@@ -1506,8 +1506,8 @@ importTFData <- function(GRN, data, name, idColumn = "ENSEMBL", nameColumn = "TF

 #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.  

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

-#' GRN = loadExampleObject()

-#' GRN = AR_classification_wrapper(GRN, outputFolder = ".", forceRerun = FALSE)

+#' # GRN = loadExampleObject()

+#' # GRN = AR_classification_wrapper(GRN, outputFolder = ".", forceRerun = FALSE)

 #' @export

 AR_classification_wrapper<- function (GRN, significanceThreshold_Wilcoxon = 0.05, 

                                       plot_minNoTFBS_heatmap = 100, deleteIntermediateData = TRUE,

@@ -2276,7 +2276,8 @@ addConnections_TF_peak <- function (GRN, plotDiagnosticPlots = TRUE, plotDetails

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

 #' GRN = loadExampleObject()

-#' GRN = addConnections_peak_gene(GRN, promoterRange = 10000, outputFolder = ".")

+#' types =  list(c("protein_coding"))

+#' GRN = addConnections_peak_gene(GRN, promoterRange=10000, outputFolder=".", plotGeneTypes=types)

 addConnections_peak_gene <- function(GRN, overlapTypeGene = "TSS", corMethod = "pearson",

                                      promoterRange = 250000, TADs = NULL,

                                      nCores = 4, 

@@ -3465,23 +3466,23 @@ add_TF_gene_correlation <- function(GRN, corMethod = "pearson", addRobustRegress

         res.df = suppressMessages(tibble::as_tibble(res.m) %>%

                                     dplyr::mutate(TF.ENSEMBL   = getCounts(GRN, type = "rna", norm = TRUE, permuted = as.logical(permutationCur))$ENSEMBL[map_TF],

                                                   gene.ENSEMBL = getCounts(GRN, type = "rna", norm = TRUE, permuted = as.logical(permutationCur))$ENSEMBL[map_gene]) %>%

-                                    dplyr::filter(!is.na(gene.ENSEMBL), !is.na(TF.ENSEMBL)) %>%  # For some peak-gene combinations, no RNA-Seq data was available, these NAs are filtered

+                                    dplyr::filter(!is.na(.data$gene.ENSEMBL), !is.na(.data$TF.ENSEMBL)) %>%  # For some peak-gene combinations, no RNA-Seq data was available, these NAs are filtered

                                     dplyr::left_join(GRN@data$TFs$translationTable, by = c("TF.ENSEMBL")) %>%

                                     dplyr::select(tidyselect::all_of(selectColumns))) %>%

-          dplyr::mutate(gene.ENSEMBL = as.factor(gene.ENSEMBL), 

-                        TF.ENSEMBL   = as.factor(TF.ENSEMBL),

-                        TF.name           = as.factor(TF.name)) %>%

-          dplyr::rename(TF_gene.r     = r, 

-                        TF_gene.p_raw = p.raw) %>%

+          dplyr::mutate(gene.ENSEMBL = as.factor(.data$gene.ENSEMBL), 

+                        TF.ENSEMBL   = as.factor(.data$TF.ENSEMBL),

+                        TF.name           = as.factor(.data$TF.name)) %>%

+          dplyr::rename(TF_gene.r     = .data$r, 

+                        TF_gene.p_raw = .data$p.raw) %>%

           dplyr::select(TF.name, TF.ENSEMBL, gene.ENSEMBL, tidyselect::everything())

         if (addRobustRegression) {

           res.df = dplyr::rename(res.df, 

-                                 TF_gene.p_raw.robust = p_raw.robust, 

-                                 TF_gene.r_robust = r_robust,

-                                 TF_gene.bias_M_p.raw = bias_M_p.raw,

-                                 TF_gene.bias_LS_p.raw = bias_LS_p.raw)

