#Calculates significant genes in each pattern according to certain threshold
#Returns the significant gene names as well as well as the correlation matrices between these genes and the means of these matrices
gapsIntraPattern <- function(Amean, Asd, DMatrix, sdThreshold = 3)
    #number of rows and cols of Asd
    numGenes = length(Asd[,1]);
    numCols = length(Asd[1,]);

    #number of samples in DMatrix
    numSamp = ncol(DMatrix);

    #Vector holding the number of each significant gene in each column
    sigGeneNums = data.frame();

    #Temp number of sig genes in the col
    sigCount = 0;

    #Keep an array of the significant gene counts
    significantGeneNums = c(0);

    #A matrix to hold the significant genes in D for the current pattern
    #The matrix just acts as a subset of D, just eliminates non relevant rows
    tempSubsetD = matrix();

    #A matrix holding the values of our correlation coefficients between genes for the current column
    tempGeneCorrMatrix = matrix();

    #A list to hold all the correlation matrices
    geneCorrMatrices = list();

    #A list to hold all the means
    geneCorrMatrMeans = list();

    #The mean of all the correlation matrices
    cbar = 0;

    #A list to return the means and the matrices
    results = list();

    #Scan in the significant genes from each column of Asd
    #The columns of sigGeneNums hold the significant genes for each col of Asd
    for(i in 1:numCols)
        sigCount = 0;
        for(j in 1:numGenes)
            if((Amean[j,i] - (sdThreshold*Asd[j,i])) > 0)
                sigCount = sigCount + 1;
                sigGeneNums[sigCount, i] = j;

        if(sigCount == 0)
            sigGeneNums[1, i] = 0;

        #Save the number of sigGenes
        significantGeneNums[i] = sigCount;

    #If a pattern has no significant genes this is clearly an error so return such
    if(any(significantGeneNums == 0))
        zeroSigCols = which(significantGeneNums == 0);
        warning("Warning: No Significant Genes in Pattern(s): ");

        for(z in 1:length(zeroSigCols))

    #Now that we have the significant genes want to grab these from our original D matrix
    #and find the sigGene x sigGene correlation matrix and find its mean

    for(j in 1:numCols)
        #Grab the number of significant genes from the interested column
        sigCount = sum(sigGeneNums[,j] > 0, na.rm = TRUE);

        if(sigCount != 0)

            #loop through the number of significant genes and pull out the rows of D that represent these genes.
            #Then find the correlation between them with the built in R corr function
            tempSubsetD = matrix(nrow = sigCount, ncol = numSamp);
            for(k in 1:sigCount)
                #Subset D based on significant Genes
                #need to transpose as it reads this in as column vector otherwise
                tempSubsetD[k,] = t(DMatrix[sigGeneNums[k,j], ]);

            #Find the correlation between these genes in D
            #Need to transpose as it calculates correlations between the columns
            tempGeneCorrMatrix = cor(t(tempSubsetD));

            #Find the mean of this matrix
            tempGeneCorrMatrMean = mean(tempGeneCorrMatrix);

            tempGeneCorrMatrix = 0;
            tempGeneCorrMatrMean = 0;

        #Save these in the overall list
        geneCorrMatrices[[j]] = tempGeneCorrMatrix;
        geneCorrMatrMeans[[j]] = tempGeneCorrMatrMean;


    #Return as an overall list of lists
    # We return Corr Matrices themselves, their means, and the means of the means (cbar)
    results[[1]] = geneCorrMatrices;
    results[[2]] = geneCorrMatrMeans;

    #Return as an overall list of lists
    for(i in 1:numCols)
        cbar = cbar + results[[2]][[i]];

    cbar = cbar/numCols;
    results[[3]] = cbar;

    names(results) = c("CorrelationMatrices", "CorrelationMatrixMeans", "IntraPatternValue");