diff --git a/CALCULATE SPOTS DIFF.R b/CALCULATE SPOTS DIFF.R
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+impose.mono <- function(x, dir){
+  # Function to impose monotonicity on elements of the vector, x
+  # dir="incr" gives increasing monotonicity
+  # dir="decr" gives decreasing monotonicity
+  
+  if(dir=="decr"){ x <- rev(x) }
+  for(i in 2:length(x)){
+    idx <- is.na(x[(i-1):i])
+    if (sum(idx)==1){
+      x[i] <- x[(i-1):i][!idx]
+    } else {
+      x[i] <- max(x[i-1],x[i])
+    }
+  }
+  if(dir=="decr"){ x <- rev(x) }
+  x
+}
+
+###
+
+perm <- function(dat, nExp, nCtl){
+  N <- nExp+nCtl 	  # total number of exp and neg ctl wells
+  k <- NROW(dat)      # number of peptide pools
+  B <- choose(N,nExp) # number of perms needed for complete enumeration
+  
+  if(B < 20) stop("Too few replicates to use this method (B < 20).")
+  
+  mu.e <- rowMeans(dat[,1:nExp], na.rm=TRUE)        # vector of peptide pool means
+  mu.c <- rowMeans(dat[,nExp+(1:nCtl)], na.rm=TRUE) # vector of neg ctl means
+  
+  # test statistic for observed data
+  t <- mu.e-mu.c                  
+  t.sort <- sort(t)
+  index <- order(t)
+  
+  # samp matrix contains all possible permutations of columns of dat matrix:
+  samp <- expand.grid(rep(list(0:1),N))
+  samp <- samp[apply(samp,1,sum)==nExp,]
+  samp <- as.matrix(samp)
+  
+  tPerm <- matrix(0,nrow=k,ncol=B)
+  
+  # calculate test statistics for each perm sample in samp:
+  for(i in 1:B){
+    perm.dat <- dat[,order(samp[i,])]
+    mu.exp <- rowMeans(perm.dat[,(1:nExp)+nCtl], na.rm=TRUE)
+    mu.ctl <- rowMeans(perm.dat[,1:nCtl], na.rm=TRUE)
+    tPerm[,i] <- mu.exp-mu.ctl
+  }
+  
+  # order rows of tPerm to correspond with sorted test statistics in t.sort:
+  if(k==1){ tPerm.mono <- tPerm.sort <- tPerm }
+  if(k>1){
+    tPerm.sort <- tPerm[index,]    
+    tPerm.mono <- apply(tPerm.sort,2,impose.mono,dir="incr")
+  }
+  
+  # calculate adjusted p-values:
+  tpvalue <- apply(tPerm.mono>=t.sort, 1, mean, na.rm=TRUE)
+  
+  # enforce monotonicity on adjusted p-values in tpvalue:
+  if(k>1){ tpvalue <- impose.mono(tpvalue, dir="decr") }
+  
+  list(tstat=t, tadjp=tpvalue[order(index)])
+}
+
+###
+elsdfreq <- function(data, nExp, nCtl, nameCtl, alpha=0.05){
+  data <- as.data.frame(data)
+  data <- data[order(data[,1],data[,2]),]
+  ncdat <- data[data[,3]==nameCtl,]
+  dat <- data.frame(matrix(NA,nrow=NROW(data[data[,3]!=nameCtl,]),ncol=3+nExp+nCtl))
+  dat[,1:(3+nExp)] <- data[data[,3]!=nameCtl,]
+  names(dat) <- c(names(data)[1:(3+nExp)],paste("c",1:nCtl,sep=""))
+  for(id in unique(data[,1])){
+    for(day in unique(data[,2])){
+      dat[dat[,1]==id & dat[,2]==day,3+nExp+(1:nCtl)] <- ncdat[ncdat[,1]==id & ncdat[,2]==day,-(1:3)]
+    }}
+  
+  ### dat = a data.frame with ptid, day, antigen in cols 1 to 3,
+  ### peptide replicates in col4:(3+nExp), control replicates in
+  ### col(4+nExp):col(3+nExp+nCtl)
+  ### Same control replicates are repeated for each peptide per unique ptid
+  
+  ### nExp = no. of experimental wells per peptide
+  ### nCtl = no. of negative control wells
+  ### Use alpha=0.05 as default
+  
+  ind <- unique(dat[,1:2])
+  adjp <- teststat <- pos.call <- NULL
+  
+  # perform permutation test on each unique ptid*day combination with
+  # multiplicity adjustment for the number of peptide pools
+  
+  for(i in 1:NROW(ind)){
+    temp.dat <- as.matrix(dat[dat[,1]==ind[i,1] & dat[,2]==ind[i,2],-(1:3)])
+    temp.id <- ind[i,1]
+    temp.day <- ind[i,2]
+    temp.pep <- dat[dat[,1]==ind[i,1] & dat[,2]==ind[i,2],3]
+    if (length(temp.pep)==1) temp.dat <- t(temp.dat)
+    temp.adjp <- temp.teststat <- rep(NA, NROW(temp.dat))
+    
+    nas <- is.na(temp.dat)
+    nCtlc <- nCtl - sum(nas[1,nExp+(1:nCtl)]) # observed number of neg ctl reps
+    nExpC <- nExp - apply(nas[,1:nExp],1,sum) # observed numbers of exp reps
+    idx <- (nExpC>=3 & nCtlc>=3) | (nExpC>=2 & nCtlc>=4)
+    if (sum(idx)>0){
+      temp.dat <- as.matrix(temp.dat[idx,])
+      if (sum(idx)==1 & length(idx)>1){
+        temp.dat <- t(temp.dat)
+        out<-perm(dat=rbind(temp.dat,temp.dat), nExp=nExp, nCtl=nCtl)
+        out<-lapply(out, function(x) x[1])  
+      }else{
+        out <- perm(dat=temp.dat, nExp=nExp, nCtl=nCtl)
+      }
+      
+      temp.teststat[idx] <- out$tstat
+      temp.adjp[idx] <- out$tadjp
+    }
+    teststat <- c(teststat, temp.teststat)
+    adjp <- c(adjp, temp.adjp)
+    
+    if (nCtlc<nCtl) warning(paste("ptid", temp.id, "has", nCtl-nCtlc, "missing value(s) for negative controls", "on day", temp.day))
+    if (min(nExpC)<nExp){
+      for (j in 1:sum(nExpC<nExp)){
+        warning(paste("ptid", temp.id, "has", nExp-nExpC[nExpC<nExp][j], "missing value(s) for", temp.pep[nExpC<nExp][j], "on day", temp.day))
+      }
+    }
+  }
+  pos.call <- ifelse(adjp <= alpha,1,0)    
+  
+  output <- data.frame(cbind(dat,round(teststat,5),round(adjp,5),pos.call))
+  colnames(output)[(ncol(dat)+1):(ncol(dat)+3)] <- c("t-stat","adjp","pos")
+  output
+  
+  
+}
+
+
+#### Read (excel or csv)  and create "data"
+# library(readxl)
+# data<-read_xlsx("C:/Users/y2955361j/Downloads/example.xlsx")
+# 
+# ## elsdfreq <- function(data, nExp, nCtl, nameCtl, alpha=0.05 
+# ### nExp = no. of experimental wells per peptide (usually 3)
+# ### nCtl = no. of negative control wells (usually 6)
+# ### Use alpha=0.05 as default
+# 
+# results<-elsdfreq (data, 3, 6, 'control', alpha=0.05)
+# View(results)
+