@@ -3000,7 +3000,7 @@ addConnections_peak_gene <- function(GRN, overlapTypeGene = "TSS", corMethod = "

 #' @param filterPeaks Character vector. Default \code{NULL}. Vector of peak IDs (as named in the GRN object) to retain. All peaks not listed will be filtered out.

 #' @param TF_peak_FDR_selectViaCorBins \code{TRUE} or \code{FALSE}.  Default \code{FALSE}. Use a modified procedure for selecting TF-peak links that is based on the user-specified FDR but that retains also links that may have a higher FDR but a more extreme correlation.

 #' @param silent \code{TRUE} or \code{FALSE}.  Default \code{FALSE}. Print progress messages and filter statistics.

-#' @param resetGraphAndStoreInternally \code{TRUE} or \code{FALSE}.  Default \code{TRUE}. If set to \code{TRUE}, the stored eGRN graph slot graph) is reset due to the potentially changed connections that

+#' @param resetGraphAndStoreInternally \code{TRUE} or \code{FALSE}.  Default \code{TRUE}. If set to \code{TRUE}, the stored eGRN graph (slot \code{graph}) is reset due to the potentially changed connections that

 #' would otherwise cause conflicts in the information stored in the object. Also, a GRN object is returned. If set to \code{FALSE}, only the new filtered connections are returned and the object is not altered.

 #' @param filterLoops  \code{TRUE} or \code{FALSE}. Default \code{TRUE}. If a TF regulates itself (i.e., the TF and the gene are the same entity), should such loops be filtered from the GRN?

 #' @template outputFolder

@@ -3619,7 +3619,9 @@ filterGRNAndConnectGenes <- function(GRN,

 }

-#' Add TF-gene correlations to a \code{\linkS4class{GRN}} object. The information is currently stored in \code{GRN@connections$TF_genes.filtered}. Note that raw p-values are not adjusted.

+#' Add TF-gene correlations to a \code{\linkS4class{GRN}} object. 

+#' 

+#' The information is currently stored in \code{GRN@connections$TF_genes.filtered}. Note that raw p-values are not adjusted.

 #' 

 #' @export

 #' @template GRN

@@ -343,7 +343,8 @@ performAllNetworkAnalyses <- function(GRN, ontology = c("GO_BP", "GO_MF"),

 #' and \emph{Reactome Pathways}, respectively. \code{GO} ontologies require the \code{topGO}, 

 #' \code{"KEGG"} the \code{clusterProfiler}, \code{"DO"} the \code{DOSE}, and \code{"Reactome"} the \code{ReactomePA} packages, respectively.

 #' As they are listed under \code{Suggests}, they may not yet be installed, and the function will throw an error if they are missing.

-#' @param algorithm Character. Default \code{"weight01"}. One of: \code{"classic"}, \code{"elim"}, \code{"weight"}, \code{"weight01"}, \code{"lea"}, \code{"parentchild"}. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), ignored otherwise. Name of the algorithm that handles the GO graph structures. Valid inputs are those supported by the \code{topGO} library.

+#' @param algorithm Character. Default \code{"weight01"}. One of: \code{"classic"}, \code{"elim"}, \code{"weight"}, \code{"weight01"}, \code{"lea"}, \code{"parentchild"}. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), ignored otherwise. Name of the algorithm that handles the GO graph structures. Valid inputs are those supported by the \code{topGO} library. 

+#' For general information about the algorithms, see \url{https://academic.oup.com/bioinformatics/article/22/13/1600/193669}. \code{weight01} is a mixture between the \code{elim} and the \code{weight} algorithms.

 #' @param statistic Character. Default \code{"fisher"}. One of: \code{"fisher"}, \code{"ks"}, \code{"t"}, \code{"globaltest"}, \code{"sum"}, \code{"ks.ties"}. Statistical test to be used. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), and valid inputs are those supported by the topGO library, ignored otherwise. For the other ontologies the test statistic is always Fisher. 

