This function can visualize a filtered eGRN in a very flexible manner and requires a GRN object as generated by build_eGRN_graph.

  outputFolder = NULL,
  basenameOutput = NULL,
  plotAsPDF = TRUE,
  pdf_width = 12,
  pdf_height = 12,
  title = NULL,
  maxEdgesToPlot = 500,
  nCommunitiesMax = 8,
  graph = "TF-gene",
  colorby = "type",
  layout = "fr",
  vertice_color_TFs = list(h = 10, c = 85, l = c(25, 95)),
  vertice_color_peaks = list(h = 135, c = 45, l = c(35, 95)),
  vertice_color_genes = list(h = 260, c = 80, l = c(30, 90)),
  vertexLabel_cex = 0.4,
  vertexLabel_dist = 0,
  forceRerun = FALSE



Object of class GRN


Character or NULL. Default NULL. If set to NULL, the default output folder as specified when initiating the object in initializeGRN will be used. Otherwise, all output from this function will be put into the specified folder. If a folder is provided, while we recommend specifying an absolute path, a relative one also works.


NULL or character. Default NULL. Basename of the output files that are produced. If set to NULL, a default basename is chosen. If a custom basename is specified, all output files will have the chosen basename as file prefix, be careful with not overwriting already existing files (if forceRerun is set to TRUE)


TRUE or FALSE. Default TRUE.Should the plots be printed to a PDF file? If set to TRUE, a PDF file is generated, the name of which depends on the value of basenameOutput. If set to FALSE, all plots are printed to the currently active device. Note that most functions print more than one plot, which means you may only see the last plot depending on your active graphics device.


Number>0. Default 12. Width of the PDF, in cm.


Number >0. Default 12. Height of the PDF, in cm.


NULL or Character. Default NULL. Title to be assigned to the plot.


Integer > 0. Default 500. Refers to the maximum number of connections to be plotted. If the network size is above this limit, nothing will be drawn. In such a case, it may help to either increase the value of this parameter or set the filtering criteria for the network to be more stringent, so that the network becomes smaller.


Integer > 0. Default 8. Maximum number of communities that get a distinct coloring. All additional communities will be colored with the same (gray) color.


Character. Default TF-gene. One of: TF-gene, TF-peak-gene. Whether to plot a graph with links from TFs to peaks to gene, or the graph with the inferred TF to gene connections.


Character. Default type. Either type or community. Color the vertices by either type (TF/peak/gene) or community. See calculateCommunitiesStats


Character. Default fr. One of star, fr, sugiyama, kk, lgl, graphopt, mds, sphere


Named list. Default list(h = 10, c = 85, l = c(25, 95)). The list must specify the color in hcl format (hue, chroma, luminence). See the colorspace package for more details and examples


Named list. Default list(h = 135, c = 45, l = c(35, 95)).


Named list. Default list(h = 260, c = 80, l = c(30, 90)).


Numeric. Default 0.4. Font size (multiplication factor, device-dependent)


Numeric. Default 0 vertex. Distance between the label and the vertex.


TRUE or FALSE. Default FALSE. Force execution, even if the GRN object already contains the result. Overwrites the old results.


The same GRN object, without modifications.

See also


GRN = loadExampleObject()
#> Downloading GRaNIE example object from
#> INFO [2024-06-03 20:00:04] Storing GRN@data$RNA$counts matrix as sparse matrix because fraction of 0s is > 0.1 (0.44)
#> Finished successfully. You may explore the example object. Start by typing the object name to the console to see a summaty. Happy GRaNIE'ing!
GRN = visualizeGRN(GRN, maxEdgesToPlot = 700, graph = "TF-gene", colorby = "type")
#> INFO [2024-06-03 20:00:04] Number of edges for the TF-gene eGRN graph: 582
#> INFO [2024-06-03 20:00:04] Plotting many connections may need a lot of time and memory
#> INFO [2024-06-03 20:00:04] Plotting GRN network to ./GRN.network_visualisation.pdf
#> INFO [2024-06-03 20:00:05]  Finished successfully. Execution time: 1.2 secs