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aya.library.R
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aya.library.R
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rm(list=ls())
gc()
ReQ_packages = c("dplyr", "tidyr", "magrittr", "data.table", "ggpubr", "prismatic", "ggplot2",
"RColorBrewer", "pheatmap", "VennDiagram")
for (pack in ReQ_packages) {
if(pack %in% rownames(installed.packages()) == FALSE) {
BiocManager::install(pack)
install.packages(pack)
suppressPackageStartupMessages(library(pack, character.only = TRUE))
} else {
suppressPackageStartupMessages(library(pack, character.only = TRUE))
}
}
## Auxiliary ##
aya_palette <- c("#E38A42FF", "#42B3E3FF",
"#E35DBFFF", "#97C75DFF",
"#C75D84FF", "#E6D250FF",
"#B84818FF", "#A477C9FF",
"#5D9483FF", "#DBD086FF",
"#86B2DBFF", "#BFEB9DFF")
## Functions ##
# Show aya color palette and perform colorblind test
aya_colors <- function() {
print("Arriba y abajo color palette:")
print(color(aya_palette))
print("Colorblind checks:")
print(color(dichromat::dichromat(aya_palette, "protan")))
print(color(dichromat::dichromat(aya_palette, "deutan")))
print(color(dichromat::dichromat(aya_palette, "tritan")))
}
# Read Arriba output files
read_fusions <- function(working_dir, pattern = "_fusions.tsv$") {
fusion_files <- list.files(working_dir, pattern = pattern,
recursive = TRUE, full.names = TRUE)
cat("Fusion files found:\n")
print(fusion_files)
for (i in 1:length(fusion_files)) {
# Read Arriba results
path2data <- fusion_files[i]
name <- gsub(".*/|_fusions.tsv","", path2data, perl=TRUE)
data <- fread(as.character(path2data), header=TRUE)
colnames(data)[1] <- "gene1"
# Create column with name
data$Sample <- name
data$gene1_gene2 <- paste0(data$gene_id1, "_", data$gene_id2)
# Attach to concatenated table
ifelse(i == 1, fusions.df <- data, fusions.df <- rbind(fusions.df, data))
}
return(fusions.df)
}
# Create fusion heatmap
fusion_heatmap <- function(data, metadata = NULL, fusion_mode = "fusion", top_features = 50, custom_order_par = NULL) {
## metadata: data.frame with rownames = Sample IDs, and columns the metadata parameters to be annotated
# Initial parameter setting
ifelse(top_features > 50, show_rownames = FALSE, show_rownames = TRUE)
if (fusion_mode == "fusion") {
# Pre-processing
fusions.tbl <- data %>%
dplyr::rowwise() %>%
dplyr::mutate(fusion_mode_par = paste0(gene1, "_", gene2))
fusions.tbl <- unique(fusions.tbl[,c("Sample", "fusion_mode_par")])
fusions.tbl <- fusions.tbl %>%
group_by(Sample, fusion_mode_par) %>%
dplyr::summarise(num = n()) %>%
dplyr::ungroup() %>%
dplyr::group_by(fusion_mode_par) %>%
dplyr::mutate(tot_num = sum(num)) %>%
dplyr::ungroup() %>%
dplyr::arrange(desc(tot_num)) %>%
dplyr::filter(tot_num > 1)
} else if (fusion_mode == "gene") {
# Pre-processing
fusions.tbl <- data[,c("Sample", "gene1")]
colnames(fusions.tbl)[2] <- "gene2"
fusions.tbl <- rbind(fusions.tbl, data[,c("Sample", "gene2")])
colnames(fusions.tbl)[2] <- "fusion_mode_par"
fusions.tbl <- unique(fusions.tbl)
fusions.tbl <- fusions.tbl %>%
dplyr::filter(fusion_mode_par != ".") %>%
group_by(Sample, fusion_mode_par) %>%
dplyr::summarise(num = n()) %>%
dplyr::ungroup() %>%
dplyr::group_by(fusion_mode_par) %>%
dplyr::mutate(tot_num = sum(num)) %>%
dplyr::ungroup() %>%
dplyr::arrange(desc(tot_num)) %>%
dplyr::filter(tot_num > 1)
} else {
stop('Error: `fusion_mode` parameter not specfified correctly; choose from: "fusion", "gene"')
}
# Top feature to plot selection
topfeat <- fusions.tbl[, c("fusion_mode_par", "tot_num")]
topfeat <- unique(topfeat)
topfeat <- topfeat$fusion_mode_par[1:top_features]
# Pivot table
fusions.tbl.piv <- fusions.tbl[fusions.tbl$fusion_mode_par %in% topfeat,] %>%
