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chemrich_chemical_classes.R
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chemrich_chemical_classes.R
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# This script runs the ChemRICH analysis for a input with a chemical classes as set definitions.
# Author Dinesh Kumar Barupal (dinesh.kumar@mssm.edu) August 2020.
load.ChemRICH.Packages <- function() {
if (!require("devtools"))
install.packages('devtools', repos="http://cran.rstudio.com/")
if (!require("RCurl"))
install.packages('RCurl', repos="http://cran.rstudio.com/")
if (!require("pacman"))
install.packages('pacman', repos="http://cran.rstudio.com/")
library(devtools)
library(RCurl)
library(pacman)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
pacman::p_load(GGally)
pacman::p_load(DT)
pacman::p_load(RCurl)
pacman::p_load(RJSONIO)
pacman::p_load(ape)
pacman::p_load(devEMF)
pacman::p_load(dynamicTreeCut)
pacman::p_load(extrafont)
pacman::p_load(ggplot2)
pacman::p_load(ggpubr)
pacman::p_load(ggrepel)
pacman::p_load(grid)
pacman::p_load(htmlwidgets)
pacman::p_load(igraph)
pacman::p_load(magrittr)
pacman::p_load(network)
pacman::p_load(officer)
pacman::p_load(openxlsx)
pacman::p_load(phytools)
pacman::p_load(plotly)
pacman::p_load(plotrix)
pacman::p_load(rcdk)
pacman::p_load(readxl)
pacman::p_load(rvg)
pacman::p_load(sna)
pacman::p_load(visNetwork)
}
run_chemrich_chemical_classes <- function(inputfile = "nameofthefile") {
ndf <- data.frame(readxl::read_xlsx(path = inputfile, sheet = 1), stringsAsFactors = F)
ndf$pvalue <- as.numeric(ndf$pvalue)
ndf$effect_size <- as.numeric(ndf$effect_size)
ndf$edirection <- "up"
ndf$efs <- 1
if(length(which(ndf$effect_size < 0)) >0) { # if regression models
ndf$edirection[which(ndf$effect_size < 0)] <- "down"
ndf$edirection[which(ndf$pvalue > 0.05)] <- "no change"
ndf$efs[which(ndf$effect_size < 0)] <- 1/abs(ndf$effect_size[which(ndf$effect_size < 0)])
ndf$efs[which(ndf$effect_size > 1)] <- abs(ndf$effect_size[which(ndf$effect_size > 1)])
ndf$efs [which(ndf$pvalue > 0.05)] <- 1
} else { # if student test
ndf$edirection[which(ndf$effect_size < 1)] <- "down"
ndf$edirection[which(ndf$pvalue > 0.05)] <- "no change"
ndf$efs[which(ndf$effect_size < 1)] <- 1/ndf$effect_size[which(ndf$effect_size < 1)]
ndf$efs[which(ndf$effect_size > 1)] <- ndf$effect_size[which(ndf$effect_size > 1)]
ndf$efs [which(ndf$pvalue > 0.05)] <- 1
}
ndf$xlogp <- as.numeric(sapply(ndf$smiles, function(x) { rcdk::get.xlogp(rcdk::parse.smiles(x)[[1]]) }))
clusterids <- names(which(table(ndf$set)>2))
clusterids <- clusterids[which(clusterids!="")]
cluster.pvalues <- sapply(clusterids, function(x) { # pvalues were calculated if the set has at least 2 metabolites with less than 0.10 pvalue.
