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script_correlation_covid19.R
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script_correlation_covid19.R
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# Correlations for searching drugs again SARS-CoV-2
# AJPerez, March 2020
# Updated, July 2020
library(ggplot2)
library(reshape2)
library(topGO)
library(scales)
library(RColorBrewer)
library(clusterProfiler)
library(ReactomePA)
library(org.Hs.eg.db)
library(org.Mm.eg.db)
library(biomaRt)
library(rWikiPathways)
library(drugbankR)
library(tidyverse)
library(clipr)
setwd("./")
# Parameters
TARGET_GENE <- 0 # 0 for all
CORR_SIGN <- 2 # 1=+, 2=-, 0=all
UPDOWN_SIGN <- 3 # 1=up, 2=down, 0=both, 3=off
FC <- 1 # Fold change threshold
EXPRESSIONS <- "expressions/seeds/" # tr_itc
OUT_FOLDER <- "results_def/"
print(list.files(path = EXPRESSIONS)) # expression matrices
# Databases' constants
dv <- "drugbank_5.1.5.db"
dids <- queryDB(type = "getIDs", db_path = dv)
ensembl <- useMart("ensembl", dataset = "hsapiens_gene_ensembl")#, host = "uswest.ensembl.org")
# Genes and correlations
NEXP <- 0.9 # %ncols(expr_table)
GUPO_array <- list.files(path = EXPRESSIONS)
#GUPO_array <- GUPO_array[31:length(GUPO_array)]
#GUPO_array <- GUPO_array[c(19)] # <------------- REMAININGs ----------------
if (TARGET_GENE != 0) {
GUPO_array <- GUPO_array[TARGET_GENE]
}
if (CORR_SIGN == 0) {
CORR_array <- c(1, -1)
} else if (CORR_SIGN == 1) {
CORR_array <- 1
} else {
CORR_array <- -1
}
if (UPDOWN_SIGN == 0) {
UPDOWN_array <- c("up", "down")
} else if (UPDOWN_SIGN == 1) {
UPDOWN_array <- "up"
} else if (UPDOWN_SIGN == 2) {
UPDOWN_array <- "down"
} else {
UPDOWN_array <- "off"
}
# Iterate genes
for (GUPO in GUPO_array) {
for (CORR in CORR_array) {
for (UPDOWN in UPDOWN_array) {
GENE <- strsplit(GUPO, '\\(')[[1]][[1]]
print(paste(GENE, CORR, UPDOWN, FC))
# Organism variables and folder
ORG <- strsplit(GUPO, '_')[[1]][[3]]
ORG1 <- "human"
ORG2 <- "hsa"
ORG3 <- "hsapiens_gene_ensembl"
if (ORG == "mus musculus") {
ORG1 <- "mouse"
ORG2 <- "mmu"
ORG3 <- "mmusculus_gene_ensembl"
}
############
# Files
FILE <- paste0(EXPRESSIONS, GUPO, "/resultados.tsv")
header <- read.csv(FILE, sep = "\t", nrows = 2, header = FALSE)
header <- rbind(header, t(c("", "", paste0("exp", seq(1, ncol(header)-2)))))
expr_table <- read.csv(FILE, sep = "\t", skip = 2, header = FALSE)
names(expr_table) <- c("Ensembl", "Genename", paste0("exp", seq(1, ncol(expr_table)-2)))
expr_table <- expr_table %>% filter(Ensembl != "") # empty gene (controls from array)
# Add gene type and experiment ID
type_table <- read.csv(paste0("../galiciame/biomart_", ORG1, "_type.tsv"), sep = "\t")
expr_table <- merge(expr_table, type_table, by.x = "Ensembl", by.y = "Gene.stable.ID")
# Remove rows with one or more NA
#expr_table <- na.omit(expr_table)
#############
# Reference gene
ref_gene_index <- which(expr_table$Genename == GENE)
expr_table <- expr_table[,order(as.numeric(expr_table[ref_gene_index,]), decreasing = T)]
# Filter by activators/inhibitors (half of correlation)
if (UPDOWN == "up") {
