUC5B","DNAH17","PLEC","DST","SYNE2","NEB","HSPG2","LAMA5","AHNAK","HMCN1","USH2A","DNAH11","MACF1","MUC17","DNAH5","GPR98","FAT1","PKD1","MDN1","RNF213","RYR1","DNAH2","DNAH3","DNAH8","DNAH1","DNAH9","ABCA13","SRRM2","CUBN","SPTBN5","PKHD1","LRP2","FBN3","CDH23","DNAH10","FAT4","RYR3","PKHD1L1","FAT2","CSMD1","PCNT","COL6A3","FRAS1","FCGBP","RYR2","HYDIN","XIRP2","LAMA1")
}
if (class(maf_file)=="character") {
if (file.exists(maf_file[1])) {
maf_df.raw <- read.table(maf_file, sep="\t", header=T, fill = T, quote="\"", stringsAsFactors = F)
} else {
stop("Interpreting 'maf_file' as a file name, but file not found.")
}
} else if (class(maf_file)=="MAF"){
maf_df.raw <- rbind(maf_file@data, maf_file@maf.silent)
}
maf_df.raw <- maf_df.raw[maf_df.raw$Hugo_Symbol != "Hugo_Symbol",]
filter_genes=!maf_df.raw$Hugo_Symbol %in% flag_genes
maf_df.raw <- maf_df.raw[filter_genes,]
if (!"tumor_freq" %in% colnames(maf_df.raw)) {
maf_df.raw$tumor_freq <- as.numeric(maf_df.raw$t_alt_count)/as.numeric(maf_df.raw$t_depth)
}
if (!"norm_freq" %in% colnames(maf_df.raw)) {
maf_df.raw$norm_freq <- as.numeric(maf_df.raw$n_alt_count)/as.numeric(maf_df.raw$n_depth)
}
filter_tumor_depth=rep(TRUE,nrow(maf_df.raw))
filter_tumor_alt=rep(TRUE,nrow(maf_df.raw))
filter_norm_alt=rep(TRUE,nrow(maf_df.raw))
filter_pop_freq=rep(TRUE,nrow(maf_df.raw))
if (!no_filter) {
options(warn=-1)
filter_tumor_depth=as.numeric(maf_df.raw$t_depth) > t_depth_min
if (!sum(is.na(maf_df.raw$norm_freq)) == nrow(maf_df.raw)){
filter_norm_alt=maf_df.raw$norm_freq < norm_freq_max
}
filter_tumor_alt=maf_df.raw$tumor_freq > tumor_freq_min
if (! is.null(t_alt_min)){
filter_tumor_alt <- filter_tumor_alt & maf_df.raw$t_alt_count > t_alt_min
}
filter_gnomad=rep(TRUE,nrow(maf_df.raw))
filter_1000G=rep(TRUE,nrow(maf_df.raw))
filter_exac=rep(TRUE,nrow(maf_df.raw))
if (!is.null(maf_df.raw$gnomAD_AF)) {
filter_gnomad=maf_df.raw$gnomAD_AF %in% c("-","") | is.na(maf_df.raw$gnomAD_AF) | as.numeric(maf_df.raw$gnomAD_AF) < min(gnomAD_AF_max,1)
}
if (!is.null(maf_df.raw$AF)) {
filter_1000G=maf_df.raw$AF %in% c("-","") | is.na(maf_df.raw$AF) | as.numeric(maf_df.raw$AF) < min(AF_max,1)
}
if (!is.null(maf_df.raw$ExAC_AF)) {
filter_exac=maf_df.raw$ExAC_AF %in% c("-","") | is.na(maf_df.raw$ExAC_AF) | as.numeric(maf_df.raw$ExAC_AF) < min(ExAC_AF_max,1)
}
filter_pop_freq=filter_gnomad & filter_1000G & filter_exac
options(warn=0)
}
filter_caller=rep(TRUE,nrow(maf_df.raw))
if (! is.null(variant_caller)) { ### Set 'variant_caller' to NULL to skip any filtering based on caller
maf_df.raw$set[maf_df.raw$set=="" & maf_df.raw$Hugo_Symbol=="Hugo_Symbol"] <- "set"
maf_df.raw$set[maf_df.raw$set==""] <- "N.A."
if (variant_caller == "consensus") { ### Set 'variant_caller' to 'consensus' to keep variants by two or more callers
# filter_caller <- grepl("-|Intersection", maf_df.raw$set)
filter_caller <- unlist(lapply(strsplit(maf_df.raw$set,"-"), function(x) {length(x)>=n_callers | "Intersection" %in% x}))
} else { ### Set 'variant_caller' to one of the callers (mutect, mutect2, vardict, or strelka) to get only that caller
# filter_caller <- grepl(paste0(variant_caller,"[|-]|Intersection"), maf_df.raw$set)
filter_caller <- unlist(lapply(strsplit(maf_df.raw$set,"-"), function(x) {any(c(variant_caller,"Intersection") %in% x)}))
}
}
maf_df.rawest <- maf_df.raw
maf_df.raw <- maf_df.raw[filter_tumor_depth & filter_norm_alt & filter_tumor_alt & filter_pop_freq & filter_caller,]
# browser()
maf_df.raw <- maf_df.raw[rowSums(is.na(maf_df.raw))!=ncol(maf_df.raw),]
if (!is.null(save_name)) {
if (! dir.exists(dirname(save_name))) {
dir.create(dirname(save_name))
}
write.table(maf_df.raw, sep="\t", quote=F, file = save_name, row.names = F)
print(paste0("Saving filtered maf to ",save_name))
return(save_name)
} else {
return(maf_df.raw)
}
}
filter_maf_tbl <- function(maftbl, # A tibble for filtering
flag_genes="default", # Character vector of gene symbols to exclude; or set to 'default' to remove preset list of 50 genes from the FLAG paper
#save_name=NULL,
grep_vcf_filter_col="PASS|\\.", # Regex to select from the FILTER column
non_silent_only=F, # If TRUE, only non-silent mutations are returned (same definition as maftools)
t_alt_min=2, # Minimum count for ALT allele in tumor sample
t_alt_max=1e12, # Maximum count for ALT allele in tumor sample
t_depth_min=5, # Minimum total depth (REF+ALT) in tumor sample
t_depth_max=1e12, # Maximum total depth (REF+ALT) in tumor sample
tumor_freq_min=0.01,
tumor_freq_max=1,
n_alt_min=0,
n_alt_max=1,
n_depth_min=0,
n_depth_max=1e12,
norm_freq_min=0,
norm_freq_max=0.01,
gnomAD_AF_min=0,
gnomAD_AF_max=0.001,
AF_min=0,
AF_max=0.001,
ExAC_AF_min=0,
ExAC_AF_max=0.001,
n_callers=2,
variant_caller=NULL) {
if (length(flag_genes)==0) {
