diff --git a/R/normalize.R b/R/normalize.R
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+# Normalization [CCBR] (afc2524c-9bae-4873-98c1-e06ef8f4632b): v273
+Normalized_Counts_MOSuite <- function(Filtered_Counts, metadata_ccbr_bulk_training) {
+  ## --------- ##
+  ## Libraries ##
+  ## --------- ##
+
+  library(limma)
+  library(tidyverse)
+  library(edgeR)
+  library(ggplot2)
+  library(plotly)
+  library(dplyr)
+  library(RColorBrewer)
+  library(colorspace)
+  library(stringr)
+  library(RCurl)
+  library(reshape2)
+  library(gridExtra)
+  library(amap)
+  library(lattice)
+  library(gplots)
+  library(gridGraphics)
+  library(dendsort)
+  library(ComplexHeatmap)
+  library(ggrepel)
+
+  ## -------------------------------- ##
+  ## User-Defined Template Parameters ##
+  ## -------------------------------- ##
+
+  # Basic Parameters:
+  counts_matrix <- Filtered_Counts
+  sample_metadata <- metadata_ccbr_bulk_training
+  gene_names_column <- "Gene"
+  columns_to_include <- c("Gene", "A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3")
+  sample_names_column <- "Sample"
+  groups_column <- "Group"
+  labels_column <- "Label"
+
+
+  # Normalization Parameters:
+  input_in_log_counts <- FALSE
+  normalization_method <- "quantile"
+
+  # PCA parameters:
+  samples_to_rename_manually_on_pca <- c("")
+  add_labels_to_pca <- TRUE
+  principal_component_on_x_axis_for_pca <- 1
+  principal_component_on_y_axis_for_pca <- 2
+  legend_position_for_pca <- "top"
+  label_offset_x_for_pca <- 2
+  label_offset_y_for_pca <- 2
+  label_font_size_for_pca <- 3
+  point_size_for_pca <- 8
+
+  # Histogram parameters:
+  color_histogram_by_group <- TRUE
+  set_min_max_for_x_axis_for_histogram <- FALSE
+  minimum_for_x_axis_for_histogram <- -1
+  maximum_for_x_axis_for_histogram <- 1
+  legend_font_size_for_histogram <- 10
+  legend_position_for_histogram <- "top"
+  number_of_histogram_legend_columns <- 6
+
+  # Visualization Parameters:
+  number_of_image_rows <- 2
+  colors_for_plots <- c()
+  make_plots_interactive <- FALSE
+  plot_correlation_matrix_heatmap <- TRUE
+
+
+  ## --------------- ##
+  ## Error Messages ##
+  ## -------------- ##
+
+
+  ## --------- ##
+  ## Functions ##
+  ## --------- ##
+
+  ### PH: START Color Picking Function
+  ##################################
+  ##### Color Picking Function
+  ##################################
+
+  ### Do not Functionalize This code yet.
+  ### I will make a General Color picker function to create a custom Color Palette to use for all figures
+  ### I think I would also like to use the metadata table to manually set colors
+  getourrandomcolors <- function(k) {
+    seed <- 10
+    n <- 2e3
+    ourColorSpace <- colorspace::RGB(runif(n), runif(n), runif(n))
+    ourColorSpace <- as(ourColorSpace, "LAB")
+    currentColorSpace <- ourColorSpace@coords
+    # Set iter.max to 20 to avoid convergence warnings.