temp <- expr_table[ref_gene_index, 4:ncol(expr_table)]
temp <- temp[1, ] < FC
expr_table <- expr_table %>% select(- names(temp[,temp]))
} else if (UPDOWN == "down") {
temp <- expr_table[ref_gene_index, 4:ncol(expr_table)]
temp <- temp[1, ] > -FC
expr_table <- expr_table %>% select(- names(temp[,temp]))
} else {
temp <- expr_table[ref_gene_index, 4:ncol(expr_table)]
temp <- temp[1, ] > -FC & temp[1, ] < FC
expr_table <- expr_table %>% select(- names(temp[,temp]))
}
# Follow with reference gene
ref_gene <- expr_table %>% filter(Genename == GENE) # GENE="SMN2" | "SMN1" if smn1+smn2
ref_expr <- as.numeric(ref_gene[,4:ncol(ref_gene)]) # -2 if smn1+smn2 and 4 -> 6
# Correlation and number of NA
fcor <- function(x) {
c <- cor(as.numeric(x), ref_expr, use = "pairwise.complete.obs")
return(c)
}
#expr_table2 <- expr_table
expr_table$Correlation <- apply(expr_table %>% select(4:ncol(expr_table)), 1, fcor) # -2 if smn1+smn2 and 4 -> 6 (add smn2 to figure)
expr_table$nexp_count <- apply(expr_table %>% select(5:ncol(expr_table)-1), 1, function(x) sum(!is.na(x))) # -2 if smn1+smn2 and 4 -> 6 (add smn2 to figure)
# Correlation distribution
corrs <- as.numeric(expr_table %>% filter(nexp_count >= NEXP*length(ref_expr)) %>% pull(Correlation))
mean_corr <- mean(corrs)
if (CORR > 0) {
CORR <- as.numeric(formatC(as.vector(quantile(corrs, 0.75) + IQR(corrs)), digits = 3, format = "f"))
CORR <- 0.7
} else {
CORR <- as.numeric(formatC(as.vector(quantile(corrs, 0.25) - IQR(corrs)), digits = 3, format = "f"))
CORR <- -0.7
}
# Filer by correlation and nexp
filtered_expr_table <- expr_table %>% filter((CORR < 0 & Correlation <= CORR &
nexp_count >= NEXP*length(ref_expr)) |
(CORR > 0 & Correlation >= CORR &
nexp_count >= NEXP*length(ref_expr)) |
Genename == GENE)
filtered_expr_table <- expr_table %>% filter(Ensembl %in% readLines(paste0("gualberto/v3/output_genes/",
GENE, "_selgenes_inverse_scores.csv_0.01")) | Genename == GENE) # Gualberto (direct/inverse)
#filtered_expr_table <- expr_table %>% filter(Ensembl %in% readLines(paste0("../galiciame/gualberto/smns/",
# GENE, "_selgenes_direct.csv")) | Genename == GENE) # Gualberto (direct/inverse)
filtered_expr_table_corr <- filtered_expr_table
filtered_expr_table %>% select(- c(Correlation, nexp_count))
# Figure
fig_expr_table <- melt(filtered_expr_table, id = c("Ensembl","Genename", "Gene.type"))
fig_expr_table <- merge(fig_expr_table, t(header[c(1,3),3:ncol(header)]), by.x = "variable", by.y = "3")
names(fig_expr_table)[6] <- "Atlas"
fig_ref <- fig_expr_table %>% filter(Genename == GENE)
p <- ggplot() +
geom_hline(yintercept = 0, color = "blue") +
geom_line(data = fig_expr_table, aes(variable, value, group = Genename, color = Gene.type), alpha = .8) +
xlab("Experiments") +
ylab("Log2-fold change") +
theme(axis.text=element_text(size=12), axis.title=element_text(size=12), legend.text = element_text(size=10.5),
axis.text.x = element_text(angle = 60, hjust = 1), legend.position="none", #"top"
