################################################################################# Package Dependencies
 
 ################################################################################ %!in% operation
 '%!in%' <- function(x,y)!('%in%'(x,y))
 
 
 ################################################################################# df2mat

 df2mat <- function(df){
     # convert df into matrix

     if(is.data.frame(df)){

         colnames(df) <- NULL
         rownames(df) <- NULL
         df <- as.matrix(df)}
     return(df)

 }
 
 
 ################################################################################# checkSym

 checkSym <- function(mat, stp){
     # checks if mat is symmetric
     # checks if values in mat are between (0, 1)

     sym_sym <- paste0(" output of ", stp)
     caption_sym <- paste0(sym_sym, " is not symmetric")

     if(!isSymmetric(mat))
         stop(caption)

     c01 <-  paste0(" output of ", stp)
     caption_01 <- paste0(c01, " are not in (0,1)")

     if(length(table(between(mat, 0, 1)["FALSE"])) != 0)
         stop(caption_01)

     rm(caption_sym, caption_01)

 
 }
 
 
 ################################################################################# checkNumeric

 checkNumeric <- function(x, stp){
     # checks if all the values in x are numeric

     caption <- paste0(" at least one non-numeric value at ", stp)
     if(!is.numeric(x))
         stop(caption)

 
 }
 
 
 ################################################################################# sigmoid

 sigmoid <- function(x, p=1, q=0){
     # sigmoid function

     res <- 1/(1 + exp(-p *(x - q) ))
     return(res)

 }
 
 
 ################################################################################# calibration

 calibration <- function(v){

     # perform calibration on vector v

     average <- mean(v)
     variance <- var(v)
     res <- sigmoid(v, p = (1/variance), q = average)
     return(res)

 }
 
 
 ################################################################################# norm_vec

 normalization <- function(x){
     # divide the vector x by its norm2

     res <- x/sqrt(sum(x^2))
     return(res)

 }
 
 
 ################################################################################# GaussianKernel

 GaussianKernel <- function(x, sigma){
     # calculates Gaussian kernel for matrix x

     res <- exp(-1 * as.matrix(dist(x)^2)/sigma)
     return(res)

 }
 
 
 ################################################################################# DOM: deepOverlapMeasure

 DOM <- function(mat){

     # mat: matrix, squared, symmetric, values are in (0,1)
     #
     # value: Deep Overlap Measure (DOM) on mat

     diag(mat) <- 0

     degreeRow <- replicate(dim(mat)[1], rowSums(mat))
     degreeCol <- t(replicate(dim(mat)[1], colSums(mat)))
     degreeMin <- pmin(degreeRow, degreeCol)
     rm(degreeRow, degreeCol)

     degreeRow <- replicate(dim(mat)[1], rowSums(mat)^2)
     degreeCol <- t(replicate(dim(mat)[1], colSums(mat)^2))
     degreeMin2 <- pmin(degreeRow, degreeCol)

     numerator <- mat + (mat %^% 2) + (mat %^% 3)
     denominator <- degreeMin2 + degreeMin + (1 - mat)

     res <- numerator/denominator
     diag(res) <- 1

     rm(degreeCol, degreeRow, degreeMin, degreeMin2)
     return(as.matrix(res))

 
 }
 
 ################################################################################# TOM: Topological Overlap Measure

 TOM <- function(mat){
     # mat: matrix, squared, symmetric, values are in (0,1)
     # Niloofar Aghaieabiane
     # October 2021
     # value: Topology Overlap Matrix (TOM) on mat

     diag(mat) <- 0

     degreeRow <- replicate(dim(mat)[1], rowSums(mat))
     degreeCol <- t(replicate(dim(mat)[1], colSums(mat)))
     degreeMin <- pmin(degreeRow, degreeCol)

     numerator <- (mat %^% 2) + mat
     denominator <- degreeMin + (1 - mat)

     res <- numerator/denominator
     diag(res) <- 1

     rm(degreeCol, degreeRow, degreeMin)
     return(as.matrix(res))

 }
 
 ################################################################################# adjacencyMatrix

 adjacencyMatrix <- function(expData, calibration = FALSE, norm = TRUE,
                             tom = TRUE, saveAdja = FALSE,
                             adjaNameFile = "adjacency.RData",
                             hm = "adjaHeatMap.png"){
 
 
     if(!is.data.frame(expData) && !is.matrix(expData)){
         stop(" the expressoion input must be eighter a data frame or a matrix")}
 
     if(ncol(expData) >= nrow(expData)){
         warning("number of genes is smaller than the samples,

             are you sure that rows denote genes??", call. = FALSE)}

     checkNumeric(expData, "expression input")

     if(is.data.frame(expData)){
         expData <- df2mat(expData)
         colnames(expData) <- NULL
         rownames(expData) <- NULL }

     expData <- t(expData)

     if(calibration == TRUE){
         message("calibration...")
         expData <- as.data.frame(lapply(as.data.frame(expData), calibration))
         expData <- df2mat(expData)}

     if(norm == TRUE){
         message("normalization...")
         expData <- as.data.frame(lapply(as.data.frame(expData), normalization))
         expData <- df2mat(expData)}

     message("Gaussian kernel...")
     message("it may take time...")
     totalDis <- dist(t(as.matrix(expData)), method = "euclidean")
     adja <- GaussianKernel( x = t(as.matrix(expData)), sigma = var(totalDis))
     rm(totalDis, expData)
     checkSym(adja, "Gussian kernel")

     adja <- df2mat(adja)

     if(tom == TRUE){
         message("TOM...\n it may take time...")
         adja <- TOM(adja)
         checkSym(adja, "TOM")

     }

     diag(adja) <- 0

     if(saveAdja){
         if(!is.character(adjaNameFile)){
             warning("adjaNamFile is not string", call. = FALSE)
             temp <- paste0("using following name \n ",  " adjacency.RData")
             message(temp)
             adjaNameFile <- "adjacency.RData"}

         save(adja, file = adjaNameFile)
         message("adjancency matrix is stored") }

     if(!is.null(hm)){
         hm_plt <- SGCP_plot_heatMap(adja, tit = "Adjacency Heatmap",
                                     xname = "genes", yname = "genes")
         jpeg(hm)
         show(hm_plt)
         dev.off()

         rm(hm_plt)
     }

     message("network is created, done!...\n")
     return(adja)

 
 
 }