 #' @param background Character. Default \code{"neighborhood"}. One of: \code{"all_annotated"}, \code{"all_RNA"}, \code{"all_RNA_filtered"}, \code{"neighborhood"}. Set of genes to be used to construct the background for the enrichment analysis. This can either be all annotated genes in the reference genome (\code{all_annotated}), all genes from the provided RNA data (\code{all_RNA}), all genes from the provided RNA data excluding those marked as filtered after executing \code{filterData} (\code{all_RNA_filtered}), or all the genes that are within the neighborhood of any peak (before applying any filters except for the user-defined \code{promoterRange} value in \code{addConnections_peak_gene}) (\code{neighborhood}).

 #' @param background_geneTypes Character vector of gene types that should be considered for the background. Default \code{"all"}. 

@@ -592,6 +593,8 @@ calculateGeneralEnrichment <- function(GRN, ontology = c("GO_BP", "GO_MF"),

   if (ontology %in% c("GO_BP","GO_MF","GO_CC")){

+      # https://support.bioconductor.org/p/9141171/

+      

       # go_enrichment =  

       #     clusterProfiler::enrichGO(

       #         gene = foreground_entrez,

@@ -604,6 +607,10 @@ calculateGeneralEnrichment <- function(GRN, ontology = c("GO_BP", "GO_MF"),

       #         minGSSize = minGSSize,

       #         maxGSSize = maxGSSize,

       #         pAdjustMethod = pAdjustMethod)

+      

+      # go.res.new = .createEnichmentTable(go_enrichment)

+      

+      # The need of p-value adjustment: https://bioconductor.org/packages/devel/bioc/vignettes/topGO/inst/doc/topGO.pdf

     go_enrichment = suppressMessages(new("topGOdata",

                                          ontology = gsub("GO_", "", ontology),

@@ -3322,7 +3322,7 @@ plotTFEnrichment <- function(GRN, rankType = "degree", n = NULL, TF.names = NULL

 #' @template pdf_width

 #' @template pdf_height

 #' @param title \code{NULL} or Character. Default \code{NULL}. Title to be assigned to the plot.

-#' @param maxRowsToPlot 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.

+#' @param maxEdgesToPlot 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.

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

 #' @param graph Character. Default \code{TF-gene}. One of: \code{TF-gene}, \code{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.

 #' @param colorby Character. Default \code{type}. Either \code{type} or \code{community}. Color the vertices by either type (TF/peak/gene) or community. See \code{\link{calculateCommunitiesStats}}

@@ -3336,11 +3336,11 @@ plotTFEnrichment <- function(GRN, rankType = "degree", n = NULL, TF.names = NULL

 #' @seealso \code{\link{build_eGRN_graph}}

 #' @examples

 #' GRN = loadExampleObject()

-#' GRN = visualizeGRN(GRN, maxRowsToPlot = 700, graph = "TF-gene", colorby = "type")

+#' GRN = visualizeGRN(GRN, maxEdgesToPlot = 700, graph = "TF-gene", colorby = "type")

 #' @return The same \code{\linkS4class{GRN}} object, without modifications.

 #' @export

 visualizeGRN <- function(GRN, outputFolder = NULL,  basenameOutput = NULL, plotAsPDF = TRUE, pdf_width = 12, pdf_height = 12,

-                         title = NULL, maxRowsToPlot = 500, nCommunitiesMax = 8, graph = "TF-gene" , colorby = "type", layout = "fr",

+                         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

@@ -3356,7 +3356,7 @@ visualizeGRN <- function(GRN, outputFolder = NULL,  basenameOutput = NULL, plotA

     checkmate::assertFlag(plotAsPDF)

     checkmate::assertNumeric(pdf_width, lower = 5, upper = 99)

     checkmate::assertNumeric(pdf_height, lower = 5, upper = 99)

-    checkmate::assertIntegerish(maxRowsToPlot, lower = 1)

+    checkmate::assertIntegerish(maxEdgesToPlot, lower = 1)

     checkmate::assertIntegerish(nCommunitiesMax,lower = 1)

     checkmate::assertChoice(graph, c("TF-gene", "TF-peak-gene"))

     checkmate::assertChoice(colorby, c("type", "community"))

