weighted.stats =

  function (x, w, conditions) 

  {

    n = ncol(x) #number of samples

    p = nrow(x) #number of genes

    nclass = length(unique(conditions)) #number of class

    if(is.factor(conditions)) {cNames = sort(levels(conditions))}

    if (is.numeric(conditions)) {cNames = as.character(sort(unique(conditions)))}

    WM = WS = wSum = se.scale = matrix(0, p, nclass)

    rownames(WS) = rownames(WM) = rownames(wSum) = rownames(se.scale) = rownames(x)

    colnames(WS) = colnames(WM) = colnames(wSum) = colnames(se.scale) = cNames

    c.ind = as.numeric(conditions)

    w.mean00 =

      function (x, w) 

      {

        wm = NULL

        for (i in 1:p)

        {

          wm0 = sum(w[i,]*x[i,]) / sum(w[i,])

          wm = c(wm, wm0)

        }

        return(wm)

      }

    w.mean =

      function (x, w, conditions) 

      {

        for (j in 1:nclass)

        {

          WM[,j] = w.mean00(x[,c.ind == j], w[,c.ind == j])

        }

        return(WM)

      }

    w.sd =

      function (x, w, conditions) 

      {

        w.sd00 =

          function (x, w) 

          {

            ws = NULL

            w.sd0 =

              function (x, w)

              {

                sumw = sum(w)

                sumw.sq = sum(w)^2

                w.sq = sum(w^2)

                denom = sum(w * ((x - mean(x))^2))

                sqrt((sumw * denom) / (sumw.sq - w.sq))

              }

            for (i in 1:p)

            {

              ws0 = w.sd0(x[i,], w[i,])

              ws = c(ws, ws0)

            }

            return(ws)

          }

        for (j in 1:nclass)

        {

          WS[,j] = w.sd00(x[,c.ind == j], w[,c.ind == j])

        }

        return(WS)

      }

    weightedMean = w.mean00(x, w) #Overall weighted mean

    weightedMean.C = w.mean(x, w, conditions) #Weighted means for each group 

    weightedSD.C = w.sd(x, w, conditions) #Weighted standard deviations for each group

    #weightedSD.pooled = weightedSD.C

    for (i in 1:nclass)

    {

      tmp = w[,which(c.ind == i)]

      rSum.tmp = rowSums(tmp)

      wSum[,i] = rSum.tmp

    }

    weightedSD.pooled = sqrt(rowSums((wSum-1) * (weightedSD.C^2)) / (rowSums(wSum) - nclass))

    se.scale = sqrt(1 / wSum + 1 / rowSums(wSum))

    s0 = median(weightedSD.pooled)

    delta = (weightedMean.C - weightedMean)/(se.scale*(weightedSD.pooled + s0))

    weightedSD.pooled = sqrt(rowSums(as.data.frame(weightedSD.pooled)) / (n - nclass))

    stats = list(n = n, p = p, nclass = nclass, se.scale = se.scale, weightedMean = weightedMean, weightedMean.C = weightedMean.C, weightedSD.C = weightedSD.C, weightedSD.pooled = weightedSD.pooled, delta = delta)

    return(stats)

  }