updated motif tutorial

#' ModuleTFNetwork

#'

#' Plotting the relationships between a TF and the co-expression modules

#'

#' @param seurat_obj A Seurat object

#' @param tf_name the Motif name for the tf

#' @param tf_gene_name the gene associated with this tf in the rownames(seurat_obj)

#' @param edge.alpha scaling factor for edge opacity in the network

#' @param cor_thresh threshold to plot correlation edges between modules

#' @param high_color color for positive correlation

#' @param mid_color color for zero correlation

#' @param low_color color for negative correlation

#' @param slot the slot in the seurat object to extract expression data for the tf_gene_name

#' @param size.scale scaling factor for the size of each node

#' @param tf_x x coordinate for the TF if the TF is not found in the UMAP

#' @param tf_y y coordinate for the TF if the TF is not foudn in the UMAP

#' @param wgcna_name the name of the WGCNA experiment in the seurat object

#' @keywords scRNA-seq

#' @export

#' @examples

#' ModuleTFNetwork

ModuleTFNetwork <- function(

  seurat_obj,

  tf_name,

  low_color = 'blue',

  slot = 'data',

  size.scale = 30,

  tf_x = 0,

  tf_y = 0,

  wgcna_name = NULL

){

  node_df$size <- as.numeric(node_df$size_intersection) / as.numeric(mod_sizes)

  # add info for tf to this table:

  if(tf_gene_name %in% umap_df$gene){

    tf_df <- umap_df[umap_df$gene == tf_gene_name, ] %>%

      dplyr::select(-c(hub, kME, module)) %>%

      dplyr::rename(name=gene)

  } else{

    tf_df <- data.frame(

      name = tf_gene_name,

      module = 'grey',

      color = 'grey',

      UMAP1 = tf_x,

      UMAP2 = tf_y

    )

  }

  tf_df$size <- 0.25

  # set up edge df

    vertex.label=V(g1)$name,

    vertex.label.dist=1.1,

    vertex.label.degree=-pi/4,

    vertex.label.family='Helvetica', #vertex.label.font=vertex_df$font,

    vertex.label.family='Helvetica',

    vertex.label.font = 3,

    vertex.label.color = 'black',

    vertex.label.cex=0,