@@ -201,7 +201,7 @@ setMethod("show",

                 # Community identification (no, yes and how many and how many nodes each)

                 cat(" Communities (TF-gene):\n")

                 df = igraph::vertex.attributes(GRN@graph[["TF_gene"]]$graph)

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

+                if (!is.null(df) & "community" %in% names(df)) {

                     communities = df %>% as.data.frame() %>% dplyr::count(community) %>% dplyr::arrange(desc(n))

                     cat("  Communities, sorted by size (n = Number of nodes): ", paste0(communities$community, " (n=", communities$n, collapse = "), "), ")\n", sep = "")

                 } else {

@@ -2876,11 +2876,11 @@ filterGRNAndConnectGenes <- function(GRN,

   checkmate::assertClass(GRN, "GRN")

   checkmate::assertCharacter(TF_peak.connectionTypes, min.len = 1, any.missing = FALSE)

-  checkmate::assert(checkmate::checkNull(peak_gene.p_raw.threshold), checkmate::checkNumeric(peak_gene.p_raw.threshold, lower = 0, upper = 1, min.len = 1))

+  checkmate::assert(checkmate::checkNull(peak_gene.p_raw.threshold), checkmate::checkNumber(peak_gene.p_raw.threshold, lower = 0, upper = 1))

   checkmate::assertNumeric(peak_gene.r_range, lower = -1, upper = 1, len = 2)

   checkmate::assertCharacter(gene.types, min.len = 1)

   checkmate::assertNumber(TF_peak.fdr.threshold, lower = 0, upper = 1)

-  checkmate::assertNumber(peak_gene.fdr.threshold, lower = 0, upper = 1)

+  checkmate::assert(checkmate::checkNull(peak_gene.fdr.threshold), checkmate::checkNumber(peak_gene.fdr.threshold, lower = 0, upper = 1))

   checkmate::assertSubset(peak_gene.fdr.method, c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none", "IHW"))

   checkmate::assert(checkmate::checkNull(peak_gene.IHW.covariate), checkmate::checkCharacter(peak_gene.IHW.covariate, min.chars = 1, len = 1))

@@ -2895,6 +2895,11 @@ filterGRNAndConnectGenes <- function(GRN,

     .checkAndLogWarningsAndErrors(NULL, message, isWarning = FALSE)

   }

+  if (!is.null(peak_gene.p_raw.threshold) & !is.null(peak_gene.fdr.threshold)) {

+    message = "Both parameters peak_gene.p_raw.threshold and peak_gene.fdr.threshold have been specified, choose only either of them."

+    .checkAndLogWarningsAndErrors(NULL, message, isWarning = FALSE)

+  }

+  

   start = Sys.time()

   if (silent) {

     futile.logger::flog.threshold(futile.logger::WARN)

@@ -424,6 +424,19 @@ GRN_connections.all

 The table contains many columns, and the prefix of each column name indicates the part of the *eGRN* network that the column refers to (e.g., TFs, TF-enhancers, enhancers, enhancer-genes or genes, or TF-gene if the function `add_TF_gene_correlation()` has been run before). Data are stored in a format that minimizes the memory footprint (e.g., each character column is stored as a factor). This table can now be used for any downstream analysis, as it is just a normal data frame.

+## Construct the eGRN graph

+

+For all network-related and visualization-related functions, we need to build a graph out of the filtered connections. For this, we provide a helper function that stores the graph-structure in the object, and it can be invoked as follows:

+

+```{r buildGraph, echo=TRUE, include=TRUE, eval = FALSE, class.output="scroll-200"}

+

+GRN = build_eGRN_graph(GRN, forceRerun = TRUE) 

+

+```

+

+We don't include the output here, but this function runs actually very quickly. 

+

+

 ## Visualize the filtered *eGRN*

 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.

@@ -494,15 +507,6 @@ As this functions needs a few minutes, for the purpose of the vignette, we do no

 First, we have to create a network representation out of the filtered connections, and there are a few options for how the network structure should look like. We here keep the default options and refer to the R help for details (`?build_eGRN_graph`).

-### Construct the eGRN graph

-

-```{r buildGraph, echo=TRUE, include=TRUE, eval = FALSE, class.output="scroll-200"}

-

-GRN = build_eGRN_graph(GRN, forceRerun = TRUE) 

-

-```

-

-We don't include the output here, but this function runs actually very quickly. 

 ### General network statistics