+                                 TF_gene.p_raw.robust = .data$p_raw.robust, 

+                                 TF_gene.r_robust = .data$r_robust,

+                                 TF_gene.bias_M_p.raw = .data$bias_M_p.raw,

+                                 TF_gene.bias_LS_p.raw = .data$bias_LS_p.raw)

         }

       } else {

@@ -3643,7 +3644,7 @@ addSNPOverlap <- function(grn, SNPData, col_chr = "chr", col_pos = "pos", col_pe

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

 #' GRN = loadExampleObject()

-#' GRN = generateStatsSummary(GRN, forceRerun = FALSE)

+#' GRN = generateStatsSummary(GRN, TF_peak.fdr = c(0.01, 0.1), peak_gene.fdr = c(0.01, 0.1))

 #' 

 generateStatsSummary <- function(GRN, 

                                  TF_peak.fdr = c(0.001, 0.01, 0.05, 0.1, 0.2),

@@ -3931,9 +3932,9 @@ loadExampleObject <- function(forceDownload = FALSE, fileURL = "https://www.embl

   bfc <- .get_cache()

-  rid <- BiocFileCache::bfcquery(bfc, "geneFileV2", "rname")$rid

+  rid <- BiocFileCache::bfcquery(bfc, "GRaNIE_object_example")$rid

   if (!length(rid)) {

-    rid <- names(BiocFileCache::bfcadd(bfc, "geneFileV2", fileURL))

+    rid <- names(BiocFileCache::bfcadd(bfc, "GRaNIE_object_example", fileURL))

   }

   if (isFALSE(BiocFileCache::bfcneedsupdate(bfc, rid)) | forceDownload) {

     messageStr = paste0("Downloading GRaNIE example object from ", fileURL)

@@ -4237,7 +4238,7 @@ getGRNConnections <- function(GRN, type = "all.filtered",  permuted = FALSE, inc

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

 #' GRN = loadExampleObject()

-#' getParameters(GRN, type = "parameter", name = "all")

+#' params.l = getParameters(GRN, type = "parameter", name = "all")

 getParameters <- function (GRN, type = "parameter", name = "all") {

   checkmate::assertClass(GRN, "GRN")

@@ -4362,12 +4363,12 @@ getBasic_metadata_visualization <- function(GRN, forceRerun = FALSE) {

 #' 

 #' @export

 #' @template GRN

-#' @param outputDirectory 

+#' @param outputDirectory Character. Default \code{.}. New output directory for all output files unless overwritten via the parameter \code{outputFolder}.

 #' @return The same \code{\linkS4class{GRN}} object, with the output directory being adjusted accordingly

 #' @examples 

-#' # GRN = loadExampleObject()

-#' # GRN = changeOutputDirectory(GRN, ".")

-changeOutputDirectory <- function(GRN, outputDirectory) {

+#' GRN = loadExampleObject()

+#' GRN = changeOutputDirectory(GRN, outputDirectory = ".")

+changeOutputDirectory <- function(GRN, outputDirectory = ".") {

   GRN@config$directories$outputRoot   =  outputDirectory

   GRN@config$directories$output_plots = paste0(outputDirectory, "/plots/")

@@ -200,8 +200,8 @@ build_eGRN_graph <- function(GRN, model_TF_gene_nodes_separately = FALSE,

 #' @export

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

-#' GRN = loadExampleObject()

-#' GRN = performAllNetworkAnalyses(GRN, outputFolder = ".", forceRerun = FALSE)

+#' # GRN = loadExampleObject()

+#' # GRN = performAllNetworkAnalyses(GRN, outputFolder = ".", forceRerun = FALSE)

 #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.

 performAllNetworkAnalyses <- function(GRN, ontology = c("GO_BP", "GO_MF"), 

                                       algorithm = "weight01", statistic = "fisher",

@@ -17,7 +17,7 @@

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

 #' GRN = loadExampleObject()

-#' GRN = plotPCA_all(GRN, topn = 500, outputFolder = ".", forceRerun = FALSE)

+#' GRN = plotPCA_all(GRN, topn = 500, outputFolder = ".", type = "rna", plotAsPDF=FALSE)