flag_genes <- c()
} else if (flag_genes[1]=="default") {
flag_genes <- c("TTN","MUC16","OBSCN","AHNAK2","SYNE1","FLG","MUC5B","DNAH17","PLEC","DST","SYNE2","NEB","HSPG2","LAMA5","AHNAK","HMCN1","USH2A","DNAH11","MACF1","MUC17","DNAH5","GPR98","FAT1","PKD1","MDN1","RNF213","RYR1","DNAH2","DNAH3","DNAH8","DNAH1","DNAH9","ABCA13","SRRM2","CUBN","SPTBN5","PKHD1","LRP2","FBN3","CDH23","DNAH10","FAT4","RYR3","PKHD1L1","FAT2","CSMD1","PCNT","COL6A3","FRAS1","FCGBP","RYR2","HYDIN","XIRP2","LAMA1")
}
# browser()
require(tibble)
require(dplyr)
df <- as_tibble(maftbl)
maf_df.raw <- df[df$Hugo_Symbol != "Hugo_Symbol",]
mafgenome <- detect_maf_genome(maf_df.raw)[[1]]
if (mafgenome %in% c("hg19","hg38")) {
maf_df.raw <- maf_df.raw[!df$Hugo_Symbol %in% flag_genes,]
}
if ("FILTER" %in% colnames(maf_df.raw)) {
if (!is.null(grep_vcf_filter_col)) {
maf_df.raw <- maf_df.raw[grepl(grep_vcf_filter_col,maf_df.raw$FILTER),]
}
} else {
message("FILTER column not found; skipping...")
}
#Variant Classification with High/Moderate variant consequences. http://asia.ensembl.org/Help/Glossary?id=535
vc.nonSilent = c("Frame_Shift_Del", "Frame_Shift_Ins", "Splice_Site", "Translation_Start_Site",
"Nonsense_Mutation", "Nonstop_Mutation", "In_Frame_Del",
"In_Frame_Ins", "Missense_Mutation")
if ("Variant_Classification" %in% colnames(maf_df.raw)) {
if (non_silent_only) {
# maf_df.raw <- maf_df.raw[grepl(grep_vcf_filter_col,pull(maf_df.raw,FILTER)),]
maf_df.raw <- maf_df.raw[maf_df.raw$Variant_Classification %in% vc.nonSilent,]
}
} else {
message("Variant_Classification column not found; skipping...")
}
caller_set_column="set"
filter_caller=rep(TRUE,nrow(maf_df.raw))
if (! is.null(variant_caller)) { ### Set 'variant_caller' to NULL to skip any filtering based on caller
if (caller_set_column %in% colnames(maf_df.raw)) {
maf_df.raw$set[maf_df.raw$set=="" & maf_df.raw$Hugo_Symbol=="Hugo_Symbol"] <- caller_set_column ## This can happen if MAFs are lazily cat-ed together (mutiple header lines in the file)
maf_df.raw$set[maf_df.raw$set==""] <- "N.A."
if (variant_caller == "consensus") { ### Set 'variant_caller' to 'consensus' to keep variants by two or more callers
# filter_caller <- grepl("-|Intersection", maf_df.raw$set)
filter_caller <- unlist(lapply(strsplit(maf_df.raw$set,"-"), function(x) {length(x)>=n_callers | "Intersection" %in% x}))
} else { ### Set 'variant_caller' to one of the callers (mutect, mutect2, vardict, or strelka) to get only that caller
# filter_caller <- grepl(paste0(variant_caller,"[|-]|Intersection"), maf_df.raw$set)
filter_caller <- unlist(lapply(strsplit(maf_df.raw$set,"-"), function(x) {any(unique(c(variant_caller,"Intersection")) %in% x)}))
}
}
}
maf_df.raw <- maf_df.raw[filter_caller,]
# browser()
if (!"tumor_freq" %in% colnames(maf_df.raw)) {
# if (! all(c("t_alt_count","t_depth") %in% colnames(maf_df.raw))) {
# stop("Can't find t_alt_count or t_depth columns")
# }
if (! "t_alt_count" %in% colnames(maf_df.raw)) {
maf_df.raw$t_alt_count <- NA
}
if (! "t_depth" %in% colnames(maf_df.raw)) {
maf_df.raw$t_depth <- NA
}
maf_df.raw$tumor_freq <- as.numeric(maf_df.raw$t_alt_count)/as.numeric(maf_df.raw$t_depth)
}
if (!"norm_freq" %in% colnames(maf_df.raw)) {
# if (! all(c("n_alt_count","n_depth") %in% colnames(maf_df.raw))) {
# maf_df.raw$norm_freq <- rep(0, nrow(maf_df.raw))
# } else {
# maf_df.raw$norm_freq <- as.numeric(maf_df.raw$n_alt_count)/as.numeric(maf_df.raw$n_depth)
# }
if (! "n_alt_count" %in% colnames(maf_df.raw) ) {
maf_df.raw$n_alt_count <- NA
}
if (! "n_depth" %in% colnames(maf_df.raw)) {
maf_df.raw$n_depth <- NA
}
maf_df.raw$norm_freq <- as.numeric(maf_df.raw$n_alt_count)/as.numeric(maf_df.raw$n_depth)
}
maf_num_filter_columns <- list("t_alt_count"=c(min=t_alt_min, max=t_alt_max),
"t_depth"=c(min=t_depth_min, max=t_depth_max),
"tumor_freq"=c(min=tumor_freq_min, max=tumor_freq_max),
"n_alt_count"=c(min=n_alt_min, max=n_alt_max),
"n_depth"=c(min=n_depth_min, max=n_depth_max),
"norm_freq"=c(min=norm_freq_min, max=norm_freq_max),
"gnomAD_AF"=c(min=gnomAD_AF_min, max=gnomAD_AF_max),
"AF"=c(min=AF_min, max=AF_max),
"ExAC_AF"=c(min=ExAC_AF_min, max=ExAC_AF_max)
)
# browser()
numfilter_columns <- names(maf_num_filter_columns)[names(maf_num_filter_columns) %in% colnames(maf_df.raw)]
notfound <- setdiff(names(maf_num_filter_columns), numfilter_columns)
if (length(notfound) > 0 ) {
message(paste0("Couldn't find these columns; skipping filtering for these: ", paste0(notfound, collapse=",")))
}
# maf_df.raw$Matched_Norm_Sample_Barcode[is.na(maf_df.raw$Matched_Norm_Sample_Barcode)] <- 0
if (all(maf_df.raw$Tumor_Sample_Barcode==maf_df.raw$Matched_Norm_Sample_Barcode, na.rm=T)) {
warning("Normal IDs match tumor IDs, skipping n_alt_count, n_depth, and norm_freq filters...")
