/
correlation_modules.R
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correlation_modules.R
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# This script predicts the correlation modules for a metabolomics data-set.
# 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)
}
chemrich_predict_correlation_modules <- function(inputfile = "nameofthefile") {
ndf <- data.frame(readxl::read_xlsx(path = inputfile, sheet = 1,col_names = T), stringsAsFactors = F)
data_matrix.sb <- ndf[,-1]
data_matrix.sb <- do.call(rbind,lapply(1:nrow(data_matrix.sb),function(x) {as.numeric(data_matrix.sb[x,])}))
cormat <- cor(t(data_matrix.sb), method = "spearman")
s <- cormat
diag(s) <- 0
s[is.na(s)] <- 0
hc <- hclust(as.dist(1-s), method="ward.D2") # ward method provide better clusters.
clust1 <- cutreeDynamic(hc,distM = as.matrix(1-s),deepSplit =4, minClusterSize = 3)
clusternames <- paste0("clust_",clust1)
xdf <- data.frame(compound_name = ndf$compound_id, order = sapply(ndf$compound_id, function(x) {which(ndf$compound_id[hc$order]==x)}), pvalue = 0,effect_size=0,set=clusternames, stringsAsFactors = F)
l <- list("raw" = ndf, "chemrich_input" = xdf )
openxlsx::write.xlsx(l, file = paste0("correlation_modules_prediction.xlsx"), asTable = TRUE)
cat(paste0("correlation_modules_prediction.xlsx", " has been saved.\n Voilla!!"))
}