+    set.seed(seed)
+    km <- kmeans(currentColorSpace, k, iter.max = 20)
+    return(unname(hex(LAB(km$centers))))
+  }
+
+
+  colorlist <- c(
+    "#5954d6", "#e1562c", "#b80058",
+    "#00c6f8", "#d163e6", "#00a76c",
+    "#ff9287", "#008cf9", "#006e00",
+    "#796880", "#FFA500", "#878500"
+  )
+  names(colorlist) <- c(
+    "indigo", "carrot", "lipstick",
+    "turquoise", "lavender", "jade",
+    "coral", "azure", "green",
+    "rum", "orange", "olive"
+  )
+  if (length(colors_for_plots) == 0) {
+    colors_for_plots <- c(
+      "indigo", "carrot", "lipstick",
+      "turquoise", "lavender", "jade",
+      "coral", "azure", "green",
+      "rum", "orange", "olive"
+    )
+  }
+
+  ### PH: END Color Picking Function
+
+
+  ### PH: START Heatmap Function (Previously created from Filter Template)
+  ################################
+  ### Heatmap Function
+  ################################
+  make_heatmap <- function(counts_matrix, sample_metadata, anno_col, anno_column) {
+    ## Annotate
+    rownames(sample_metadata) <- sample_metadata[[labels_column]]
+    annoVal <- lapply(anno_column, function(x) {
+      out <- as.factor(sample_metadata[, x]) %>% levels()
+      # names(out)=x
+      return(out)
+    }) %>% unlist()
+    col <- anno_col[1:length(annoVal)]
+    names(col) <- annoVal
+
+    cols <- lapply(anno_column, function(x) {
+      ax <- as.factor(sample_metadata[, x]) %>% levels()
+      out <- col[ax]
+      return(out)
+    })
+    names(cols) <- (anno_column)
+
+    anno <- columnAnnotation(
+      df = sample_metadata[, anno_column, drop = F],
+      col = cols
+    )
+
+
+    ## Create Correlation Matrix
+
+    old <- sample_metadata[[sample_names_column]]
+    new <- sample_metadata[[labels_column]]
+    names(old) <- new
+    counts_matrix <- rename(counts_matrix, any_of(old))
+
+    mat <- as.matrix(counts_matrix)
+    tcounts <- t(mat)
+
+
+    ## calculate correlation
+    d <- Dist(tcounts, method = "correlation", diag = TRUE)
+    m <- as.matrix(d)
+
+    ## create dendogram
+    dend <- rev(dendsort(as.dendrogram(hclust(d, method = "average"))))
+
+
+
+    ### plot
+    new.palette <- colorRampPalette(c("blue", "green", "yellow"))
+    lgd <- Legend(new.palette(20), title = "Correlation", title_position = "lefttop-rot")
+    hm <- Heatmap(m,
+      heatmap_legend_param = list(
+        title = "Correlation",
+        title_position = "leftcenter-rot"
+      ),
+      cluster_rows = dend,
+      cluster_columns = dend,
+      top_annotation = anno,
+      row_names_gp = gpar(fontsize = 15),
+      column_names_gp = gpar(fontsize = 15),
+      # heatmap_height=unit(1, "npc"),
+      # heatmap_width=unit(.5, "npc"),
+      # width = unit(.5, "npc"),
+      # height = unit(.5, "npc"),
+      col = new.palette(20)
+    )
+
+    return(hm)
+  }
+
+  ### PH: End Heatmap Function (Previously created from Filter Template)
+
+
+  ## --------------- ##
+  ## Main Code Block ##
+  ## --------------- ##
+
+
+  ##############################
+  #### Create Generic Row names
+  ##############################
+  ## create unique rownames to correctly add back Annocolumns at end of template
+
+  samples_to_include <- columns_to_include[columns_to_include %in% sample_metadata[, sample_names_column, drop = T]]
+  anno_col <- columns_to_include[columns_to_include %in% sample_metadata[, sample_names_column, drop = T] == F]
+
+
+  samples_to_include <- samples_to_include[samples_to_include != gene_names_column]
+  samples_to_include <- samples_to_include[samples_to_include != "Gene"]
+  samples_to_include <- samples_to_include[samples_to_include != "GeneName"]
+
+  ## create unique rownames to correctly add back Annocolumns at end of template
+  counts_matrix[, gene_names_column] <- paste0(counts_matrix[, gene_names_column], "_", 1:nrow(counts_matrix))
+
+
+  anno_col <- c(anno_col, gene_names_column) %>% unique()
+  anno_tbl <- counts_matrix[, anno_col, drop = F] %>% as.data.frame()
+
+
+  df.filt <- counts_matrix[, samples_to_include]
+  gene_names <- NULL
+  gene_names$GeneID <- counts_matrix[, 1]
+
+
+
+
+  ##############################
+  #### Input Data Validation
+  ##############################
+  sample_metadata <- sample_metadata[match(colnames(df.filt), sample_metadata[[sample_names_column]]), ] # First match sample metadata to counts matrix
+  sample_metadata <- sample_metadata[rowSums(is.na(sample_metadata)) != ncol(sample_metadata), ] # Remove empty rows
+  sample_metadata <- sample_metadata[, colSums(is.na(sample_metadata)) == 0] # Remove empty columns
+  rownames(sample_metadata) <- sample_metadata[[sample_names_column]]
+
+  df.filt <- df.filt[, match(sample_metadata[[sample_names_column]], colnames(df.filt))] # Match counts matrix columns to sample metadata
+
+
+  ### PH: START Limma Normalization
+  ##############################
+  #### Limma Normalization
+  ##############################
+
+
+  # If input is in log space, linearize
+  if (input_in_log_counts == TRUE) {
+    x <- DGEList(counts = 2^df.filt, genes = gene_names)
+  } else {
+    x <- DGEList(counts = df.filt, genes = gene_names)
+  }
+
+  v <- voom(x, normalize = normalization_method)
+  rownames(v$E) <- v$genes$GeneID
+  as.data.frame(v$E) %>% rownames_to_column(gene_names_column) -> df.voom
+  print(paste0("Total number of features included: ", nrow(df.voom)))
+
+  ### PH: END Limma Normalization
+
+
+
+  ### PH: START PCA Plot
+  ########################
+  ## PCA Plot:
+  ########################
+  ## same function as used from Filter Template except input is Normalized Counts table instead of Raw Data.