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(-10, 10, 2)) +
geom_tile(data=fig_expr_table, aes(x = variable, y = -10, fill = Atlas)) +
geom_line(data=fig_ref, aes(variable, value, group = Genename), size = 1.2) +
guides(colour = guide_legend(nrow = 3))
print(p)
# Folder
FOLDER <- paste0(OUT_FOLDER, GENE, "_", CORR, "_", NEXP, "_", UPDOWN , "_", FC, "_", ORG1)
if (!dir.exists(FOLDER)){
dir.create(FOLDER)
}
# Save figure
pdf(paste0(FOLDER, "/../", GENE, "_correlation.pdf"), width=16, height=8, paper='special')
print(p)
dev.off()
# Correlation distribution
h1 <- ggplot(expr_table %>% filter(nexp_count >= NEXP*length(ref_expr)), aes(Correlation)) +
#geom_histogram(binwidth = 20, color="grey20", aes(fill=ind), position = "dodge") +
xlab("Correlation value") +
ylab("Number of genes") +
#scale_x_continuous(limits =c(-1, 1)) +
geom_histogram(aes(y = ..count..), binwidth=0.01, alpha = .5) +
#geom_density() +
geom_vline(xintercept = mean_corr, color = "blue", linetype = "dashed", size = 1.25) +
scale_x_continuous(breaks = seq(-1, 1, by = 0.1)) +
theme_grey() +
theme(legend.title = element_blank(), legend.position=c(0.82, 0.87), text = element_text(size=16))
print(h1)
pdf(paste0(FOLDER, "/corr_distribution.pdf"), width=16, height=8, paper='special')
print(h1)
dev.off()
# Save file and count results
filtered_expr_table_wo_SMN <- filtered_expr_table %>% filter(Genename != GENE)
if (nrow(filtered_expr_table_wo_SMN) == 0) { next }
write.table(filtered_expr_table_corr %>% filter(Gene.type == "protein_coding") %>% select(Ensembl, Genename, Correlation),
file = paste0(FOLDER, "/genes_correlations.tsv"), quote = F, col.names = F, row.names = F, sep = "\t")
write.table(filtered_expr_table_wo_SMN %>% filter(Gene.type == "protein_coding") %>% select(Ensembl),
file = paste0(FOLDER, "/genes.ensembl"), quote = F, col.names = F, row.names = F)
write.table(filtered_expr_table_wo_SMN %>% filter(Gene.type == "protein_coding") %>% select(Genename),
file = paste0(FOLDER, "/genes.gn"), quote = F, col.names = F, row.names = F)
write.table(filtered_expr_table_wo_SMN %>% group_by(Gene.type) %>% summarise(count=n()),
file = paste0(FOLDER, "/types.tsv"), quote = F, col.names = F, row.names = F, sep = "\t")
genes <- as.vector(unlist(filtered_expr_table_wo_SMN %>% filter(Gene.type == "protein_coding") %>% select(Ensembl)))
##############
# Enrichment #
##############
next
# Files
file_bg <- paste0("../galiciame/biomart_", ORG1, "_go2_protein.tsv")
Nodes <- 50 # number of processes to show
Ontology <- "GOES" #GO.P.ID : PFC (BP MF CC)
#Create temp file
data <- read.csv(file_bg, sep = "\t", header = TRUE, row.names = NULL)[,(c('ID', Ontology))]
file_temp <- paste0(file_bg,"2")
write.table(data, file = file_temp, sep = "\t", quote = FALSE, col.names = FALSE, row.names = FALSE)
# Get background annotation
GOesByID <- readMappings(file = file_temp)
bg_genes <- names(GOesByID)
compared_genes <- factor(as.integer(bg_genes %in% genes))
names(compared_genes) <- bg_genes
# Iterate through the two ontologies
for (ONT in c("BP", "CC")) {
next