cl.member <- which(ndf$set==x)
if( length(which(ndf$pvalue[cl.member]<.05)) >0 ){
pval.cl.member <- ndf$pvalue[cl.member]
p.test.results <- ks.test(pval.cl.member,"punif",alternative="greater")
p.test.results$p.value
} else {
1
}
})
cluster.pvalues[which(cluster.pvalues==0)] <- 2.2e-20 ### All the zero are rounded to the double.eps pvalues.\
#clusterdf <- data.frame(name=clusterids[which(cluster.pvalues!=10)],pvalues=cluster.pvalues[which(cluster.pvalues!=10)], stringsAsFactors = F)
clusterdf <- data.frame(name=clusterids,pvalues=cluster.pvalues, stringsAsFactors = F)
clusterdf$keycpdname <- sapply(clusterdf$name, function(x) {
dfx <- ndf[which(ndf$set==x),]
dfx$compound_name[which.min(dfx$pvalue)]
})
altrat <- sapply(clusterdf$name, function (k) {
length(which(ndf$set==k & ndf$pvalue<0.05))/length(which(ndf$set==k))
})
uprat <-sapply(clusterdf$name, function (k) {
length(which(ndf$set==k & ndf$pvalue<0.05 & ndf$edirection == "up"))/length(which(ndf$set==k & ndf$pvalue<0.05))
})
clust_s_vec <- sapply(clusterdf$name, function (k) {
length(which(ndf$set==k))
})
clusterdf$alteredMetabolites <- sapply(clusterdf$name, function (k) {length(which(ndf$set==k & ndf$pvalue<0.05))})
clusterdf$upcount <- sapply(clusterdf$name, function (k) {length(which(ndf$set==k & ndf$pvalue<0.05 & ndf$edirection == "up"))})
clusterdf$downcount <- sapply(clusterdf$name, function (k) {length(which(ndf$set==k & ndf$pvalue<0.05 & ndf$edirection == "down"))})
clusterdf$upratio <- uprat
clusterdf$altratio <- altrat
clusterdf$csize <- clust_s_vec
clusterdf <- clusterdf[which(clusterdf$csize>2),]
clusterdf$adjustedpvalue <- p.adjust(clusterdf$pvalues, method = "fdr")
clusterdf$xlogp <- as.numeric(sapply(clusterdf$name, function(x) { median(ndf$xlogp[which(ndf$set==x)]) })) ##
clusterdf$Compounds <- sapply(clusterdf$name, function(x) {
dfx <- ndf[which(ndf$set==x),]
paste(dfx$compound_name,collapse="<br>")
})
clustdf <- clusterdf[which(clusterdf$pvalues!=1),]
#################################################
########## Impact Visualization Graph ###########
#################################################
clustdf.alt.impact <- clustdf[which(clustdf$pvalues<0.05 & clustdf$csize>1 & clustdf$alteredMetabolites>1) ,]
clustdf.alt.impact <- clustdf.alt.impact[order(clustdf.alt.impact$xlogp),]
clustdf.alt.impact$order <- order(clustdf.alt.impact$xlogp)
clustdf.alt.impact$logPval <- -log(clustdf.alt.impact$pvalues)
p2 <- ggplot(clustdf.alt.impact,aes(x=xlogp,y=-log(pvalues)))
p2 <- p2 + geom_point(aes(size=csize, color=upratio)) +
#labs(subtitle = "Figure Legend : Point size corresponds to the count of metabolites in the group. Point color shows that proportion of the increased metabolites where red means high and blue means low number of upregulated compounds.")+
scale_color_gradient(low = "blue", high = "red", limits=c(0,1))+
scale_size(range = c(5, 30)) +
scale_y_continuous("-log (pvalue)",limits = c(0, max(-log(clustdf.alt.impact$pvalues))+4 )) +
scale_x_continuous(" Lipophilicity (xlogp) ") +
theme_bw() +
#labs(title = "ChemRICH cluster impact plot") +
geom_label_repel(aes(label = name), color = "gray20",family="Arial",data=subset(clustdf.alt.impact, csize>2),force = 5)+
theme(text=element_text(family="Arial Black"))+
theme(
plot.title = element_text(face="bold", size=30,hjust = 0.5),
axis.title.x = element_text(face="bold", size=20),
axis.title.y = element_text(face="bold", size=20, angle=90),
panel.grid.major = element_blank(), # switch off major gridlines
panel.grid.minor = element_blank(), # switch off minor gridlines
legend.position = "none", # manually position the legend (numbers being from 0,0 at bottom left of whole plot to 1,1 at top right)
legend.title = element_blank(), # switch off the legend title
legend.text = element_text(size=12),
legend.key.size = unit(1.5, "lines"),
legend.key = element_blank(), # switch off the rectangle around symbols in the legend
legend.spacing = unit(.05, "cm"),
axis.text.x = element_text(size=10,angle = 0, hjust = 1),
axis.text.y = element_text(size=15,angle = 0, hjust = 1)
)
p2
read_pptx() %>%
add_slide(layout = "Title and Content", master = "Office Theme") %>%
ph_with(dml(ggobj = p2), location = ph_location(type = "body",width=10, height=8,left = 0, top = 0)) %>%
print(target = paste0("chemrich_class_impact_plot.pptx")) %>%
invisible()
ggsave(paste0("chemrich_class_impact_plot.png"), p2,height = 8, width = 12, dpi=300)
cat(paste0("chemrich_class_impact_plot.pptx"," has been created.\n"))