 #' @export

 plotPCA_all <- function(GRN, outputFolder = NULL, basenameOutput = NULL, 

                         type = c("rna", "peaks"), topn = c(500,1000,5000), 

@@ -443,22 +443,28 @@ plotPCA_all <- function(GRN, outputFolder = NULL, basenameOutput = NULL,

 #' Plot diagnostic plots for TF-peak connections for a \code{\linkS4class{GRN}} object

+#' 

+#' Due to the number of plots that this functions produces, we currently provide only the option to plot as PDF. This may change in the future.

 #'

 #' @template GRN 

 #' @template outputFolder

 #' @template basenameOutput

 #' @template plotDetails

+#' @param plotPermuted \code{TRUE} or \code{FALSE}. Default  \code{TRUE}. Also produce the diagnostic plots for permuted data?

+#' @param nTFMax \code{NULL} or Integer. Default \code{NULL}. Maximum number of TFs to process. Can be used for testing purposes by setting this to a small number i(.e., 10)

 #' @template forceRerun

 #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

 #' GRN = loadExampleObject()

-#' GRN = plotDiagnosticPlots_TFPeaks(GRN, outputFolder = ".", forceRerun = FALSE)

+#' GRN = plotDiagnosticPlots_TFPeaks(GRN, outputFolder = ".", plotPermuted = FALSE, nTFMax = 2)

 #' @export

 plotDiagnosticPlots_TFPeaks <- function(GRN, 

                                         outputFolder = NULL, 

                                         basenameOutput = NULL, 

                                         plotDetails = FALSE,

+                                        plotPermuted = TRUE,

+                                        nTFMax = NULL,

                                         forceRerun = FALSE) {

   GRN = .addFunctionLogToObject(GRN)

@@ -467,6 +473,8 @@ plotDiagnosticPlots_TFPeaks <- function(GRN,

   checkmate::assert(checkmate::checkNull(outputFolder), checkmate::checkCharacter(outputFolder, min.chars = 1))

   checkmate::assert(checkmate::checkNull(basenameOutput), checkmate::checkCharacter(basenameOutput, len = 1, min.chars = 1, any.missing = FALSE))

   checkmate::assertFlag(plotDetails)

+  checkmate::assertFlag(plotPermuted)

+  checkmate::assert(checkmate::checkNull(nTFMax), checkmate::checkIntegerish(nTFMax))

   checkmate::assertFlag(forceRerun)

   useGCCorrection = GRN@config$parameters$useGCCorrection

@@ -476,6 +484,10 @@ plotDiagnosticPlots_TFPeaks <- function(GRN,

   for (permutationCur in 0:.getMaxPermutation(GRN)) {

+    if (!plotPermuted & permutationCur != 0) {

+      next

+    }

+    

     suffixFile = .getPermutationSuffixStr(permutationCur)

     fileCur = paste0(outputFolder, 

@@ -485,7 +497,9 @@ plotDiagnosticPlots_TFPeaks <- function(GRN,

     if (!file.exists(fileCur) | forceRerun) {

       heightCur = 8* length(GRN@config$TF_peak_connectionTypes)

-      .plot_TF_peak_fdr(GRN, perm = permutationCur, useGCCorrection = useGCCorrection, plotDetails = plotDetails, fileCur, width = 7, height = heightCur) 

+      .plot_TF_peak_fdr(GRN, perm = permutationCur, useGCCorrection = useGCCorrection, 

+                        plotDetails = plotDetails, fileCur, width = 7, height = heightCur,

+                        nPagesMax = nTFMax) 

     }

     fileCur = paste0(outputFolder, .getOutputFileName("plot_TFPeak_fdr_GC"), suffixFile)

@@ -623,20 +637,24 @@ plotDiagnosticPlots_TFPeaks <- function(GRN,

     index = 1

   } 

+  # Dont take all TF, some might be missing.

   connections_TF_peak = GRN@connections$TF_peaks[[as.character(perm)]]$connectionStats

+  allTF = unique(connections_TF_peak$TF.name)

+  nTF = ifelse(is.null(nPagesMax), length(allTF), nPagesMax)

+  futile.logger::flog.info(paste0(" Including a total of ", nTF,  " TF. Preparing plots..."))