# Create topGO object
GOdata <- new("topGOdata", ontology = ONT, allGenes = compared_genes,
annot = annFUN.gene2GO, gene2GO = GOesByID)
asd <- unlist(Term(GOTERM))
# Run Fisher test
resultFisher <- runTest(GOdata, algorithm = "classic", statistic = "fisher")
# Create and print table with enrichment result
allRes <- GenTable(GOdata, classicFisher = resultFisher, topNodes = Nodes)
# Different palettes
palette <- c("#F52A2A", "#D561EA", "#61B0EA", "green", "#E89B57", "#E4EA61", "white") # alternative palette
myPalette <- colorRampPalette(rev(brewer.pal(9, "YlOrRd")))
# Figure
########
layout(t(1:2), widths=c(8,3))
par(mar=c(4, .5, .7, .7), oma=c(3, 15, 3, 4), las=1)
pvalue <- as.numeric(gsub("<", "", allRes$classicFisher)) # remove '<' symbols
allRes$classicFisher <- pvalue
max_value <- as.integer(max(-log(pvalue)))+1
pv_range <- exp(-seq(max_value, 0, -1))
allRes <- mutate(allRes, plot_id = paste(GO.ID, Term, sep = " - "))
mylabels <- paste (allRes$GO.ID, "-", asd[allRes$GO.ID])
mybreaks <- 10^-(0:30)
p <- ggplot(data=allRes, aes(x=reorder(plot_id, Significant), y=Significant)) +
geom_bar(stat="identity", color="black", aes(fill=as.numeric(log(classicFisher))), size = 0.3)+
geom_text(aes(label=mylabels), position=position_fill(vjust=0), hjust=0, fontface="bold", size = 5)+
coord_flip() +
theme(panel.background = element_blank(), panel.grid.major.x = element_line(colour = "darkgrey", size=0.75),
panel.grid.minor.x = element_line(colour = "grey",size=0.75), axis.title.y=element_blank(),
axis.text.y=element_blank(), axis.ticks.y=element_blank(),
axis.ticks.x =element_blank(), axis.line.y=element_blank(), axis.text=element_text(size=12)) +
ylab("Number of genes") +
guides(fill = guide_colourbar(barheight = 25, reverse=T)) +
scale_fill_gradientn(name = "p-value", colours = myPalette(4), breaks = log(mybreaks),
guide = guide_colourbar(reverse = TRUE), labels=mybreaks) +
scale_y_continuous(breaks = seq(0, max(allRes$Significant), by = 20))
print(p)
# Save results
if (exists("allRes") & nrow(allRes) > 4) {
# Save table and figure
write.table(allRes, file = paste0(FOLDER, "/enrichment_", ONT, ".tsv"), quote = F, row.names = F, sep = "\t")
pdf(paste0(FOLDER, "/enrichment_", ONT, ".pdf"), width=16, height=8, paper='special')
print(p)
dev.off()
# Save list of genes by enriched GO
#GOnames <- as.vector(allRes$GO.ID)
#allGenes <- genesInTerm(GOdata, GOnames)
#significantGenes <- list()
#for(x in 1:Nodes){
# significantGenes[[x]] <- allGenes[[x]][allGenes[[x]] %in% as.vector(genes)]
#}
#names(significantGenes) <- allRes$Term
#new_folder <- paste0(FOLDER, "/", ONT, "/")
#dir.create(new_folder)
#for(x in 1:Nodes){
# write.table(significantGenes[x], quote = FALSE, sep = "\t", col.names = F, row.names = F,
# file = paste(new_folder, x, "-", str_replace_all(names(significantGenes)[x], "/", "_"), ".tsv", sep=""))
#}
}
}
###################
# ClusterProfiler #
###################
entrez <- getBM(attributes="entrezgene_id", filters = 'ensembl_gene_id', values = genes, mart = ensembl)
entrez <- as.vector(unlist(entrez['entrezgene_id']))
