/
annotation_search_venn.R
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annotation_search_venn.R
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require(gdata)
require(dplyr)
require(clusterProfiler)
# Loads the OrgDb package containing the GO terms.
pkg <- snakemake@params[["OrgDb_package_name"]]
require(pkg, character.only = TRUE)
########################
###### Annotation ######
########################
# Loads the function that will make the annotation
anotation_search <- function(query = query,
annotation_file = annotation_file,
list_by = list_by,
output_name = output_name,
query_is_file = "FALSE",
query_is = query_is,
isoform_name = "TRUE"){
# Reads the MSD-Methylated sites. Check if it is a file or a vector with the MSD Sites
if (query_is_file == "TRUE"){
lista <- read.table(query, colClasses = "character")
lista <- as.character(lista$V1)
}else if (query_is_file == "FALSE"){
lista <- as.character(query[!is.na(query)])
}else{
print("Invalid 'query_is_file' argument!")
}
# Reads the annotation table of the genome
anot <- read.table(annotation_file,
header = T,
sep = "\t",
quote = "\"",
colClasses = "character",
fill = T)
colnames(anot) <- c("locus",
"locus_start",
"locus_end",
"locus_strand",
"distance_to_gene_kb",
"gene_name",
"chromosome",
"gene_start",
"gene_end",
"gene_strand",
"gene_description",
"gene_desc_unique_BioMart",
"gene_desc_unique_B2GO",
"gene_descriptiom_intersect",
"annotation_go_id",
"go_id_unique_BioMart",
"go_id_unique_B2GO",
"go_id_intersect",
"enzyme_code",
"enzyme_code_unique_BioMart",
"enzyme_code_unique_B2GO",
"enzyme_code_intersect",
"enzyme_description",
"enzyme_description_unique_BioMart",
"enzyme_description_unique_B2GO",
"enzyme_description_intersect",
"interpro_accession",
"interpro_type",
"interpro_name",
"interpro_signatures",
"interpro_go_id",
"interpro_go_term",
"interpro_go_category")
# Verify how the output should be generated
if (query_is == "mark"){
if (list_by == "mark"){
anotation <- anot[anot$locus %in% lista, ]
without_anot <- lista[lista %in% anot$locus == "FALSE"]
write.table(anotation,
paste(output_name, "by_marks.txt", sep = "_"),
col.names = T,
row.names = F,
quote = F,
sep = "\t")
# Warning about the sites without methylation and write a file with their names
if (length(without_anot) > 0){
print(paste("Warning!: There are",
length(without_anot),
"marks without annotations for the sample",
output_name,
"!"))
write.table(without_anot,
paste(output_name, "without_annotations.txt", sep = "_"),
col.names = F,
row.names = F,
quote = F,
sep = "\t")
}
}
else if (list_by == "gene"){
anotation <- anot[anot$locus %in% lista, ]
without_anot <- lista[lista %in% anot$locus == "FALSE"]
# Join the MSD-Methylated sites of the same genes.
gene_anot_complete <- data.frame()
for (i in unique(anotation$gene_name)){
gene <- anotation[anotation$gene_name == i, ]
locus_gene <- paste(gene$locus, collapse = ",")
locus_strand <- paste(gene$locus_strand, collapse = ",")
locus_distance <- paste(gene$distance_to_gene_kb, collapse = ",")
gene_anot <- gene[1, ]
gene_anot$locus <- locus_gene
gene_anot$locus_strand <- locus_strand
gene_anot$distance_to_gene_kb <- locus_distance
gene_anot_complete <- rbind(gene_anot_complete, gene_anot)
}
# Formats the file and remove unecessary columns
genes_names <- as.data.frame(gene_anot_complete$gene_name)
colnames(genes_names) <- "gene_name"
genes_annotations <- cbind(genes_names, gene_anot_complete)
genes_annotations <- genes_annotations[, -c(13:15, 17:19, 21:23, 25:27)]
if (isoform_name == "TRUE"){
isoform <- as.data.frame(paste(genes_annotations$gene_name, ".1", sep = ""))
colnames(isoform) <- "isoform_name"
genes_annotations <- cbind(isoform, genes_annotations)
}
print(paste(output_name, "by_genes.txt", sep = "_"))
write.table(genes_annotations,
paste(output_name, "by_genes.txt", sep = "_"),
col.names = T,
row.names = F,
quote = F,
sep = "\t")
if (length(without_anot) > 0){
print(paste("Warning!: There are",
length(without_anot),
"marks without annotations for the sample",
output_name,
"!"))
write.table(without_anot,
paste(output_name, "without_annotations.txt", sep = "_"),
col.names = F,
row.names = F,
quote = F,
sep = "\t")
}
}else{
print("Invalid 'list_by' argument!")