numfilter_columns <- setdiff(numfilter_columns, c("n_alt_count","n_depth","norm_freq"))
}
return_df <- maf_df.raw
if (length(numfilter_columns)>0) {
all_num_filters <- lapply(numfilter_columns, function(col_name) {
currdata <- as.numeric(pull(maf_df.raw,col_name))
currdata[is.na(currdata)] <- 0 ## Keeps NAs
filter_vec <- currdata >= maf_num_filter_columns[[col_name]]["min"] & currdata <= maf_num_filter_columns[[col_name]]["max"]
return(filter_vec)
})
names(all_num_filters) <- numfilter_columns
# browser()
# print(lapply(all_num_filters, sum))
final_num_filters <- Reduce("&", all_num_filters)
return_df <- maf_df.raw[final_num_filters,]
}
return(return_df)
}
filter_maf_chunked <- function(maf, chunk_lines=10000, savename=NULL,...) {
require(maftools)
clindata <- NULL
if ("MAF" %in% class(maf)) {
filtered_df <- filter_maf_tbl(
rbind(maf@data, maf@maf.silent),
...)
clindata <- maf@clinical.data
} else if (file.exists(maf)) {
require(readr)
readr_filterfunc <- function(df, pos) {
filter_maf_tbl(df,...)
}
filtered_df <- read_tsv_chunked(maf,chunk_size = chunk_lines, col_types = cols(), callback = DataFrameCallback$new(readr_filterfunc), comment="#")
} else {
# stop(paste0("Don't know what to do with input type '",class(maf),"'"))
stop(paste0("MAF file not found: '",maf,"'"))
}
if ( ! is.null(savename) ) {
if (!dir.exists(dirname(savename))) {dir.create(dirname(savename), recursive = T)}
write.table(filtered_df, file=savename,sep="\t", quote=F, row.names=F)
}
maf.filtered <- read.maf(filtered_df, clinicalData = clindata)
invisible(maf.filtered)
}
make_oncoplot <- function(maf.filtered, cohort_freq_thresh = 0.01, auto_adjust_threshold=T,
oncomat_only=F, show_sample_names=NULL,
clin_data=NULL, clin_data_colors=NULL,
savename=NULL) {
require(ComplexHeatmap)
### Read in MAF file
# maf.filtered <- read.maf(maf_file)
### Structure info about the fraction of the cohort that has each gene mutated
frac_mut <- data.frame(Hugo_Symbol=maf.filtered@gene.summary$Hugo_Symbol,
# frac_mut=(maf.filtered@gene.summary$MutatedSamples/as.numeric(maf.filtered@summary$summary[3])),
frac_mut=(maf.filtered@gene.summary$AlteredSamples/as.numeric(maf.filtered@summary$summary[3])),
stringsAsFactors = F)
ngene_max=25
target_frac = sort(frac_mut$frac_mut, decreasing = T)[min(ngene_max,nrow(frac_mut))]
if (auto_adjust_threshold) {
cohort_freq_thresh <- max(c(cohort_freq_thresh,target_frac))
}
### Select genes based on the frequency threshold
freq_genes <- frac_mut$Hugo_Symbol[frac_mut$frac_mut >= cohort_freq_thresh]
freq_genes <- freq_genes[1:ngene_max]
if (length(freq_genes) == 0) {
stop("No genes to plot; change the frequency threshold to include more genes.")
}
if (length(freq_genes) > 100) {
target_frac = round(sort(frac_mut$frac_mut, decreasing = T)[min(ngene_max,nrow(frac_mut))],2)
# stop(paste0("Too many genes for oncoplot. Trying setting the cohort mutated fraction to > ", target_frac))
warning(paste0("Too many genes for oncoplot. Trying setting the cohort mutated fraction to > ", target_frac))
# return(NA)
}
gene_list <- list(freq_genes)
reasons <- paste0("Cohort Freq > ",round(cohort_freq_thresh,digits = 3))
### Collect genes to plot
genes_for_oncoplot <- data.frame(Hugo_Symbol=c(), reason=c())
for (i in 1:length(gene_list)) {
if (is.na(gene_list[[i]][1])) {
next
}
genes_for_oncoplot <- rbind(genes_for_oncoplot,
data.frame(Hugo_Symbol=gene_list[[i]],
reason=reasons[i]))
}
genes_for_oncoplot <- cbind(genes_for_oncoplot,
frac=frac_mut$frac_mut[match(genes_for_oncoplot$Hugo_Symbol, frac_mut$Hugo_Symbol)])
genes_for_oncoplot <- genes_for_oncoplot[order(genes_for_oncoplot$reason, -genes_for_oncoplot$frac),]
# browser()
### Split the oncoplot based on the reason for picking the gene
### Here, we're only picked based on the frequency
### But this framework is useful for plotting genes picked using various criteria
split_idx=genes_for_oncoplot$reason
split_colors <- rainbow(length(unique(split_idx)))
# names(split_colors) <- as.character(genes_for_oncoplot$reason[!duplicated(genes_for_oncoplot$reason)])
names(split_colors) <- unique(split_idx)
split_colors <- list(Reason=split_colors)
# source("scripts/helper_functions.oncoplot.R")
### Make matrix to plot, and order it correctly
oncomat <- createOncoMatrix(maf.filtered, g=genes_for_oncoplot$Hugo_Symbol, add_missing = F)$oncoMatrix
oncomat <- oncomat[match(genes_for_oncoplot$Hugo_Symbol,rownames(oncomat)), ]
onco_genes <- rownames(oncomat)
if (oncomat_only) {
return(oncomat)
}
oncomat.plot <- oncomat
### Set the height of the plot based on number of genes
onco_height=NULL
if (is.null(onco_height)) {
onco_height=max(round(0.2*nrow(oncomat.plot),0),5)
}
### Make the mutation type names prettier by removing the underscore
# my_mut_col <- mutation_colors
# names(mutation_colors) <- gsub("_"," ",names(mutation_colors))
oncomat.plot <- gsub("_"," ",oncomat.plot)
# browser()
if (length(oncomat.plot) < 1) {
stop("No samples to plot.")