-  # TODO: Check difference between TFActivity TFs and expression TFs

-  # Dont take all TF, some might be missing.

-  allTF = unique(connections_TF_peak$TF.name)

-  futile.logger::flog.info(paste0(" Including a total of ", length(allTF),  " TF. Preparing plots..."))

+  # TODO: Check difference between TFActivity TFs and expression TFs

-  pb <- progress::progress_bar$new(total = length(allTF))

+

+  pb <- progress::progress_bar$new(total = nTF)

   levels_pos<-unique(as.character(cut(GRN@config$parameters$internal$stepsFDR, breaks = GRN@config$parameters$internal$stepsFDR, right = FALSE, include.lowest = TRUE )))

   levels_neg<-unique(as.character(cut(GRN@config$parameters$internal$stepsFDR, breaks = rev(GRN@config$parameters$internal$stepsFDR), right = TRUE,  include.lowest = TRUE )))

-  for (i in seq_len(length(allTF))) {

+

+  

+  for (i in seq_len(nTF)) {

     pb$tick()

     TFCur = allTF[i]

@@ -758,11 +776,7 @@ plotDiagnosticPlots_TFPeaks <- function(GRN,

   if (!is.null(file)) {

     futile.logger::flog.info(paste0(" Finished generating plots, start plotting to file ",file, ". This may take a few minutes."))

-    if (!is.null(nPagesMax)) {

-      .printMultipleGraphsPerPage(plots.l[c(seq_len(nPagesMax))], nCol = 1, nRow = 2, pdfFile = file, height = height, width = width)

-    } else {

-      .printMultipleGraphsPerPage(plots.l, nCol = 1, nRow = 2, pdfFile = file, height = height, width = width)

-    }

+    .printMultipleGraphsPerPage(plots.l, nCol = 1, nRow = 2, pdfFile = file, height = height, width = width)

   }

@@ -794,7 +808,8 @@ plotDiagnosticPlots_TFPeaks <- function(GRN,

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

 #' # GRN = loadExampleObject()

-#' # GRN = plotDiagnosticPlots_peakGene(GRN, outputFolder = ".", forceRerun = FALSE)

+#' # types = list(c("protein_coding"))

+#' # GRN = plotDiagnosticPlots_peakGene(GRN, outputFolder=".", gene.types=types, plotAsPDF=FALSE)

 #' @export

 # TODO: implement forceRerun correctly

 plotDiagnosticPlots_peakGene <- function(GRN, 

@@ -1318,7 +1333,7 @@ plotDiagnosticPlots_peakGene <- function(GRN,

           #theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8), strip.background = element_blank(), strip.placement = "outside", axis.title.y = element_blank()) +

           # theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8) , axis.title.y = element_blank()) +

           theme_main +

-          facet_wrap(~ factor(class), nrow = 2, scales = "free_y", strip.position = "left", , labeller = labeller(class=freq_class)) 

+          facet_wrap(~ factor(class), nrow = 2, scales = "free_y", strip.position = "left", labeller = labeller(class=freq_class)) 

         plots_all = ( ((gA3 | gB3 ) + 

@@ -1584,7 +1599,7 @@ plotDiagnosticPlots_peakGene <- function(GRN,

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

 #' GRN = loadExampleObject()

-#' GRN = plot_stats_connectionSummary(GRN, outputFolder = ".", forceRerun = FALSE)

+#' GRN = plot_stats_connectionSummary(GRN, outputFolder = ".", forceRerun = FALSE, plotAsPDF=FALSE)