@@ -3406,6 +3406,7 @@ visualizeGRN <- function(GRN, outputFolder = NULL,  basenameOutput = NULL, plotA

         grn.merged = GRN@graph$TF_peak_gene$table %>%

             dplyr::rename(TF.name = .data$V1_name) 

+        

         grn.merged$V1[!is.na(grn.merged$TF.name)] = as.character(grn.merged$TF.name[!is.na(grn.merged$TF.name)]) # replace TF ensembl with TF name

         edges_final = grn.merged %>%

@@ -3416,22 +3417,22 @@ visualizeGRN <- function(GRN, outputFolder = NULL,  basenameOutput = NULL, plotA

     }

     edges_final = edges_final %>%

-        dplyr::mutate(weight_transformed = dplyr::case_when(weight < 0.2 ~ 1,

-                                                            weight < 0.4 ~ 1.5,

-                                                            weight < 0.6 ~ 2,

-                                                            weight < 0.8 ~ 2.5,

-                                                            TRUE ~ 3),

+        dplyr::mutate(weight_transformed = dplyr::case_when(abs(weight) < 0.2 ~ 0.2,

+                                                            abs(weight) < 0.4 ~ 0.3,

+                                                            abs(weight) < 0.6 ~ 0.4,

+                                                            abs(weight) < 0.8 ~ 0.5,

+                                                            TRUE ~ 0.6),

                       R_direction = dplyr::case_when(R < 0 ~ "neg", TRUE ~ "pos"),

-                      color       = dplyr::case_when(R < 0 ~ "blue", TRUE ~ "grey")) %>%

+                      color       = dplyr::case_when(R < 0 ~ "gray90", TRUE ~ "gray50")) %>%

         dplyr::select(.data$from, .data$to, .data$weight, .data$R, .data$linetype, .data$weight_transformed, .data$R_direction, .data$color)

     nRows = nrow(edges_final)

-    futile.logger::flog.info(paste0("Number of rows: ",nRows))

-    if (maxRowsToPlot > 500 & nRows > 500) {

-        futile.logger::flog.info(paste0("Plotting many connections takes a lot of time and memory"))

+    futile.logger::flog.info(paste0("Number of edges for the ", graph, " eGRN graph: ",nRows))

+    if (maxEdgesToPlot > 500 & nRows > 500) {

+        futile.logger::flog.info(paste0("Plotting many connections may need a lot of time and memory"))

     }

@@ -3443,10 +3444,10 @@ visualizeGRN <- function(GRN, outputFolder = NULL,  basenameOutput = NULL, plotA

         futile.logger::flog.info(paste0("Plotting GRN network"))

     }

-    if (nRows > maxRowsToPlot) { 

-        futile.logger::flog.info(paste0("Number of rows to plot (", nRows, ") exceeds limit of the maxRowsToPlot parameter. Plotting only empty page"))

+    if (nRows > maxEdgesToPlot) { 

+        futile.logger::flog.info(paste0("Number of edges to plot (", nRows, ") exceeds limit of the maxEdgesToPlot parameter. Plotting only empty page"))

         plot(c(0, 1), c(0, 1), ann = FALSE, bty = 'n', type = 'n', xaxt = 'n', yaxt = 'n', main = title)

-        message = paste0(title, "\n\nPlotting omitted.\n\nThe number of rows in the GRN (", nRows, ") exceeds the maximum of ", maxRowsToPlot, ".\nSee the maxRowsToPlot parameter to increase the limit")

+        message = paste0(title, "\n\nPlotting omitted.\n\nThe number of rows in the GRN (", nRows, ") exceeds the maximum of ", maxEdgesToPlot, ".\nSee the maxEdgesToPlot parameter to increase the limit")

         text(x = 0.5, y = 0.5, message, cex = 1.6, col = "red")

         if (plotAsPDF) {

@@ -3525,8 +3526,14 @@ visualizeGRN <- function(GRN, outputFolder = NULL,  basenameOutput = NULL, plotA

         ## VERTICES ##

-        shape_vertex = c("square","circle", "circle")

-        names(shape_vertex) = names(colors_categories.l)

+        if (graph == "TF-peak-gene"){

+            shape_vertex = c("square","circle", "circle")

