Run the activator-repressor classification for the TFs for a GRN object

  significanceThreshold_Wilcoxon = 0.05,
  plot_minNoTFBS_heatmap = 100,
  deleteIntermediateData = TRUE,
  plotDiagnosticPlots = TRUE,
  outputFolder = NULL,
  corMethod = "pearson",
  forceRerun = FALSE



Object of class GRN


Numeric[0,1]. Default 0.05. Significance threshold for Wilcoxon test that is run in the end for the final classification. See the Vignette and *diffTF* paper for details.


Integer[1,]. Default 100. Minimum number of TFBS for a TF to be included in the heatmap that is part of the output of this function.


TRUE or FALSE. Default TRUE. Should intermediate data be deleted before returning the object after a successful run? Due to the size of the produced intermediate data, we recommend setting this to TRUE, but if memory or object size are not an issue, the information can also be kept.


TRUE or FALSE. Default TRUE. Run and plot various diagnostic plots? If set to TRUE, PDF files will be produced and saved in the output directory (in a subfolder called plots).


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.


Character. One of pearson, spearman or bicor. Default pearson. Method for calculating the correlation coefficient. For pearson and spearman , see cor for details. bicor denotes the *biweight midcorrelation*, a correlation measure based on medians as calculated by WGCNA::bicorAndPvalue. Both spearman and bicor are considered more robust measures that are less prone to be affected by outliers.


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


An updated GRN object, with additional information added from this function.


# See the Workflow vignette on the GRaNIE website for examples
# GRN = loadExampleObject()
# GRN = AR_classification_wrapper(GRN, outputFolder = ".", forceRerun = FALSE)