library(ade4) library (vegan) ## load vegan AFTER ade4 library(FD) library(gplots) library(ggplot2) library(ecodist) library(rJava) library(xlsx) ############################################################ ############################################################## ################################################################ Trial <- read.xlsx("Log2FC_Spearman_Copy.xlsx", sheetIndex = 1) #Trial2 <- read.csv ("Genus_OneP_PDOnly.csv") row.names(Trial) <- Trial$Code colnames(Trial) Trial <- Trial[, -1] FinalData=NULL #Table <- Trial #Table.T <- Table[, -1:-5] #total.count <- sum(Table.T) #T.filter <- Table.T[,apply(Table[, -1:-5], 2, sum) >= (total.count * 0.01)] #Group <- Table[, 1:5] #Group <- as.data.frame(Group) #Group[Group==""] <- NA #T <- cbind(Group, T.filter) #i=1 #j=1 for (i in 1:(ncol(Trial)-1)){ k=i+1 for (j in k:ncol(Trial)){ #j=i+1 Value = as.matrix(cor.test(Trial[ ,i], Trial[ ,j], method = "spearman", use = "complete.obs")) p.value <- Value[3] q.value <- p.adjust(p.value, method = "fdr") R.value <- Value[4] Values <- cbind(p.value ,q.value, R.value) Values <- as.data.frame(Values) Values[,4] <- colnames(Trial[i]) Values[,5] <- colnames(Trial[j]) colnames(Values)[4] = "Comparison_1" colnames(Values)[5] = "Comparison_2" FinalData = rbind(FinalData, Values) } } write.xlsx(FinalData, "SpearmanCorrelations_Log2FC_Spearman_Output_fdr.xlsx", row.names = F)