+            names(shape_vertex) = names(colors_categories.l)

+        } else {

+            shape_vertex = c("square","circle")

+            names(shape_vertex) = names(colors_categories.l)

+        }

+        

         vertices = tibble::tribble(~id,

@@ -3778,7 +3785,7 @@ visualizeGRN <- function(GRN, outputFolder = NULL,  basenameOutput = NULL, plotA

         # TODO: E(net)$lty = edges_final$linetype

         # TODO: E(net)$width <- 1+E(net)$weight/12

         #igraph::E(net)$width <- 1+igraph::E(net)$weight/12

-        igraph::E(net)$width <- igraph::E(net)$weight

+        igraph::E(net)$width <- igraph::E(net)$weight_transformed

         #igraph::E(net)$weight <- edges_final$weight_transformed # too block-y for large networks. stick to givren weight.

@@ -3882,7 +3889,8 @@ visualizeGRN <- function(GRN, outputFolder = NULL,  basenameOutput = NULL, plotA

             vertex.size= igraph::V(net)$vertex.size,

             vertex.color=igraph::V(net)$vertex.color,

             edge.color = igraph::E(net)$color,

-            edge.width = igraph::E(net)$weight,

+            edge.width = igraph::E(net)$weight_transformed,

+            #edge.width = 0.5,

             vertex.label=igraph::V(net)$label,

             vertex.label.font=1, 

             vertex.label.cex = vertexLabel_cex, 

@@ -2,7 +2,7 @@

 % Please edit documentation in R/core.R

 \name{add_TF_gene_correlation}

 \alias{add_TF_gene_correlation}

-\title{Add TF-gene correlations to a \code{\linkS4class{GRN}} object. The information is currently stored in \code{GRN@connections$TF_genes.filtered}. Note that raw p-values are not adjusted.}

+\title{Add TF-gene correlations to a \code{\linkS4class{GRN}} object.}

 \usage{

 add_TF_gene_correlation(

   GRN,

@@ -30,7 +30,7 @@ A value >1 requires the \code{BiocParallel} package (as it is listed under \code

 An updated \code{\linkS4class{GRN}} object, with additional information added from this function.

 }

 \description{

-Add TF-gene correlations to a \code{\linkS4class{GRN}} object. The information is currently stored in \code{GRN@connections$TF_genes.filtered}. Note that raw p-values are not adjusted.

+The information is currently stored in \code{GRN@connections$TF_genes.filtered}. Note that raw p-values are not adjusted.

 }

 \examples{

 # See the Workflow vignette on the GRaNIE website for examples

@@ -27,7 +27,8 @@ and \emph{Reactome Pathways}, respectively. \code{GO} ontologies require the \co

 \code{"KEGG"} the \code{clusterProfiler}, \code{"DO"} the \code{DOSE}, and \code{"Reactome"} the \code{ReactomePA} packages, respectively.

 As they are listed under \code{Suggests}, they may not yet be installed, and the function will throw an error if they are missing.}

-\item{algorithm}{Character. Default \code{"weight01"}. One of: \code{"classic"}, \code{"elim"}, \code{"weight"}, \code{"weight01"}, \code{"lea"}, \code{"parentchild"}. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), ignored otherwise. Name of the algorithm that handles the GO graph structures. Valid inputs are those supported by the \code{topGO} library.}

+\item{algorithm}{Character. Default \code{"weight01"}. One of: \code{"classic"}, \code{"elim"}, \code{"weight"}, \code{"weight01"}, \code{"lea"}, \code{"parentchild"}. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), ignored otherwise. Name of the algorithm that handles the GO graph structures. Valid inputs are those supported by the \code{topGO} library. 

+For general information about the algorithms, see \url{https://academic.oup.com/bioinformatics/article/22/13/1600/193669}. \code{weight01} is a mixture between the \code{elim} and the \code{weight} algorithms.}

 \item{statistic}{Character. Default \code{"fisher"}. One of: \code{"fisher"}, \code{"ks"}, \code{"t"}, \code{"globaltest"}, \code{"sum"}, \code{"ks.ties"}. Statistical test to be used. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), and valid inputs are those supported by the topGO library, ignored otherwise. For the other ontologies the test statistic is always Fisher.}

@@ -25,7 +25,8 @@ and \emph{Reactome Pathways}, respectively. \code{GO} ontologies require the \co

 \code{"KEGG"} the \code{clusterProfiler}, \code{"DO"} the \code{DOSE}, and \code{"Reactome"} the \code{ReactomePA} packages, respectively.