}
### Column labels get cluttered if too many samples
if (!is.logical(show_sample_names)) {
show_sample_names=T
if (ncol(oncomat.plot) > 20) {
show_sample_names=F
}
}
myanno=NULL
if (!is.null(clin_data)) {
# browser()
anno_data <- data.frame(clin_data[match(colnames(oncomat.plot), clin_data$Tumor_Sample_Barcode),],stringsAsFactors = F)
row.names(anno_data) <- anno_data$Tumor_Sample_Barcode
anno_data <- anno_data[,!colnames(anno_data) %in% "Tumor_Sample_Barcode", drop=F]
if (ncol(anno_data) > 0) {
myanno <- HeatmapAnnotation(df=anno_data,col = clin_data_colors)
}
}
## Show total burden for top annotation
variant_type_data <- data.frame(maf.filtered@variant.classification.summary)
rownames(variant_type_data) <- variant_type_data$Tumor_Sample_Barcode
colnames(variant_type_data) <- gsub("_"," ",colnames(variant_type_data))
variant_type_data <- variant_type_data[,c(-1,-ncol(variant_type_data))]
variant_type_data <- variant_type_data[match(colnames(oncomat.plot), rownames(variant_type_data)),
rev(order(colSums(variant_type_data)))]
# browser()
var_anno_colors <- mutation_colors[match(colnames(variant_type_data), names(mutation_colors))]
top_ha = HeatmapAnnotation("Total\nMutations" = anno_barplot(variant_type_data, gp = gpar(fill = var_anno_colors), border = F),
annotation_name_side = "left",annotation_name_rot=90,annotation_name_gp = gpar(cex=0.7))
# browser()
pct_anno <- paste0(prettyNum(frac_mut$frac_mut[match(onco_genes, frac_mut$Hugo_Symbol)]*100,digits=1),"%")
left_ha = rowAnnotation("Cohort Pct"=anno_text(pct_anno,gp = gpar(cex=0.7)), show_annotation_name=F)
# print(oncomat.plot)
### Make the oncoplot
onco_base_default <- oncoPrint(oncomat.plot, alter_fun = alter_fun, col=mutation_colors, row_order=1:nrow(oncomat.plot),
name="oncoplot",
show_pct = F,
row_split=split_idx,
row_title = NULL,
bottom_annotation = myanno,
top_annotation = top_ha,
left_annotation = left_ha,
show_column_names = show_sample_names,
alter_fun_is_vectorized = T)#,
if ( ! is.null(savename) ) {
# save_name <- paste0(out_dir,"/oncoplot.",cohort_freq_thresh,".pdf")
# browser()
anno_height=ifelse(!is.null(clin_data), min(c(4, 0.5*ncol(clin_data))), 0)
onco_height=max(round(0.1*nrow(oncomat.plot),0),4) + anno_height
# onco_width=onco_height*0.75 + anno_height*1.2
onco_width=ncol(oncomat.plot)*0.01 + anno_height*1.2
pdf(file = savename,height=onco_height,width=onco_width)
draw(onco_base_default)
dev.off()
}
### Return the oncoplot (if function is pointed to a variable)
invisible(onco_base_default)
}
make_oncoplot2 <- function(maf.filtered, cohort_freq_thresh = 0.1, auto_adjust_threshold=T,
use_clinvar_anno=F, title_text="",
genes_to_plot=NULL, include_all=F, include_all_genes=F,
show_sample_names=NULL,
total_mut=T, total_mut_plotarea=unit(2,"inches"),
clin_data=NULL,clin_data_colors=NULL,
custom_column_order=NULL, full_output=F,
savename=NULL) {
require(ComplexHeatmap)
### Read in MAF file
# maf.filtered <- read.maf(maf_file)
if (! is.null(genes_to_plot)) {
if (class(genes_to_plot)=="character") {
if (length(genes_to_plot)==1) {
## Then it's either a file name or a single gene
if (file.exists(genes_to_plot)) {
### Need to parse file type and read accordingly; assuming tsv for now
gene_data <- read.table(genes_to_plot,sep="\t", header=T)
if (sum(c("Hugo_Symbol","Reason") %in% colnames(gene_data)) != 2) {
stop("Can't find Hugo Symbol or Reason in custom gene input.")
}
genes_for_oncoplot <- gene_data
} else {
stop(paste0("Can't find file: ",genes_to_plot))
}
} else {
genes_for_oncoplot <- data.frame(Hugo_Symbol=genes_to_plot,Reason="Selected Genes")
}
} else if (class(genes_to_plot) %in% "data.frame") {
genes_for_oncoplot <- genes_to_plot
if (! "Reason" %in% colnames(genes_for_oncoplot)) {
genes_for_oncoplot$Reason <- "Selected Genes"
}
} else {
stop(paste0("Don't know what to do with 'genes_to_plot' of class: ",class(genes_to_plot)))
}
genes_for_oncoplot <- genes_for_oncoplot[,c("Hugo_Symbol","Reason")]
genes_for_oncoplot <- data.frame(apply(genes_for_oncoplot,2,as.character), stringsAsFactors = F)
if ( ! include_all_genes ) {
genes_for_oncoplot <- genes_for_oncoplot[genes_for_oncoplot$Hugo_Symbol %in% maf.filtered@gene.summary$Hugo_Symbol, ]
}
} else {
genes_for_oncoplot <- data.frame(Hugo_Symbol=c(), Reason=c())
}
frac_mut <- data.frame(Hugo_Symbol=maf.filtered@gene.summary$Hugo_Symbol,
frac_mut=(maf.filtered@gene.summary$AlteredSamples/as.numeric(maf.filtered@summary$summary[3])),
stringsAsFactors = F)
if (! is.null(cohort_freq_thresh)) {
### Structure info about the fraction of the cohort that has each gene mutated
# browser()
# target_frac = sort(frac_mut$frac_mut, decreasing = T)[min(50,nrow(frac_mut))]
# cohort_freq_thresh <- max(c(cohort_freq_thresh,target_frac))
### Select genes based on the frequency threshold
freq_genes <- frac_mut$Hugo_Symbol[frac_mut$frac_mut > cohort_freq_thresh]
if (length(freq_genes) == 0) {
stop("No genes to plot; change the frequency threshold to include more genes.")