print("'list_by' accepts only 'gene' or 'mark'")
}
}else if (query_is == "gene"){
if (list_by == "mark"){
anotation <- anot[anot$gene_name %in% lista, ]
without_anot <- lista[lista %in% anot$gene_name == "FALSE"]
write.table(anotation,
paste(output_name, "by_marks.txt", sep = "_"),
col.names = T,
row.names = F,
quote = F,
sep = "\t")
if (length(without_anot) > 0){
print(paste("Warning!: There are",
length(without_anot),
"marks without annotations for the sample",
output_name,
"!"))
write.table(without_anot,
paste(output_name, "without_annotations.txt", sep = "_"),
col.names = F,
row.names = F,
quote = F,
sep = "\t")
}
}
else if (list_by == "gene"){
anotation <- anot[anot$gene_name %in% lista, ]
without_anot <- lista[lista %in% anot$gene_name == "FALSE"]
gene_anot_complete <- data.frame()
for (i in unique(anotation$gene_name)){
gene <- anotation[anotation$gene_name == i, ]
locus_gene <- paste(gene$locus, collapse = ",")
locus_strand <- paste(gene$locus_strand, collapse = ",")
locus_distance <- paste(gene$distance_to_gene_kb, collapse = ",")
gene_anot <- gene[1, ]
gene_anot$locus <- locus_gene
gene_anot$locus_strand <- locus_strand
gene_anot$distance_to_gene_kb <- locus_distance
gene_anot_complete <- rbind(gene_anot_complete, gene_anot)
}
genes_names <- as.data.frame(gene_anot_complete$gene_name)
colnames(genes_names) <- "gene_name"
genes_annotations <- cbind(genes_names, gene_anot_complete)
genes_annotations <- genes_annotations[, -c(13:15, 17:19, 21:23, 25:27)]
if (isoform_name == "TRUE"){
isoform <- as.data.frame(paste(genes_annotations$gene_name, ".1", sep = ""))
colnames(isoform) <- "isoform_name"
genes_annotations <- cbind(isoform, genes_annotations)
}
write.table(genes_annotations,
paste(output_name, "by_genes.txt", sep = "_"),
col.names = T,
row.names = F,
quote = F,
sep = "\t")
if (length(without_anot) > 0){
print(paste("Warning!: There are",
length(without_anot),
"marks without annotations for the sample",
output_name,
"!"))
write.table(without_anot,
paste(output_name, "without_annotations.txt", sep = "_"),
col.names = F,
row.names = F,
quote = F,
sep = "\t")
}
}
else{
print("Invalid 'list_by' argument!")
print("'list_by' accepts only 'gene' or 'mark'")
}
}else if (query_is == "transcript"){
lista2 <- character()
for (i in lista){
new_id <- unlist(strsplit(i, "[.]"))
new_id <- paste(new_id[1], new_id[2], sep = ".")
lista2 <- rbind(lista2, new_id)
}
lista <- as.character(lista2)
if (list_by == "mark"){
anotation <- anot[anot$gene_name %in% lista, ]
without_anot <- lista[lista %in% anot$gene_name == "FALSE"]
write.table(anotation,
paste(output_name, "by_marks.txt", sep = "_"),
col.names = T,
row.names = F,
quote = F,
sep = "\t")
if (length(without_anot) > 0){
print(paste("Warning!: There are",
length(without_anot),
"marks without annotations for the sample",
output_name,
"!"))
write.table(without_anot,
paste(output_name, "without_annotations.txt", sep = "_"),
col.names = F,
row.names = F,
quote = F,
sep = "\t")
}
}
else if (list_by == "gene"){
anotation <- anot[anot$gene_name %in% lista, ]
without_anot <- lista[lista %in% anot$gene_name == "FALSE"]
gene_anot_complete <- data.frame()
for (i in unique(anotation$gene_name)){
gene <- anotation[anotation$gene_name == i, ]
locus_gene <- paste(gene$locus, collapse = ",")
locus_strand <- paste(gene$locus_strand, collapse = ",")
locus_distance <- paste(gene$distance_to_gene_kb, collapse = ",")
gene_anot <- gene[1, ]
gene_anot$locus <- locus_gene
gene_anot$locus_strand <- locus_strand
gene_anot$distance_to_gene_kb <- locus_distance
gene_anot_complete <- rbind(gene_anot_complete, gene_anot)
}
genes_names <- as.data.frame(gene_anot_complete$gene_name)
colnames(genes_names) <- "gene_name"
genes_annotations <- cbind(genes_names, gene_anot_complete)
genes_annotations <- genes_annotations[, -c(13:15, 17:19, 21:23, 25:27)]
if (isoform_name == "TRUE"){
isoform <- as.data.frame(paste(genes_annotations$gene_name, ".1", sep = ""))
colnames(isoform) <- "isoform_name"
genes_annotations <- cbind(isoform, genes_annotations)
}
write.table(genes_annotations,
paste(output_name, "by_genes.txt", sep = "_"),
col.names = T,
row.names = F,
quote = F,
sep = "\t")
if (length(without_anot) > 0){
print(paste("Warning!: There are",
length(without_anot),
"marks without annotations for the sample",
output_name,
"!"))