+  ## major difference is that rawData had to be log transformed and This data does not so we will either
+  ## need a parameter that instructs function to log transform data or some logic to detect log transformed data
+
+
+  edf <- v$E
+  tedf <- t(edf)
+  tedf <- tedf[, colSums(is.na(tedf)) != nrow(tedf)]
+  tedf <- tedf[, apply(tedf, 2, var) != 0]
+  pca <- prcomp(tedf, scale. = T)
+
+  pcx <- paste0("PC", principal_component_on_x_axis_for_pca)
+  pcy <- paste0("PC", principal_component_on_y_axis_for_pca)
+  pca.df <- as.data.frame(pca$x) %>% dplyr::select(.data[[pcx]], .data[[pcy]])
+  pca.df$group <- sample_metadata[[groups_column]]
+  pca.df$sample <- sample_metadata[[labels_column]]
+  perc.var <- (pca$sdev^2 / sum(pca$sdev^2)) * 100
+  perc.var <- formatC(perc.var, format = "g", digits = 4)
+  pc.x.lab <- paste0(pcx, " ", perc.var[principal_component_on_x_axis_for_pca], "%")
+  pc.y.lab <- paste0(pcy, " ", perc.var[principal_component_on_y_axis_for_pca], "%")
+  labelpos <- pca.df
+  labelpos$mean_y <- pca.df[[pcy]] + label_offset_y_for_pca
+  labelpos$mean_x <- pca.df[[pcx]] + label_offset_x_for_pca
+  pca.df$xdata <- pca.df[[pcx]]
+  pca.df$ydata <- pca.df[[pcy]]
+
+  # Manual changes to sample names
+  replacements <- samples_to_rename_manually_on_pca
+
+  if (!is.null(replacements)) {
+    if (replacements != c("")) {
+      for (x in replacements) {
+        old <- strsplit(x, ": ?")[[1]][1]
+        new <- strsplit(x, ": ?")[[1]][2]
+        pca.df$sample <- ifelse(pca.df$sample == old, new, pca.df$sample)
+      }
+    }
+  }
+
+
+  colorval <- colorlist[colors_for_plots]
+  colorval <- unname(colorval) # remove names which affect ggplot
+
+  if (length(unique(sample_metadata[[groups_column]])) > length(colorval)) {
+    ## Original color-picking code.
+    k <- length(unique(sample_metadata[[groups_column]])) - length(colorval)
+    more_cols <- getourrandomcolors(k)
+    colorval <- c(colorval, more_cols)
+  }
+  #### plot PCA
+  pcaPlot <- ggplot(pca.df, aes(x = xdata, y = ydata, text = sample)) +
+    geom_point(aes(color = group), text = sample, size = point_size_for_pca) +
+    theme_bw() +
+    theme(
+      legend.position = legend_position_for_pca,
+      legend.title = element_blank(),
+      panel.grid.major = element_blank(),
+      panel.grid.minor = element_blank(),
+      panel.background = element_blank(),
+      axis.text = element_text(size = 18),
+      axis.title = element_text(size = 20),
+      # panel.grid.major = element_line(size = 1),
+      # axis.line=element_line(size=1),
+      panel.border = element_rect(colour = "black", fill = NA, size = 1),
+      axis.ticks = element_line(size = 1),
+      legend.text = element_text(size = 18)
+    ) +
+    coord_fixed() +
+    scale_colour_manual(values = colorval) +
+    xlab(pc.x.lab) +
+    ylab(pc.y.lab)
+
+  if (add_labels_to_pca == TRUE) {
+    pcaPlot <- pcaPlot +
+      geom_text_repel(aes(label = sample, color = group),
+        size = 7,
+        show.legend = F,
+        direction = c("both"),
+        box.padding = 1.25
+      )
+  }
+
+  par(mfrow = c(2, 1))
+  ### PH: END PCA Plot
+
+
+  ### PH: START Histogram plot
+  ########################
+  ## Start Histogram Plot:
+  ########################
+  ## same function as used from Filter Template only change I think we need is to recognize the type of Counts input to label X axis ( is this normalized or Raw Counts)
+  ## my thought would be just make it a parameter to set  X axis label and we will set the defaults in the Workflow function
+
+  df.m <- melt(edf, id.vars = c(gene_names_column))
+  df.m <- dplyr::rename(df.m, sample = Var2)
+
+  if (set_min_max_for_x_axis_for_histogram == TRUE) {
+    xmin <- minimum_for_x_axis_for_histogram
+    xmax <- maximum_for_x_axis_for_histogram
