voomDDA.train = function (counts, conditions, normalization = c("TMM", "deseq", "none"), pooled.var = TRUE) 

{

  normalization = match.arg(normalization)

  counts = data.matrix(counts)

  p = nrow(counts)

  n = ncol(counts)

  nclass = length(unique(conditions))

  n.class = table(conditions)

  if (min(n.class) == 1) {

    stop(warning("Warning: a class contains only 1 sample"))

  }

  #### voom transformation steps

{

  if (normalization == "TMM"){

    design = model.matrix(~conditions)

    rownames(design) = colnames(counts)

    dge = DGEList(counts = counts)

    dge = calcNormFactors(dge, method = "TMM")

    vm = voom(dge, design, plot=F)

    x = vm $ E #pxn dim gene expression matrix

    w = vm $ weights #pxn dim weight matrix

    dimnames(w) = dimnames(x)

  } 

  else if (normalization == "deseq"){

    design = model.matrix(~conditions)

    rownames(design) = colnames(counts)

    dge = DGEList(counts = counts)

    dge = calcNormFactors(dge, method = "RLE")

    vm = voom(dge, design, plot=F)

    x = vm $ E #pxn dim gene expression matrix

    w = vm $ weights #pxn dim weight matrix

    dimnames(w) = dimnames(x)

  }

  else {

    design = model.matrix(~conditions)

    rownames(design) = colnames(counts)

    vm = voom(counts, design, plot=F)

    x = vm $ E #pxn dim gene expression matrix

    w = vm $ weights #pxn dim weight matrix

    dimnames(w) = dimnames(x)

  }

}

  if(is.factor(conditions)) classNames = levels(conditions)

  wStats = weighted.stats(x, w, conditions)

  results = structure(list(counts = counts, conditions = factor(conditions), weightedStats = wStats, normalization = normalization,

                           nclass = nclass, classNames = classNames, PooledVar = pooled.var), class = "voomDDA")

  return(results)

}