############
# Reactome #
############
xr = ""
xr <- enrichPathway(gene = entrez, pvalueCutoff = 0.05, readable = T, organism = ORG1, pAdjustMethod = "fdr")
if (exists("xr") & nrow(xr) > 1) {
r <- dotplot(xr, showCategory = 15, font.size = 14)
#scale_fill_gradientn(name = "p_adjust", colours = myPalette(4), guide = guide_colourbar(reverse = TRUE))
#print(r)
#pdf(paste0(FOLDER, "/../", GENE, ".pdf"), width=16, height=8, paper='special')
png(paste0(FOLDER, "/../", GENE, ".png"), width = 800, height = 600)
print(r)
dev.off()
#write.table(data.frame(xr$Description), quote = FALSE, col.names = F, row.names = F, file = paste0(FOLDER, "/../", GENE, ".txt"))
write.table(entrez, quote = FALSE, col.names = F, row.names = F, file = paste0(FOLDER, "/../", GENE, ".txt"))
# Write data
#new_folder <- paste0(FOLDER, "/REACT/")
#dir.create(new_folder)
#for (i in 1:nrow(xr)) {
# xrgeneid <- unlist(strsplit(xr$geneID[i], split="/"))
# xrensembl <- getBM(attributes="ensembl_gene_id", filters = 'entrezgene_id', values = xrgeneid, mart = ensembl)
# xrensembl <- as.vector(unlist(xrensembl['ensembl_gene_id']))
# write.table(xrensembl, quote = FALSE, sep = "\t", col.names = F, row.names = F,
# file = paste(new_folder, i, "-", str_replace_all(xr$Description[i], "/", "_"), ".tsv", sep=""))
#}
write.table(as.data.frame(xr), file = paste0(FOLDER, "/enrichment_REACTOME.tsv"), quote = F, row.names = F, sep = "\t")
}
#next
################
# WikiPathways #
################
xw = ""
ORG_WIKI <- ORG
substr(ORG_WIKI, 1, 1) <- toupper(substr(ORG_WIKI, 1, 1))
wp.gmt <- rWikiPathways::downloadPathwayArchive(organism = ORG_WIKI, format = "gmt")
#listOrganisms()
wp2gene <- clusterProfiler::read.gmt(wp.gmt)
wp2gene <- wp2gene %>% tidyr::separate(ont, c("name","version","wpid","org"), "%")
wpid2gene <- wp2gene %>% dplyr::select(wpid,gene) #TERM2GENE
wpid2name <- wp2gene %>% dplyr::select(wpid,name) #TERM2NAME
xw <- clusterProfiler::enricher(entrez, pAdjustMethod = "fdr", pvalueCutoff = 0.1, TERM2GENE = wpid2gene, TERM2NAME = wpid2name)
if (exists("xw") & nrow(xw) > 4) {
#as.data.frame(xw)
b <- barplot(xw, showCategory=8, colorEdge = TRUE) +
ylab("Number of genes")
# scale_fill_gradientn(name = "p_adjust", colours = myPalette(4), guide = guide_colourbar(reverse = TRUE))
print(b)
pdf(paste0(FOLDER, "/wiki_plot.pdf"), width=16, height=8, paper='special')
print(b)
dev.off()
# Write data
#new_folder <- paste0(FOLDER, "/WIKI/")
#dir.create(new_folder)
#for (i in 1:nrow(xw)) {
# xwgeneid <- unlist(strsplit(xw$geneID[i], split="/"))
# xwensembl <- getBM(attributes="ensembl_gene_id", filters = 'entrezgene_id', values = xwgeneid, mart = ensembl)
# xwensembl <- as.vector(unlist(xwensembl['ensembl_gene_id']))
# write.table(xwensembl, quote = FALSE, sep = "\t", col.names = F, row.names = F,
# file = paste(new_folder, i, "-", str_replace_all(xw$Description[i], "/", "_"), ".tsv", sep=""))
#}
write.table(as.data.frame(xw), file = paste0(FOLDER, "/enrichment_WIKI.tsv"), quote = F, row.names = F, sep = "\t")
}
}
}
}