write.table(without_anot,
paste(output_name, "without_annotations.txt", sep = "_"),
col.names = F,
row.names = F,
quote = F,
sep = "\t")
}
}
else{
print("Invalid 'list_by' argument!")
print("'list_by' accepts only 'gene' or 'mark'")
}
}
}
for (i in 2:(length(snakemake@input) - 1)){
anotation_search(query = paste(snakemake@input[[i]]),
annotation_file = snakemake@input[[1]],
list_by = "gene",
output_name = paste(unlist(strsplit(
snakemake@input[[i]],
split = "_genes",
fixed = TRUE))[1],
"annotation",
sep = "_"),
query_is_file = "TRUE",
query_is = "transcript",
isoform_name = "TRUE")
print(
paste(
unlist(
strsplit(snakemake@input[[i]],
split = "_genes",
fixed = TRUE))[1],
"annotation",
sep = "_"))
}
#################################
###### Go terms enrichment ######
#################################
# Reads the list of genes from gff3 file
genes_IDs <- read.table(snakemake@input[[length(snakemake@input)]],
sep = "\t")
col_gene_name <- snakemake@params[["col_gene_name"]]
## Extracts the names from the Tags column
genes_IDs <- genes_IDs[genes_IDs[, 3] == "gene", ]
genes_names <- data.frame(
do.call("rbind",
strsplit(as.character(genes_IDs[, 9]), ";", fixed = TRUE)))
genes_names <- data.frame(
do.call("rbind",
strsplit(as.character(genes_names[, col_gene_name]), "=", fixed = TRUE)))
genes_names <- as.character(genes_names$X2)
# Verifies if all genes or a subset of then should be used as universe in enrichment analysis.
if (snakemake@params[["change_universe"]] == TRUE){
universe_subset <- read.table(snakemake@params[["universe_subset"]],
sep = "\t")
universe <- genes_names[genes_names %in% universe_subset$V1]
}else if (snakemake@params[["change_universe"]] == FALSE){
universe <- genes_names
}
# Enrichment parameters
padjustmethod <- snakemake@params[["pAdjustMethod"]]
pvaluecutoff <- snakemake@params[["pvalueCutoff"]]
qvalue <- snakemake@params[["qvalue"]]
sim_cutoff <- snakemake@params[["sim_cutoff"]] # cutoff to removes similarity
# plot parameters
number_of_terms_by_plot <- snakemake@params[["number_of_terms_by_plot"]]
plot_by <- snakemake@params[["plot_by"]]
# Makes a list to use in "compareCluster" function. Each vector in the lista is a sample.
file_genes_all <- data.frame()
for (i in 2:(length(snakemake@input) - 1)){
genes_list <- read.table(snakemake@input[[i]], sep = "\t")
colnames(genes_list) <- strsplit(paste(snakemake@input[[i]]), ".txt")
if (i == 2){
file_genes_all <- genes_list
}else{
file_genes_all <- cbindX(file_genes_all, genes_list)
}
}
compare_list <- as.list(file_genes_all)
# Remove NAs
compare_list <- lapply(compare_list, function(x) x[!is.na(x)])
#
ont <- c("BP", "MF", "CC")
all_rich_ont_terms <- data.frame()
for (o in ont){
# Executes the enrichment analysis and compare the most significant terms for each sample.
comparison <- compareCluster(compare_list,
fun = "enrichGO",
OrgDb = "org.Egrandis.eg.db",
keyType = "GID", # Nome da coluna com os genes no OrgDb
ont = paste(o),
pAdjustMethod = padjustmethod,
universe = universe,
pvalueCutoff = pvaluecutoff,
qvalue = qvalue)
# Removes the redundance of terms
comparison_simp <- simplify(comparison,
cutoff = sim_cutoff,
by = "p.adjust",
select_fun = min)
terms_plot <- dotplot(comparison_simp,
showCategory = number_of_terms_by_plot,
font.size = 14,
by = plot_by)
svg(filename = paste0("images/cluster_profiler",
paste("GO_terms_venn_sub_set",
o,
".svg",
sep = "")),
width = 16,
height = 10,
pointsize = 12)
print(terms_plot)
dev.off()
rich_ont_terms <- as.data.frame(comparison_simp)
rich_ont_terms$Ontology <- rep(o, nrow(rich_ont_terms))
rich_ont_terms <- rich_ont_terms[, c(1:3, 11, 4:10)]
all_rich_ont_terms <- rbind(all_rich_ont_terms, rich_ont_terms)
}
for (i in unique(all_rich_ont_terms[, 1])){
enriched_terms <- all_rich_ont_terms[all_rich_ont_terms[, 1] == i, ]
write.table(enriched_terms,
paste(i, "enriched_GO_terms.txt", sep = "_"),
row.names = F,
col.names = T,
sep = "\t",
quote = F)
print(paste(i, "enriched_GO_terms.txt", sep = "_"))
}