 As they are listed under \code{Suggests}, they may not yet be installed, and the function will throw an error if they are missing.}

-\item{algorithm}{Character. Default \code{"weight01"}. One of: \code{"classic"}, \code{"elim"}, \code{"weight"}, \code{"weight01"}, \code{"lea"}, \code{"parentchild"}. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), ignored otherwise. Name of the algorithm that handles the GO graph structures. Valid inputs are those supported by the \code{topGO} library.}

+\item{algorithm}{Character. Default \code{"weight01"}. One of: \code{"classic"}, \code{"elim"}, \code{"weight"}, \code{"weight01"}, \code{"lea"}, \code{"parentchild"}. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), ignored otherwise. Name of the algorithm that handles the GO graph structures. Valid inputs are those supported by the \code{topGO} library. 

+For general information about the algorithms, see \url{https://academic.oup.com/bioinformatics/article/22/13/1600/193669}. \code{weight01} is a mixture between the \code{elim} and the \code{weight} algorithms.}

 \item{statistic}{Character. Default \code{"fisher"}. One of: \code{"fisher"}, \code{"ks"}, \code{"t"}, \code{"globaltest"}, \code{"sum"}, \code{"ks.ties"}. Statistical test to be used. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), and valid inputs are those supported by the topGO library, ignored otherwise. For the other ontologies the test statistic is always Fisher.}

@@ -34,7 +34,8 @@ and \emph{Reactome Pathways}, respectively. \code{GO} ontologies require the \co

 \code{"KEGG"} the \code{clusterProfiler}, \code{"DO"} the \code{DOSE}, and \code{"Reactome"} the \code{ReactomePA} packages, respectively.

 As they are listed under \code{Suggests}, they may not yet be installed, and the function will throw an error if they are missing.}

-\item{algorithm}{Character. Default \code{"weight01"}. One of: \code{"classic"}, \code{"elim"}, \code{"weight"}, \code{"weight01"}, \code{"lea"}, \code{"parentchild"}. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), ignored otherwise. Name of the algorithm that handles the GO graph structures. Valid inputs are those supported by the \code{topGO} library.}

+\item{algorithm}{Character. Default \code{"weight01"}. One of: \code{"classic"}, \code{"elim"}, \code{"weight"}, \code{"weight01"}, \code{"lea"}, \code{"parentchild"}. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), ignored otherwise. Name of the algorithm that handles the GO graph structures. Valid inputs are those supported by the \code{topGO} library. 

+For general information about the algorithms, see \url{https://academic.oup.com/bioinformatics/article/22/13/1600/193669}. \code{weight01} is a mixture between the \code{elim} and the \code{weight} algorithms.}

 \item{statistic}{Character. Default \code{"fisher"}. One of: \code{"fisher"}, \code{"ks"}, \code{"t"}, \code{"globaltest"}, \code{"sum"}, \code{"ks.ties"}. Statistical test to be used. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), and valid inputs are those supported by the topGO library, ignored otherwise. For the other ontologies the test statistic is always Fisher.}

@@ -78,7 +78,7 @@ keeping backwards compatibility with \code{\linkS4class{GRN}} objects.}

 \item{outputFolder}{Character or \code{NULL}. Default \code{NULL}. If set to \code{NULL}, the default output folder as specified when initiating the object in \code{link{initializeGRN}} will be used. Otherwise, all output from this function will be put into the specified folder. We recommend specifying an absolute path.}

-\item{resetGraphAndStoreInternally}{\code{TRUE} or \code{FALSE}.  Default \code{TRUE}. If set to \code{TRUE}, the stored eGRN graph slot graph) is reset due to the potentially changed connections that

