--- a
+++ b/weighted.stats.R
@@ -0,0 +1,99 @@
+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)
+  }
\ No newline at end of file