dplyr::select(-tot_num) %>%
tidyr::pivot_wider(names_from = "Sample", values_from = "num") %>%
dplyr::mutate(across(.cols = everything(), ~replace_na(., 0)))
fusions.mat <- as.matrix(fusions.tbl.piv[,2:dim(fusions.tbl.piv)[2]])
rownames(fusions.mat) <- fusions.tbl.piv$fusion_mode_par
# Pre-processing for annotation
if (!is.null(metadata)) {
# Create annotation color scheme for heatmap
counter <- 1
anno_colors <- list()
for (i in 1:dim(metadata)[2]) {
j <- length(unique(metadata[,i]))
if (j == 1) {
assign(paste0("var",i), aya_palette[counter])
} else {
if (counter < 12 && (counter+j-1) <= 12) {
assign(paste0("var",i), aya_palette[counter:(counter+j-1)])
} else if (counter <= 12 && (counter+j-1) > 12) {
assign(paste0("var",i), aya_palette[c(counter:12, 1:((counter+j-1) %% 12))])
}
}
anno_colors$tmp <- get(paste0("var",i))
names(anno_colors)[i] <- colnames(metadata)[i]
counter <- (counter + j) %% 12
}
# Custom ordering
if (!is.null(custom_order_par)) {
custom_ord <- metadata$Sample[sort(metadata[,custom_order_par], index.return = TRUE)$ix]
custom_ord <- custom_ord[custom_ord %in% colnames(fusions.mat)]
fusions.mat <- fusions.mat[,custom_ord]
}
# Draw heatmap w/ annotation
return(
pheatmap(fusions.mat,
cluster_rows = TRUE,
cluster_cols = FALSE,
annotation_col = metadata,
annotation_colors = anno_colors,
show_rownames = show_rownames,
breaks = c(0,0.1,1),
color = c("white", "#a3c771")
)
)
} else {
# Draw heatmap w/out annotation
return(
pheatmap(fusions.mat,
cluster_rows = TRUE,
cluster_cols = TRUE,
show_rownames = show_rownames,
breaks = c(0,0.1,1),
color = c("white", "#a3c771")
)
)
}
}
# Perform fusion statistical tests (Fisher's)
fusion_stat_tests <- function(data, metadata, metadata_par, fusion_mode = "fusion", top_features = 100, pval = 0.1) {
if (fusion_mode == "fusion") {
# Pre-processing
fusions.tbl <- data %>%
dplyr::rowwise() %>%
dplyr::mutate(fusion_mode_par = paste0(gene1, "_", gene2))
fusions.tbl <- unique(fusions.tbl[,c("Sample", "fusion_mode_par")])
fusions.tbl <- fusions.tbl %>%
group_by(Sample, fusion_mode_par) %>%
dplyr::summarise(num = n()) %>%
dplyr::ungroup() %>%
dplyr::group_by(fusion_mode_par) %>%
dplyr::mutate(tot_num = sum(num)) %>%
dplyr::ungroup() %>%
dplyr::arrange(desc(tot_num)) %>%
dplyr::filter(tot_num > 1)
} else if (fusion_mode == "gene") {
# Pre-processing
fusions.tbl <- data[,c("Sample", "gene1")]
colnames(fusions.tbl)[2] <- "gene2"
fusions.tbl <- rbind(fusions.tbl, data[,c("Sample", "gene2")])
colnames(fusions.tbl)[2] <- "fusion_mode_par"
fusions.tbl <- unique(fusions.tbl)
fusions.tbl <- fusions.tbl %>%
dplyr::filter(fusion_mode_par != ".") %>%
group_by(Sample, fusion_mode_par) %>%
dplyr::summarise(num = n()) %>%
dplyr::ungroup() %>%
dplyr::group_by(fusion_mode_par) %>%
dplyr::mutate(tot_num = sum(num)) %>%
dplyr::ungroup() %>%
dplyr::arrange(desc(tot_num)) %>%
dplyr::filter(tot_num > 1)
} else {
stop('Error: `fusion_mode` parameter not specfified correctly; choose from: "fusion", "gene"')
}
# Pivot table
fusions.tbl.piv <- fusions.tbl %>%
dplyr::select(Sample, fusion_mode_par, num) %>%
tidyr::pivot_wider(names_from = "fusion_mode_par", values_from = "num") %>%
dplyr::mutate(across(.cols = everything(), ~replace_na(., 0)))
# Attach metadata
fusions.meta <- left_join(fusions.tbl.piv, metadata, by = "Sample")
for (i in 2:(top_features+1)) {
if (fisher.test(table(unlist(fusions.meta[,i]), unlist(fusions.meta[,metadata_par])))$p.value < pval) {
print(paste0("Found significant result for:", colnames(fusions.meta)[i]))
print(table(unlist(fusions.meta[,i]), unlist(fusions.meta[,metadata_par])))
print(fisher.test(table(unlist(fusions.meta[,i]), unlist(fusions.meta[,metadata_par]))))
cat("\n")
}
}
}