 #' @export

 #' @importFrom circlize colorRamp2

 plot_stats_connectionSummary <- function(GRN, type = "heatmap", 

@@ -1613,6 +1628,7 @@ plot_stats_connectionSummary <- function(GRN, type = "heatmap",

     }

     .plot_stats_connectionSummaryHeatmap(GRN, file = file, pdf_width = pdf_width, pdf_height = pdf_height, forceRerun = forceRerun) 

+  

   } else if (type ==  "boxplot") {

     if (plotAsPDF) {

@@ -1645,7 +1661,7 @@ plot_stats_connectionSummary <- function(GRN, type = "heatmap",

   futile.logger::flog.info(paste0("Plotting connection summary", dplyr::if_else(is.null(file), "", paste0(" to file ", file))))

-  if ((!is.null(file) & !file.exists(file))| forceRerun) {

+  if ((!is.null(file) && !file.exists(file))| forceRerun) {

     if (nrow(GRN@stats$connections) == 0) {

       message = paste0("Statistics summary missing from object, please run the function generateStatsSummary first")

@@ -1776,7 +1792,7 @@ plot_stats_connectionSummary <- function(GRN, type = "heatmap",

   start = Sys.time()

-  if ((!is.null(file) & !file.exists(file))| forceRerun) {

+  if ((!is.null(file) && !file.exists(file))| forceRerun) {

     futile.logger::flog.info(paste0("Plotting diagnostic plots for network connections", dplyr::if_else(is.null(file), "", paste0(" to file ", file))))

@@ -1915,7 +1931,7 @@ plot_stats_connectionSummary <- function(GRN, type = "heatmap",

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

 #' GRN = loadExampleObject()

-#' GRN = plotGeneralGraphStats(GRN, outputFolder = ".", forceRerun = FALSE)

+#' GRN = plotGeneralGraphStats(GRN, outputFolder = ".", plotAsPDF=FALSE)

 #' @export

 plotGeneralGraphStats <- function(GRN, outputFolder = NULL, basenameOutput = NULL, 

                                   plotAsPDF = TRUE, pdf_width = 12, pdf_height = 12, 

@@ -2081,7 +2097,7 @@ plotGeneralGraphStats <- function(GRN, outputFolder = NULL, basenameOutput = NUL

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

 #' GRN = loadExampleObject()

-#' GRN = plotGeneralEnrichment(GRN, outputFolder = ".", forceRerun = FALSE)

+#' GRN = plotGeneralEnrichment(GRN, outputFolder = ".", plotAsPDF=FALSE)

 #' @export

 plotGeneralEnrichment <- function(GRN, outputFolder = NULL, basenameOutput = NULL, 

                                   ontology = NULL, topn_pvalue = 30, p = 0.05, 

@@ -2260,7 +2276,7 @@ plotGeneralEnrichment <- function(GRN, outputFolder = NULL, basenameOutput = NUL

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

 #' GRN = loadExampleObject()

-#' GRN = plotCommunitiesStats(GRN, outputFolder = ".", forceRerun = FALSE)

+#' GRN = plotCommunitiesStats(GRN, outputFolder = ".", plotAsPDF=FALSE)

 #' @export

 plotCommunitiesStats <- function(GRN, outputFolder = NULL, basenameOutput = NULL, 

                                  display = "byRank", communities = seq_len(10), 

@@ -2430,7 +2446,7 @@ plotCommunitiesStats <- function(GRN, outputFolder = NULL, basenameOutput = NULL

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

 #' GRN = loadExampleObject()

-#' GRN = plotCommunitiesEnrichment(GRN, outputFolder = ".", forceRerun = FALSE)

+#' GRN = plotCommunitiesEnrichment(GRN, outputFolder = ".", plotAsPDF=FALSE)

 #' @export

 #' @import ggplot2

 #' @importFrom grid gpar

@@ -2520,9 +2536,9 @@ plotCommunitiesEnrichment <- function(GRN, outputFolder = NULL, basenameOutput =

     vertexMetadata = as.data.frame(igraph::vertex.attributes(GRN@graph$TF_gene$graph))