+  } else {
+    xmin <- min(df.m$value)
+    xmax <- max(df.m$value)
+  }
+
+  if (color_histogram_by_group == TRUE) {
+    df.m %>% mutate(colgroup = sample_metadata[sample, groups_column]) -> df.m
+    df.m <- df.m[complete.cases(df.m[, "colgroup"]), ]
+    df.m$colgroup <- gsub("\\s", "_", df.m$colgroup)
+    df.m$colgroup <- factor(df.m$colgroup, levels = unique(df.m$colgroup))
+    # print(unique(df.m$sample))
+    n <- length(levels(df.m$colgroup))
+    cols <- colorval[1:n]
+    # cols<- getourrandomcolors(n)
+
+    histPlot <- ggplot(df.m, aes(x = value, group = sample)) +
+      geom_density(aes(colour = colgroup), size = 1)
+  } else {
+    df.m$sample <- sample_metadata[df.m$sample, labels_column]
+    n <- length(unique(df.m$sample))
+    cols <- getourrandomcolors(n)
+    # cols=colorval[1:n]
+
+    histPlot <- ggplot(df.m, aes(x = value, group = sample)) +
+      geom_density(aes(colour = sample), size = 1)
+  }
+  histPlot <- histPlot +
+    xlab("Normalized Counts") + ylab("Density") +
+    theme_bw() +
+    theme(
+      legend.position = legend_position_for_histogram,
+      legend.text = element_text(size = legend_font_size_for_histogram),
+      legend.title = element_blank(),
+      panel.background = element_blank(),
+      axis.text = element_text(size = 18),
+      axis.title = element_text(size = 20),
+      panel.border = element_rect(colour = "black", fill = NA, size = 0),
+      axis.line = element_line(size = .5),
+      axis.ticks = element_line(size = 1)
+    ) +
+    # ggtitle("Frequency Histogram") +
+    xlim(xmin, xmax) +
+    # scale_linetype_manual(values=rep(c('solid', 'dashed','dotted','twodash'),n)) +
+    scale_colour_manual(values = cols) +
+    guides(linetype = guide_legend(ncol = number_of_histogram_legend_columns))
+
+  ########################
+  ### Output Figures
+  ########################
+
+  ### PH START:  Make Plots interactive.
+  ## We can add logic to PCA and Histrogram to make them interactive using Plotly
+  if (plot_correlation_matrix_heatmap == TRUE) {
+    if (make_plots_interactive == TRUE) {
+      pcaPlot1 <- (pcaPlot) %>% ggplotly(tooltip = c("sample", "group"))
+      histPlot2 <- (histPlot + theme(legend.position = "none")) %>% ggplotly(tooltip = c("sample"))
+      ### PH END:  Make Plots interactive.
+      ## Rest of Code is NIDAP specific
+
+
+      grid.newpage()
+      print(pcaPlot1)
+      grid.newpage()
+      print(histPlot2)
+    } else {
+      require(gridExtra)
+      # corHM<-make_heatmap(df.filt,sample_metadata,colorval)
+      corHM <- make_heatmap(
+        counts_matrix = df.filt,
+        sample_metadata = sample_metadata,
+        anno_col = colorval,
+        anno_column = groups_column
+      )
+
+
+      grid.newpage()
+      print(pcaPlot)
+      grid.newpage()
+      print(corHM)
+      grid.newpage()
+      print(histPlot)
+    }
+  } else {
+    if (make_plots_interactive == TRUE) {
+      pcaPlot1 <- (pcaPlot) %>% ggplotly(tooltip = c("sample", "group"))
+      histPlot2 <- (histPlot + theme(legend.position = "none")) %>% ggplotly(tooltip = "sample")
+
+      grid.newpage()
+      print(pcaPlot1)
+      grid.newpage()
+      print(histPlot2)
+    } else {
+      grid.newpage()
+      print(pcaPlot)
+      grid.newpage()
+      print(histPlot)
+    }
+  }
+
+  print("Sample columns")
+  print(colnames(df.voom)[!colnames(df.voom) %in% gene_names_column])
+  print("Feature Columns")
+  print(colnames(anno_tbl))
+
+  df.voom <- merge(anno_tbl, df.voom, by = gene_names_column, all.y = T)
+  df.voom[, gene_names_column] <- gsub("_[0-9]+$", "", df.voom[, gene_names_column])
+
+  return(df.voom)
+}
+
+#################################################
+## Global imports and functions included below ##
+#################################################