}
if (length(freq_genes) > 200) {
# target_frac = round(sort(frac_mut$frac_mut, decreasing = T)[min(50,nrow(frac_mut))],2)
ngene_max=100
# auto_adjust_threshold=T
target_frac = round(sort(frac_mut$frac_mut, decreasing = T)[min(ngene_max,nrow(frac_mut))],digits = 2)
if (auto_adjust_threshold) {
# browser()
cohort_freq_thresh <- max(c(cohort_freq_thresh,target_frac))
}
warning(paste0("Too many genes for oncoplot. Setting the cohort mutated fraction to > ", target_frac))
freq_genes <- frac_mut$Hugo_Symbol[frac_mut$frac_mut >= cohort_freq_thresh]
freq_genes <- freq_genes[1:ngene_max]
# return(NA)
}
gene_list <- list(freq_genes)
reasons <- paste0("Cohort Freq > ",cohort_freq_thresh)
### Collect genes to plot
for (i in 1:length(gene_list)) {
if (is.na(gene_list[[i]][1])) {
next
}
genes_for_oncoplot <- rbind(genes_for_oncoplot,
data.frame(Hugo_Symbol=gene_list[[i]],
Reason=reasons[i]))
}
genes_for_oncoplot <- cbind(genes_for_oncoplot,
frac=frac_mut$frac_mut[match(genes_for_oncoplot$Hugo_Symbol, frac_mut$Hugo_Symbol)])
genes_for_oncoplot <- genes_for_oncoplot[order(genes_for_oncoplot$Reason, -genes_for_oncoplot$frac),]
}
# browser()
### Split the oncoplot based on the reason for picking the gene
### Here, we're only picked based on the frequency
### But this framework is useful for plotting genes picked using various criteria
split_idx=factor(genes_for_oncoplot$Reason)
split_colors <- rainbow(length(levels(split_idx)))
names(split_colors) <- as.character(genes_for_oncoplot$Reason[!duplicated(genes_for_oncoplot$Reason)])
split_colors <- list(Reason=split_colors)
# source("scripts/helper_functions.oncoplot.R")
### Make matrix to plot, and order it correctly
if (use_clinvar_anno) {
oncomat <- createOncoMatrix_CLINSIG(maf.filtered, g=genes_for_oncoplot$Hugo_Symbol, add_missing = T)$oncoMatrix
} else {
oncomat <- createOncoMatrix(maf.filtered, g=genes_for_oncoplot$Hugo_Symbol, add_missing = T)$oncoMatrix
}
oncomat <- oncomat[match(genes_for_oncoplot$Hugo_Symbol,rownames(oncomat)), , drop=F]
onco_genes <- rownames(oncomat)
# browser()
if (include_all) {
### createOncoMatrix drops empty samples, so this adds them back in
# all_wes_samples <- as.character(sample_info.exome$Tumor_Sample_Barcode[!is.na(sample_info.exome$Tumor_Sample_Barcode)])
all_wes_samples <- levels(maf.filtered@variants.per.sample$Tumor_Sample_Barcode)
extra_samples <- setdiff(all_wes_samples, colnames(oncomat) )
print(paste0("Adding back ", length(extra_samples), " samples with no reported mutations..."))
empty_data <- matrix(data = "", nrow=nrow(oncomat), ncol=length(extra_samples), dimnames=list(rownames(oncomat), extra_samples))
oncomat <- cbind(oncomat, empty_data)
}
# if (include_all_genes) {
# ### createOncoMatrix drops empty samples, so this adds them back in
# # all_wes_samples <- as.character(sample_info.exome$Tumor_Sample_Barcode[!is.na(sample_info.exome$Tumor_Sample_Barcode)])
# all_genes <- unique(input_genes$Hugo_Symbol)
# extra_genes <- setdiff(all_genes, rownames(oncomat) )
# print(paste0("Adding back ", length(extra_genes), " genes with no reported mutations..."))
# empty_data <- matrix(data = "", nrow=length(extra_genes), ncol=ncol(oncomat), dimnames=list(extra_genes, colnames(oncomat)))
# oncomat <- rbind(oncomat, empty_data)
# }
if (!is.null(custom_column_order)) {
custom_order <- match(custom_column_order, colnames(oncomat), nomatch=0)
# browser()
oncomat <- oncomat[,custom_order]
}
oncomat.plot <- oncomat
### Set the height of the plot based on number of genes
onco_height=NULL
if (is.null(onco_height)) {
onco_height=max(round(0.2*nrow(oncomat.plot),0),6)
}
### Make the mutation type names prettier by removing the underscore
# my_mut_col <- mutation_colors
# names(mutation_colors) <- gsub("_"," ",names(mutation_colors))
oncomat.plot <- gsub("_"," ",oncomat.plot)
# browser()
if (ncol(oncomat.plot) < 1) {
stop("No samples to plot.")
