updated some buigs

  # get TOM

  TOM <- GetTOM(seurat_obj, wgcna_name)

  # get hub genes:

  n_hubs <- 25

  hub_list <- lapply(mods, function(cur_mod){

    cur <- subset(modules, module == cur_mod)

    cur <- cur[,c('gene_name', paste0('kME_', cur_mod))] %>%

      top_n(n_hubs)

    colnames(cur)[2] <- 'var'

    cur %>% arrange(desc(var)) %>% .$gene_name

  })

  names(hub_list) <- mods

  print('here')

  # loop over modules

  for(cur_mod in mods){

    print(cur_mod)

    cur_color <- modules %>% subset(module == cur_mod) %>% .$color %>% unique

    # number of genes, connections

    # name of column with current kME info

    cur_kME <- paste0('kME_', cur_mod)

    # get genes in this module:

    cur <- subset(modules, module == cur_mod)

    rowind <- cur[,c('gene_name', cur_kME)] %>%

      top_n(n_genes) %>% .$gene_name

    cur <- cur[rowind,]

    # arrange by cur_kME

    cur <- cur %>% arrange(get(cur_kME), descending=TRUE)

    cur_genes <- hub_list[[cur_mod]]

    print(cur_genes)

    if (nrow(cur) < n_genes) {

      n_genes <-  nrow(cur);

      n_conns <- n_genes * (n_genes - 1);

    }

    # if (length(cur_genes) < n_genes) {

    #   n_genes <-  length(cur_genes);

    #   n_conns <- n_genes * (n_genes - 1);

    # }

    # Identify the columns in the TOM that correspond to these hub genes

    matchind <- match(cur$gene_name, colnames(TOM))

    matchind <- match(cur_genes, colnames(TOM))

    reducedTOM = TOM[matchind,matchind]

    orderind <- order(reducedTOM,decreasing=TRUE)

    reducedTOM <- matrix(0,nrow(reducedTOM),ncol(reducedTOM));

    reducedTOM[connections2keep] <- 1;

    print('here')

    print(dim(reducedTOM))

    print(n_genes)

    # only label the top 10 genes?

    if(label_center){cur$gene_name[11:25] <- ''}

    if(label_center){cur_genes[11:25] <- ''}

    # top 10 as center

    gA <- graph.adjacency(as.matrix(reducedTOM[1:10,1:10]),mode="undirected",weighted=TRUE,diag=FALSE)

      edge.color=adjustcolor(cur_color, alpha.f=0.25),

      edge.alpha=edge.alpha,

      vertex.color=cur_color,

      vertex.label=as.character(cur$gene_name),

      vertex.label=as.character(cur_genes),

      vertex.label.dist=1.1,

      vertex.label.degree=-pi/4,

      vertex.label.color="black",

  group.by=NULL,

  module_names = NULL,

  combine=TRUE, #returns a list of individual heatmaps if FALSE

  draw.lines=TRUE,

  disp.min = -2.5, disp.max = 2.5, # cutoff expression values

  wgcna_name=NULL

){

  }

  # drop if there are missing levels:

  seurat_plot@meta.data[[group.by]] <- droplevels(seurat_plot@meta.data[[group.by]])

  seurat_obj@meta.data[[group.by]] <- droplevels(seurat_obj@meta.data[[group.by]])

  # get modules

  modules <- GetModules(seurat_obj, wgcna_name)

  mod_colors <- modules %>% dplyr::select(c(module, color)) %>% distinct

  # get hub genes:

  # hub_list <- lapply(mods, function(cur_mod){

  #   cur <- subset(modules, module == cur_mod)

  #   cur[,c('gene_name', paste0('kME_', cur_mod))] %>%

  #     top_n(n_hubs) %>% .$gene_name

  # })

  hub_list <- lapply(mods, function(cur_mod){

    cur <- subset(modules, module == cur_mod)

    cur[,c('gene_name', paste0('kME_', cur_mod))] %>%

      top_n(n_hubs) %>% .$gene_name

    cur <- cur[,c('gene_name', paste0('kME_', cur_mod))] %>%

      top_n(n_hubs)

    colnames(cur)[2] <- 'var'

    cur %>% arrange(desc(var)) %>% .$gene_name

  })

  names(hub_list) <- mods

  print(hub_list)

  seurat_obj$barcode <- colnames(seurat_obj)

  temp <- table(seurat_obj@meta.data[[group.by]])

        features = hub_list[[i]],

        group.by=group.by,

        raster=TRUE, slot='scale.data',

        disp.min = -2.5, disp.max=2.5,

        label=FALSE, group.bar=FALSE

        disp.min = disp.min, disp.max=disp.max,

        label=FALSE,

        group.bar=FALSE,

        draw.lines=draw.lines

      )

    } else{

      plot_list[[i]] <- DoHeatmap(

       raster=TRUE, slot='scale.data',

       group.bar.height=0,

       label=FALSE, group.bar=FALSE,

       disp.min = -2.5, disp.max=2.5

       draw.lines=draw.lines,

       disp.min = disp.min, disp.max=disp.max

     ) + NoLegend()

    }

    print(i)