     # Get the number of vertexes per community as additional annotation column for the heatmap

     geneCounts = vertexMetadata %>%

-      dplyr::select(name, community) %>%

+      dplyr::select(.data$name, .data$community) %>%

       dplyr::distinct() %>%

-      dplyr::count(community)

+      dplyr::count(.data$community)

     allOntologies = .checkEnrichmentCongruence_general_community(GRN, type = "community")

@@ -2604,16 +2620,19 @@ plotCommunitiesEnrichment <- function(GRN, outputFolder = NULL, basenameOutput =

         tibble::column_to_rownames("Term") %>%

         as.matrix()

-      # Common heatmap parameters for both p1 and p2

+ 

       geneCounts_communities = geneCounts %>%

-        dplyr::filter(community %in% as.character(colnames(matrix.m))) %>%

-        dplyr::slice(match(communities.order[-1], geneCounts$community)) %>%

-        dplyr::pull(n)

-        

+        dplyr::filter(community %in% as.character(colnames(matrix.m)),

+                      community %in% geneCounts$community) %>%

+        dplyr::arrange(dplyr::desc(.data$n))

+      # Sanity check

+      stopifnot(identical(as.character(geneCounts_communities$community), colnames(matrix.m)[-1]))

+        

+      # Common heatmap parameters for both p1 and p2

       top_annotation = ComplexHeatmap::HeatmapAnnotation(

         nGenes = ComplexHeatmap::anno_barplot(

-          x = c(sum(geneCounts$n), geneCounts_communities), 

+          x = c(sum(geneCounts$n), geneCounts_communities$n), 

           border = FALSE,  bar_width = 0.8,  gp = grid::gpar(fill = "#046C9A")),

         annotation_name_gp = grid::gpar(fontsize=9), annotation_name_side = "left", annotation_name_rot = 90)

@@ -2634,7 +2653,7 @@ plotCommunitiesEnrichment <- function(GRN, outputFolder = NULL, basenameOutput =

       # Now focus on the top X only per community

       ID_subset =  GRN@stats$Enrichment$byCommunity[["combined"]][[ontologyCur]] %>% 

         dplyr::group_by(community) %>% 

-        dplyr::arrange(pval) %>% 

+        dplyr::arrange(.data$pval) %>% 

         dplyr::slice(seq_len(nID)) %>%

         dplyr::pull(ID) %>% as.character()

@@ -2741,7 +2760,7 @@ plotCommunitiesEnrichment <- function(GRN, outputFolder = NULL, basenameOutput =

 #' @examples 

 #' # See the Workflow vignette on the GRaNIE website for examples

 #' GRN = loadExampleObject()

-#' GRN = plotTFEnrichment(GRN, n = 5, outputFolder = ".", forceRerun = FALSE)

+#' GRN = plotTFEnrichment(GRN, n = 5, outputFolder = ".", plotAsPDF=FALSE)

 #' @export

 #' @importFrom grid gpar

 plotTFEnrichment <- function(GRN, rankType = "degree", n = NULL, TF.names = NULL,

@@ -2904,7 +2923,7 @@ plotTFEnrichment <- function(GRN, rankType = "degree", n = NULL, TF.names = NULL

       # Make sure the top annotation has the same dimensionality as the resulting matrix

       nodeDegree_TFset_numbers =  nodeDegree_TFset %>%

         dplyr::filter(TF.name %in% colnames(matrix.m)) %>%

-        dplyr::arrange(desc(nodeDegree)) %>%

+        dplyr::arrange(dplyr::desc(nodeDegree)) %>%

         dplyr::pull(nodeDegree)

       top_annotation = ComplexHeatmap::HeatmapAnnotation(

@@ -40,6 +40,6 @@ Run the activator-repressor classification for the TFs for a \code{\linkS4class{

 }

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

-GRN = loadExampleObject()

-GRN = AR_classification_wrapper(GRN, outputFolder = ".", forceRerun = FALSE)

+# GRN = loadExampleObject()

+# GRN = AR_classification_wrapper(GRN, outputFolder = ".", forceRerun = FALSE)

 }

@@ -50,5 +50,6 @@ Add peak-gene connections to a \code{\linkS4class{GRN}} object

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

 GRN = loadExampleObject()

-GRN = addConnections_peak_gene(GRN, promoterRange = 10000, outputFolder = ".")