+\item{resetGraphAndStoreInternally}{\code{TRUE} or \code{FALSE}.  Default \code{TRUE}. If set to \code{TRUE}, the stored eGRN graph (slot \code{graph}) is reset due to the potentially changed connections that

 would otherwise cause conflicts in the information stored in the object. Also, a GRN object is returned. If set to \code{FALSE}, only the new filtered connections are returned and the object is not altered.}

 \item{silent}{\code{TRUE} or \code{FALSE}.  Default \code{FALSE}. Print progress messages and filter statistics.}

@@ -31,7 +31,8 @@ and \emph{Reactome Pathways}, respectively. \code{GO} ontologies require the \co

 \code{"KEGG"} the \code{clusterProfiler}, \code{"DO"} the \code{DOSE}, and \code{"Reactome"} the \code{ReactomePA} packages, respectively.

 As they are listed under \code{Suggests}, they may not yet be installed, and the function will throw an error if they are missing.}

-\item{algorithm}{Character. Default \code{"weight01"}. One of: \code{"classic"}, \code{"elim"}, \code{"weight"}, \code{"weight01"}, \code{"lea"}, \code{"parentchild"}. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), ignored otherwise. Name of the algorithm that handles the GO graph structures. Valid inputs are those supported by the \code{topGO} library.}

+\item{algorithm}{Character. Default \code{"weight01"}. One of: \code{"classic"}, \code{"elim"}, \code{"weight"}, \code{"weight01"}, \code{"lea"}, \code{"parentchild"}. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), ignored otherwise. Name of the algorithm that handles the GO graph structures. Valid inputs are those supported by the \code{topGO} library. 

+For general information about the algorithms, see \url{https://academic.oup.com/bioinformatics/article/22/13/1600/193669}. \code{weight01} is a mixture between the \code{elim} and the \code{weight} algorithms.}

 \item{statistic}{Character. Default \code{"fisher"}. One of: \code{"fisher"}, \code{"ks"}, \code{"t"}, \code{"globaltest"}, \code{"sum"}, \code{"ks.ties"}. Statistical test to be used. Only relevant if ontology is GO related (GO_BP, GO_MF, GO_CC), and valid inputs are those supported by the topGO library, ignored otherwise. For the other ontologies the test statistic is always Fisher.}

@@ -12,7 +12,7 @@ visualizeGRN(

   pdf_width = 12,

   pdf_height = 12,

   title = NULL,

-  maxRowsToPlot = 500,

+  maxEdgesToPlot = 500,

   nCommunitiesMax = 8,

   graph = "TF-gene",

   colorby = "type",

@@ -40,7 +40,7 @@ visualizeGRN(

 \item{title}{\code{NULL} or Character. Default \code{NULL}. Title to be assigned to the plot.}

-\item{maxRowsToPlot}{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.}

+\item{maxEdgesToPlot}{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.}

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

@@ -70,7 +70,7 @@ This function can visualize a filtered eGRN in a very flexible manner and requir

 }

 \examples{

 GRN = loadExampleObject()

-GRN = visualizeGRN(GRN, maxRowsToPlot = 700, graph = "TF-gene", colorby = "type")

+GRN = visualizeGRN(GRN, maxEdgesToPlot = 700, graph = "TF-gene", colorby = "type")

 }

 \seealso{

 \code{\link{build_eGRN_graph}}

@@ -490,7 +490,7 @@ As you can see, some details about the TF-peak-gene and TF-gene are shown as out

 The `GRaNIE` package also offers a function to visualize a filtered *eGRN* network! It is very easy to invoke, but provides many options to customize the output and the way the graph is drawn. We recommend to explore the options in the R help (`?getGRNConnections`), and here just run the default visualization.

-```{r visualizeGRN, echo=TRUE, include=TRUE, eval = TRUE, fig.cap="<i>eGRN example visualization</i>", fig.height = 10, class.output="scroll-200"}

+```{r visualizeGRN, echo=TRUE, include=TRUE, eval = TRUE, fig.cap="<i>eGRN example visualization</i>", fig.height = 9, class.output="scroll-200"}

 GRN = visualizeGRN(GRN, plotAsPDF = FALSE)

 ```