}
### Column labels get cluttered if too many samples
if (!is.logical(show_sample_names)) {
show_sample_names=T
if (ncol(oncomat.plot) > 20) {
show_sample_names=F
}
}
# if (show_burden) {
variant_type_data <- data.frame(maf.filtered@variant.classification.summary)
unmut_samples <- setdiff(levels(variant_type_data$Tumor_Sample_Barcode),as.character(variant_type_data$Tumor_Sample_Barcode))
if (length(unmut_samples) > 0) {
unmut_data <- data.frame(unmut_samples, matrix(0, nrow=length(unmut_samples), ncol=ncol(variant_type_data)-1))
colnames(unmut_data) <- colnames(variant_type_data)
variant_type_data <- rbind(variant_type_data,unmut_data)
}
rownames(variant_type_data) <- variant_type_data$Tumor_Sample_Barcode
colnames(variant_type_data) <- gsub("_"," ",colnames(variant_type_data))
variant_type_data <- variant_type_data[,c(-1,-ncol(variant_type_data)), drop=F]
variant_type_data <- variant_type_data[match(colnames(oncomat.plot), rownames(variant_type_data)),
rev(order(colSums(variant_type_data))), drop=F]
# browser()
var_anno_colors <- mutation_colors[match(colnames(variant_type_data), names(mutation_colors))]
ha = NULL
if (!is.null(total_mut)) {
if (file.exists(as.character(total_mut))) {
mb_covered <- compute_exome_coverage(total_mut)/1e6
variant_type_data <- variant_type_data/mb_covered
total_mut=T
} else {
total_mut=F
}
if (total_mut) {
ha = HeatmapAnnotation(Burden = anno_barplot(variant_type_data,
gp = gpar(fill = var_anno_colors),
border = F,
height=total_mut_plotarea)
)
}
}
# }
pct_anno <- paste0(prettyNum(frac_mut$frac_mut[match(onco_genes, frac_mut$Hugo_Symbol)]*100,digits=1),"%")
left_ha = rowAnnotation("Cohort Pct"=anno_text(pct_anno,gp = gpar(cex=0.7)), show_annotation_name=F)
myanno=NULL
if (!is.null(clin_data)) {
# browser()
if (! "Tumor_Sample_Barcode" %in% colnames(clin_data)) {
stop("'clin_dat' must contain a column named 'Tumor_Sample_Barcode'.")
}
anno_data <- data.frame(clin_data[match(colnames(oncomat.plot), clin_data$Tumor_Sample_Barcode, nomatch=0),],stringsAsFactors = F)
rownames(anno_data) <- anno_data$Tumor_Sample_Barcode
extra_ids <-setdiff(colnames(oncomat.plot), rownames(anno_data))
emptydat <- data.frame(matrix(nrow=length(extra_ids), ncol=ncol(anno_data)), stringsAsFactors = F)
colnames(emptydat) <- colnames(anno_data)
emptydat[,"Tumor_Sample_Barcode"] <- extra_ids
anno_data <- rbind(anno_data,
emptydat)
row.names(anno_data) <- anno_data$Tumor_Sample_Barcode
anno_data <- anno_data[,!colnames(anno_data) %in% "Tumor_Sample_Barcode", drop=F]
if (ncol(anno_data) > 0) {
myanno <- HeatmapAnnotation(df=anno_data,col = clin_data_colors)
}
}
### Make the oncoplot
# browser()
onco_base_default <- oncoPrint(oncomat.plot, alter_fun = alter_fun,
col=mutation_colors,
# row_order=1:nrow(oncomat.plot),
column_order = 1:ncol(oncomat.plot),
name="oncoplot",
row_title=title_text,
show_pct = F,
row_split=split_idx,
bottom_annotation = myanno,
left_annotation = rowAnnotation(Reason = split_idx, col=split_colors, annotation_width = unit(0.3, "mm"), show_annotation_name=F),
right_annotation = left_ha,
top_annotation = ha,
show_column_names = show_sample_names)#,
# draw(onco_base_default)
# column_names_rot = 30,
# column_gap = unit(0.0001,"npc"),
# width = unit(0.75, "npc"))
# print(dim(oncomat))
### Save the oncoplot
if (full_output) {
return_val <- list(onco_base_default, oncomat.plot)
} else {
return_val <- onco_base_default
}
if ( ! is.null(savename) ) {
# save_name <- paste0(out_dir,"/oncoplot.",cohort_freq_thresh,".pdf")
onco_height=max(round(0.2*nrow(oncomat.plot),0),6)
onco_width=max(c(onco_height*0.6,max(round(0.4*ncol(oncomat.plot),0))))
pdf(file = savename,height=onco_height,width=onco_width)
draw(onco_base_default)
dev.off()
}
invisible(return_val)
}
make_burden_plot <- function(maf.filtered, plotType=NULL, mb_covered=NULL,
sample_order=NULL,
save_data_to_file=NULL,
add_median=T) {
require(dplyr)
num_var_data <- maf.filtered@variants.per.sample
colnames(num_var_data) <- c("Tumor_Sample_Barcode","Variants_filtered")
num_var_data$mut_burden_count <- num_var_data$Variants_filtered
num_var_data$mut_burden <- num_var_data$mut_burden_count
y_label_text="Mutation Count"
if (is.numeric(mb_covered)) {
print("Normalizing mutation count by covered bases...")