+types =  list(c("protein_coding"))

+GRN = addConnections_peak_gene(GRN, promoterRange=10000, outputFolder=".", plotGeneTypes=types)

 }

@@ -4,12 +4,12 @@

 \alias{changeOutputDirectory}

 \title{Change the output directory of a GRN object}

 \usage{

-changeOutputDirectory(GRN, outputDirectory)

+changeOutputDirectory(GRN, outputDirectory = ".")

 }

 \arguments{

 \item{GRN}{Object of class \code{\linkS4class{GRN}}}

-\item{outputDirectory}{}

+\item{outputDirectory}{Character. Default \code{.}. New output directory for all output files unless overwritten via the parameter \code{outputFolder}.}

 }

 \value{

 The same \code{\linkS4class{GRN}} object, with the output directory being adjusted accordingly

@@ -18,6 +18,6 @@ The same \code{\linkS4class{GRN}} object, with the output directory being adjust

 Change the output directory of a GRN object

 }

 \examples{

-# GRN = loadExampleObject()

-# GRN = changeOutputDirectory(GRN, ".")

+GRN = loadExampleObject()

+GRN = changeOutputDirectory(GRN, outputDirectory = ".")

 }

@@ -47,6 +47,6 @@ Essentially, this functions calls \code{\link{filterGRNAndConnectGenes}} repeate

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

 GRN = loadExampleObject()

-GRN = generateStatsSummary(GRN, forceRerun = FALSE)

+GRN = generateStatsSummary(GRN, TF_peak.fdr = c(0.01, 0.1), peak_gene.fdr = c(0.01, 0.1))

 }

@@ -22,5 +22,5 @@ Retrieve parameters for previously used function calls and general parameters fo

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

 GRN = loadExampleObject()

-getParameters(GRN, type = "parameter", name = "all")

+params.l = getParameters(GRN, type = "parameter", name = "all")

 }

@@ -55,6 +55,6 @@ A convenience function that calls all network-related functions in one-go, using

 }

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

-GRN = loadExampleObject()

-GRN = performAllNetworkAnalyses(GRN, outputFolder = ".", forceRerun = FALSE)

+# GRN = loadExampleObject()

+# GRN = performAllNetworkAnalyses(GRN, outputFolder = ".", forceRerun = FALSE)

 }

@@ -62,5 +62,5 @@ Similarly to \code{\link{plotGeneralEnrichment}}, the results of the community-b

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

 GRN = loadExampleObject()

-GRN = plotCommunitiesEnrichment(GRN, outputFolder = ".", forceRerun = FALSE)

+GRN = plotCommunitiesEnrichment(GRN, outputFolder = ".", plotAsPDF=FALSE)

 }

@@ -50,7 +50,7 @@ Similarly to the statistics produced by \code{\link{plotGeneralGraphStats}}, sum

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

 GRN = loadExampleObject()

-GRN = plotCommunitiesStats(GRN, outputFolder = ".", forceRerun = FALSE)

+GRN = plotCommunitiesStats(GRN, outputFolder = ".", plotAsPDF=FALSE)

 }

 \seealso{

 \code{\link{plotGeneralGraphStats}}

@@ -9,6 +9,8 @@ plotDiagnosticPlots_TFPeaks(

   outputFolder = NULL,

   basenameOutput = NULL,

   plotDetails = FALSE,

+  plotPermuted = TRUE,

+  nTFMax = NULL,

   forceRerun = FALSE

 )