## Export the result table.
clustdf.e <- clusterdf[order(clusterdf$pvalues),]
clustdf.e$pvalues <- signif(clustdf.e$pvalues, digits = 2)
clustdf.e$adjustedpvalue <- signif(clustdf.e$adjustedpvalue, digits = 2)
clustdf.e$upratio <- signif(clustdf.e$upratio, digits = 1)
clustdf.e$altratio <- signif(clustdf.e$altratio, digits = 1)
clustdf.e <- clustdf.e[,c("name","csize","pvalues","adjustedpvalue","keycpdname","alteredMetabolites","upcount","downcount","upratio","altratio")]
names(clustdf.e) <- c("Cluster name","Cluster size","p-values","FDR","Key compound","Altered metabolites","Increased","Decreased","Increased ratio","Altered Ratio")
#df1$TreeLabels <- treeLabels
ndf$pvalue <- signif(ndf$pvalue, digits = 2)
ndf$efs <- signif(ndf$efs, digits = 2)
ndf$FDR <- signif( p.adjust(ndf$pvalue), digits = 2)
l <- list("ChemRICH_Results" = clustdf.e, "Compound_ChemRICH" = ndf )
openxlsx::write.xlsx(l, file = paste0("chemRICH_class_results.xlsx"), asTable = TRUE)
cat(paste0("chemRICH_class_results.xlsx", " has been saved.\n"))
##################################
#### Interactive Cluster Plot ####
##################################
p2 <- ggplot(clustdf.alt.impact,aes(label=name,label2=pvalues, label3=csize,label4=Compounds))
p2 <- p2 + geom_point(aes(x=xlogp,y=-log(pvalues),size=csize, color=upratio)) +
#labs(caption = "Figure Legend : Point size corresponds to the count of metabolites in the group. Point color shows that proportion of the increased metabolites where red means high and blue means low number of upregulated compounds.")+
scale_color_gradient(low = "blue", high = "red", limits=c(0,1))+
scale_size(range = c(5, 30)) +
scale_y_continuous("-log (pvalue)",limits = c(0, max(-log(clustdf.alt.impact$pvalues))+5 )) +
scale_x_continuous(" Lipophilicity (xlogp) ") +
theme_bw() +
#labs(title = "ChemRICH cluster impact plot") +
geom_text(aes(x=xlogp,y=-log(pvalues),label = name), color = "gray20",data=subset(clustdf.alt.impact, csize>2))+
theme(
plot.title = element_text(face="bold", size=30,hjust = 0.5),
axis.title.x = element_text(face="bold", size=20),
axis.title.y = element_text(face="bold", size=20, angle=90),
panel.grid.major = element_blank(), # switch off major gridlines
panel.grid.minor = element_blank(), # switch off minor gridlines
legend.position = "none", # manually position the legend (numbers being from 0,0 at bottom left of whole plot to 1,1 at top right)
legend.title = element_blank(), # switch off the legend title
legend.text = element_text(size=12),
legend.key.size = unit(1.5, "lines"),
legend.key = element_blank(), # switch off the rectangle around symbols in the legend
legend.spacing = unit(.05, "cm"),
axis.text.x = element_text(size=15,angle = 0, hjust = 1),
axis.text.y = element_text(size=15,angle = 0, hjust = 1)
)
gg <- ggplotly(p2,tooltip = c("label","label2","label4"), width = 1600, height = 1000)
gg
saveWidget(gg,file = paste0("chemrich_class_interactive.html"), selfcontained = T)
cat(paste0("chemrich_class_interactive.html", " has been saved.\n"))
}