num_var_data$mut_burden <- num_var_data$mut_burden/mb_covered
y_label_text="Mutation Burden (mutations/Mb)"
}
nsamples=nrow(num_var_data)
if (is.null(plotType)) {
plotType <- ifelse(nrow(num_var_data) > 15, "Dotplot", "Barplot")
print(paste0("Using plot type: ", plotType))
}
# browser()
## Re-jigger the factor levels so they're ordered by decreasing mutation burden (for the plotting)
num_var_data$Tumor_Sample_Barcode <- factor(num_var_data$Tumor_Sample_Barcode,
levels=num_var_data$Tumor_Sample_Barcode[order(num_var_data$mut_burden, decreasing = T)])
########################################################
#### 5. Generate plots for mutation burden
## Pick colors
median_mut_burdens <- num_var_data %>% summarise(median=median(mut_burden))
num_var_data$xlabel <- factor(num_var_data$xlabel,
levels=num_var_data$xlabel[order(num_var_data$mut_burden, decreasing = T)])
num_var_data$hoverlabel <- paste0("Sample: ",num_var_data$Tumor_Sample_Barcode,"\nMutations: ", num_var_data$mut_burden)
### Mutation burden stacked with variant classification counts
### Works better for smaller cohorts, say < 20
variant_type_per_sample <- as.data.frame(maf.filtered@variant.classification.summary)
var_type.melt <- reshape2::melt(variant_type_per_sample, id.vars="Tumor_Sample_Barcode",variable.name="classification",value.name="mutation_count")
var_type.melt$mut_burden <- var_type.melt$mutation_count
if (is.numeric(mb_covered)) {
var_type.melt$mut_burden <- var_type.melt$mut_burden/mb_covered
}
plotdata <- var_type.melt[var_type.melt$classification != "total",]
tsb_order <- variant_type_per_sample$Tumor_Sample_Barcode[order(variant_type_per_sample$total, decreasing = T)]
if (!is.null(sample_order)) {
tsb_order <- sample_order
}
plotdata$Tumor_Sample_Barcode <- factor(as.character(plotdata$Tumor_Sample_Barcode),
levels=tsb_order)
plotdata$classification <- gsub("_"," ",plotdata$classification)
class_means <- plotdata %>% group_by(classification) %>% summarise(mean=mean(mut_burden))
plotdata$classification <- factor(as.character(plotdata$classification),
levels=class_means$classification[order(class_means$mean, decreasing = F)])
my_class_colors <- mutation_colors[names(mutation_colors) %in% levels(plotdata$classification)]
plotdata$hoverlabel <- paste0("Sample: ",plotdata$Tumor_Sample_Barcode,"\nMutations: ", plotdata$mut_burden)
if (plotType=="Barplot") {
if (length(unique(plotdata$Tumor_Sample_Barcode)) <= 20) {
xaxis_text <- element_text(angle=30, hjust=1)
} else {
xaxis_text <- element_blank()
}
burden_plot <- ggplot(plotdata, aes(x=Tumor_Sample_Barcode, y=mut_burden, text=hoverlabel)) +
geom_bar(aes(fill=classification), stat="identity",width=1,size=0.3, color="black") +
scale_fill_manual(values=my_class_colors) +
theme_linedraw(base_size = 12) +
xlab("") + ylab(y_label_text) +
# geom_hline(data = median_mut_burdens, aes(yintercept=median),linetype="dashed", color="grey60") + ### Is screwed up with ggplotly
theme(
axis.text.x = xaxis_text,
axis.ticks.x = element_blank(),
legend.position = "right",
legend.text = element_text(size=8),
legend.title = element_blank(),
legend.key.height = unit(0.01,"npc"),
legend.key.width = unit(0.02,"npc"),
plot.title = element_text(hjust = 0.5),
panel.border = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_blank())
} else {
require(ggbeeswarm)
### Mutation Burden - Scatter/Dot plot
### Works better for larger cohorts
alpha_val=1
point_cex=2
if (nrow(num_var_data) > 200) {
alpha_val=0.5
} else if (nrow(num_var_data) > 20) {
alpha_val=0.8
}
burden_plot <- ggplot(num_var_data, aes(x=1, y=mut_burden, text=hoverlabel)) +
# geom_beeswarm(color="blue3",cex=2,size=5,dodge.width=0.2,priority='density', alpha=alpha_val) +
geom_quasirandom(color="blue3",width=0.3,size=5,alpha=alpha_val, method="quasirandom", bandwidth=0.1) +
# stat_summary(fun.y = median, fun.ymin = median, fun.ymax = median,
# geom = "crossbar", width = 0.7, color="gray70", size = 0.2) +
scale_y_log10()+
theme_linedraw(base_size = 12) +
ggtitle("Mutation Burden") +
ylab(y_label_text) + xlab("") +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
}
median_val=median(var_type.melt[var_type.melt$classification== "total","mut_burden"])
if (is.numeric(add_median)) {
median_val=add_median
add_median=T
}
if (add_median) {
median_mut_burdens <- data.frame(median=median_val)
burden_plot <- burden_plot + geom_hline(data = median_mut_burdens, aes(yintercept=median),linetype="dashed", color="grey60")
}
## Write data to a file for external plotting if desired
if (!is.null(save_data_to_file)) {
if (dir.exists(dirname(save_data_to_file))) {
outdata <- as.data.frame(maf.filtered@variant.classification.summary)
outdata$total_per_mb <- outdata$total/mb_covered
outdata$mb_covered <- mb_covered
print(paste0("Saving plot data to ", save_data_to_file))
write.table(outdata, file = save_data_to_file, sep="\t", quote=F,row.names = F)
} else {
warning("Path for output data file not found! Skipping...")
}
}
return(burden_plot)
}
plot_silent_nonsilent <- function(mymaf, savename=NULL, returndata=F) {
nonsilent_summary <- mymaf@variant.classification.summary[,c("Tumor_Sample_Barcode","total")]
nonsilent_summary$type <- "Non-Silent"
silent_classif_data <- mymaf@maf.silent %>% group_by(Tumor_Sample_Barcode, Variant_Classification) %>% summarise(count=n())
silent_classif_data <- reshape2::dcast(silent_classif_data,Tumor_Sample_Barcode ~ Variant_Classification, value.var = "count")
silent_summary <- data.frame(Tumor_Sample_Barcode = silent_classif_data$Tumor_Sample_Barcode,
total = rowSums(silent_classif_data[,-1], na.rm=T),
type = "Silent"
)
# browser()
plotdata <- rbind(nonsilent_summary, silent_summary)
tots <- plotdata %>% group_by(Tumor_Sample_Barcode) %>% summarise(tot=sum(total))