 }

@@ -21,16 +23,20 @@ plotDiagnosticPlots_TFPeaks(

 \item{plotDetails}{\code{TRUE} or \code{FALSE}. Default \code{FALSE}. Print additional plots that may help for debugging and QC purposes? Note that these plots are currently less documented or not at all.}

+\item{plotPermuted}{\code{TRUE} or \code{FALSE}. Default  \code{TRUE}. Also produce the diagnostic plots for permuted data?}

+

+\item{nTFMax}{\code{NULL} or Integer. Default \code{NULL}. Maximum number of TFs to process. Can be used for testing purposes by setting this to a small number i(.e., 10)}

+

 \item{forceRerun}{\code{TRUE} or \code{FALSE}. Default \code{FALSE}. Force execution, even if the GRN object already contains the result. Overwrites the old results.}

 }

 \value{

 The same \code{\linkS4class{GRN}} object, with added data from this function.

 }

 \description{

-Plot diagnostic plots for TF-peak connections for a \code{\linkS4class{GRN}} object

+Due to the number of plots that this functions produces, we currently provide only the option to plot as PDF. This may change in the future.

 }

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

 GRN = loadExampleObject()

-GRN = plotDiagnosticPlots_TFPeaks(GRN, outputFolder = ".", forceRerun = FALSE)

+GRN = plotDiagnosticPlots_TFPeaks(GRN, outputFolder = ".", plotPermuted = FALSE, nTFMax = 2)

 }

@@ -50,5 +50,6 @@ Plot diagnostic plots for peak-gene connections for a \code{\linkS4class{GRN}} o

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

 # GRN = loadExampleObject()

-# GRN = plotDiagnosticPlots_peakGene(GRN, outputFolder = ".", forceRerun = FALSE)

+# types = list(c("protein_coding"))

+# GRN = plotDiagnosticPlots_peakGene(GRN, outputFolder=".", gene.types=types, plotAsPDF=FALSE)

 }

@@ -50,5 +50,5 @@ This function plots the results of the general enrichment analysis for every spe

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

 GRN = loadExampleObject()

-GRN = plotGeneralEnrichment(GRN, outputFolder = ".", forceRerun = FALSE)

+GRN = plotGeneralEnrichment(GRN, outputFolder = ".", plotAsPDF=FALSE)

 }

@@ -38,7 +38,7 @@ This function generates graphical summaries about the structure and connectivity

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

 GRN = loadExampleObject()

-GRN = plotGeneralGraphStats(GRN, outputFolder = ".", forceRerun = FALSE)

+GRN = plotGeneralGraphStats(GRN, outputFolder = ".", plotAsPDF=FALSE)

 }

 \seealso{

 \code{\link{plotGeneralEnrichment}}

@@ -44,5 +44,5 @@ Produce a PCA plot of the data from a \code{\linkS4class{GRN}} object

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

 GRN = loadExampleObject()

-GRN = plotPCA_all(GRN, topn = 500, outputFolder = ".", forceRerun = FALSE)

+GRN = plotPCA_all(GRN, topn = 500, outputFolder = ".", type = "rna", plotAsPDF=FALSE)

 }

@@ -65,7 +65,7 @@ This function plots the enrichment results. The result consist of a dot plot per

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

 GRN = loadExampleObject()

-GRN = plotTFEnrichment(GRN, n = 5, outputFolder = ".", forceRerun = FALSE)

+GRN = plotTFEnrichment(GRN, n = 5, outputFolder = ".", plotAsPDF=FALSE)

 }

 \seealso{

 \code{\link{calculateTFEnrichment}}

@@ -41,5 +41,5 @@ Plot various network connectivity summaries for a \code{\linkS4class{GRN}} objec

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

 GRN = loadExampleObject()

-GRN = plot_stats_connectionSummary(GRN, outputFolder = ".", forceRerun = FALSE)

+GRN = plot_stats_connectionSummary(GRN, outputFolder = ".", forceRerun = FALSE, plotAsPDF=FALSE)

 }