plotdata$Tumor_Sample_Barcode <- factor(plotdata$Tumor_Sample_Barcode,
levels=as.character(tots$Tumor_Sample_Barcode)[order(tots$tot,decreasing = T)])
myplot <- ggplot(plotdata, aes(x=Tumor_Sample_Barcode, y = total, fill=type)) +
geom_col() + scale_fill_brewer(palette="Set1") +
theme_linedraw(base_size = 12) +
xlab("")+
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle=30, hjust = 1, size=10),
axis.ticks.x = element_blank())
if (!is.null(savename)) {
if (! dir.exists(dirname(savename))) {dir.create(dirname(savename), recursive = T)}
ggsave(savename,width=6, height=6)
}
return_val <- myplot
if (returndata) {
return_val <- list(plot=myplot, data=plotdata)
}
return(return_val)
}
make_overlap_plot <- function(mymaf, use_silent_mutations=F,
summarize_by="gene",
plotType=c("ribbon","heatmap"),
savename="overlap_plot.pdf",
savewidth=8, saveheight=8) {
require(dplyr)
mafdata <- mymaf@data
if (use_silent_mutations) {
mafdata <- rbind(mafdata, mymaf@maf.silent)
}
mafdata_by_sample <- split(mafdata, mafdata$Tumor_Sample_Barcode)
if (summarize_by=="gene") {
id_cols <- c("Chromosome","Start_Position","End_Position","Hugo_Symbol")
} else if (summarize_by=="mutation") {
id_cols <- c("Chromosome","Start_Position","End_Position","Hugo_Symbol","HGVSp_Short")
} else {
stop("'summarize_by' must be either 'gene' or 'mutation'")
}
mutations_list <- lapply(mafdata_by_sample, function(currmafdata, mycols) {
id_string <- apply(currmafdata[,..mycols],1,paste0, collapse="_")
return(id_string)
}, id_cols)
pw_combinations <- matrix(0,nrow = length(mutations_list),ncol = length(mutations_list))
colnames(pw_combinations) <- names(mutations_list)
rownames(pw_combinations) <- names(mutations_list)
for ( row_idx in 1:nrow(pw_combinations) ) {
for (col_idx in 1:ncol(pw_combinations) ) {
# if (!row_idx==col_idx) {
# overlap_val=sum(mutations_list[[ rownames(pw_combinations)[row_idx] ]]$id_str %in% mutations_list[[colnames(pw_combinations)[col_idx] ]]$id_str)
overlap_val=length(intersect(mutations_list[[ rownames(pw_combinations)[row_idx] ]],mutations_list[[colnames(pw_combinations)[col_idx] ]]))
pw_combinations[row_idx,col_idx]=overlap_val
# }
}
}
hm_data <- pw_combinations
pdf(savename, width=savewidth, height=saveheight)
if ("heatmap" %in% plotType) {
require(ComplexHeatmap)
# pheatmap(hm_data,cluster_rows = T, cluster_cols = T,
# clustering_method = "complete",
# main = "Hierarchically Clustered")
myhm <- Heatmap(hm_data,
cluster_rows = T, cluster_columns = T,
column_title = paste0("Number of ",ifelse(summarize_by=="gene","mutated genes","variants")," in common between samples (pair-wise)")
)
draw(myhm)
}
if ("ribbon" %in% plotType) {
require(circlize)
count_clustering <- hclust(dist(pw_combinations), method = "complete")
cluster_order <- count_clustering$labels[count_clustering$order]
chordData <- pw_combinations
grid.col <- rainbow(n=nrow(chordData))
names(grid.col) <- cluster_order
chordDiagram(chordData,order=cluster_order,grid.col=grid.col,
annotationTrack = c("grid","axis"),
preAllocateTracks = list(track.height = max(strwidth(unlist(dimnames(chordData))))))
circos.track(track.index = 1, panel.fun = function(x, y) {
circos.text(CELL_META$xcenter, CELL_META$ylim[1], CELL_META$sector.index,
facing = "clockwise", niceFacing = TRUE, adj = c(-0.2, 0))
}, bg.border = NA) # here set bg.border to NA is important
}
dev.off()
invisible(NULL)
}
make_single_ribbon_plot <- function(maf, onco_genes=NULL, save_name=NULL, ribbon_color=NULL,
pval_high=0.1, ## All interactions with less than this p-value will be shown
pval_low=0.05, ## Links with p-value less than this will be highlighted with a dashed border
plot_frac_mut_axis=TRUE, ## Whether or not to draw a numerical axis on the perimeter
rotate_plot_degrees=0, ## For custom rotation
shrink_factor=1.3, # Higher = more shrinkage; use to control whitespace (or lack thereof) around figure
scale_ribbon_to_fracmut=TRUE, ## Whether or not to scale ribbon widths to their frequency
sig_colors=NULL, ## Vector of 4 colors for coloring significance
gene_colors=NULL ## color(s) for gene blocks
) {
# pval_low <- 0.05
# browser()
require(circlize)
if (!is.null(save_name)) {
if (! dir.exists(dirname(save_name))) {
dir.create(dirname(save_name))
}
plot_file <- gsub(".pdf",".interactions.pdf",save_name)
pdf(file = plot_file,height=5,width=5)
} else {
pdf(file = NULL)
}
# browser()
# if (is.null(onco_genes)) {
# onco_genes = maf@gene.summary$Hugo_Symbol
# }
som_int <- somaticInteractions(maf = maf, genes=onco_genes, pvalue = c(pval_low, pval_high))
dev.off()
# browser()
cooccur_data <- som_int
cooccur_data$pair_string <- apply(cooccur_data[,1:2], 1, function(x) {paste0(sort(x), collapse="_")})
cooccur_data$popfrac <- NA
cooccur_idx <- cooccur_data$Event=="Co_Occurence"
mut_excl_idx = cooccur_data$Event=="Mutually_Exclusive"
if (scale_ribbon_to_fracmut) {
cooccur_data$popfrac1[cooccur_idx] <- unlist(cooccur_data[cooccur_idx,"11"]/as.numeric(maf@summary$summary[3]))
cooccur_data$popfrac2[cooccur_idx] <- cooccur_data$popfrac1[cooccur_idx]
cooccur_data$popfrac1[mut_excl_idx] <- unlist(cooccur_data[mut_excl_idx,"10"]/as.numeric(maf@summary$summary[3]))
cooccur_data$popfrac2[mut_excl_idx] <- unlist(cooccur_data[mut_excl_idx,"01"]/as.numeric(maf@summary$summary[3]))
} else {
cooccur_data$popfrac1 <- 1
cooccur_data$popfrac2 <- 1
}
chord_data <- cooccur_data[,c("gene1","gene2","popfrac1","popfrac2","pValue","Event")]
chord_data[which(is.na(chord_data[,3])),3] <- 0
if (is.null(sig_colors)) {
sig_colors = RColorBrewer::brewer.pal(5, "BrBG")
sig_colors <- sig_colors[-3]
}
names(sig_colors) <- paste0(rep(c("Co-occurence", "Mutually Exclusive"), each=2), " p-val < ",c(pval_low, pval_high, pval_high, pval_low ))