GRaNIEdev: Edge cases for network analyses

src/GRaNIEdev/DESCRIPTION

 Package: GRaNIEdev
 Title: GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using chromatin accessibility and RNA-seq data
-Version: 0.13.10
+Version: 0.13.11
 Encoding: UTF-8
 Authors@R: c(person("Christian", "Arnold", email =
         "christian.arnold@embl.de", role = c("cre","aut")),

src/GRaNIEdev/R/core.R

@@ -4303,6 +4303,8 @@ performAllNetworkAnalyses <- function(GRN, ontology = c("BP", "MF"),
     
     GRN = plotCommunitiesEnrichment(GRN, outputFolder = outputFolder, display = display, communities = communities, forceRerun = forceRerun)
     
+   
+    
     .printExecutionTime(start)
     
     GRN
@@ -4457,24 +4459,34 @@ calculateGeneralEnrichment <- function(GRN, ontology = c("BP", "MF"),
   
   result.list = list()
   
+  
   geneList = factor(as.integer(unique(background) %in% unique(foreground)))
   names(geneList) = unique(background)
   
   for (ontology_cur in ontology){
-    data = suppressMessages(new("topGOdata",
-               ontology = ontology_cur,
-               allGenes = geneList,
-               description = description,
-               nodeSize = 5,
-               annot = topGO::annFUN.org,
-               mapping = mapping, 
-               ID = "ensembl"))
-    
-    result = suppressMessages(topGO::runTest(data, algorithm = algorithm, statistic = statistic))
-    # Dont trim GO terms here, happens later when plotting
-    result.tbl = unique(topGO::GenTable(data, pval = result, orderBy = "pval", numChar = 1000, 
-                                        topNodes = length(topGO::score(result))) )
-    result.tbl$GeneRatio = result.tbl$Significant / length(unique(foreground))
+      
+    if (length(geneList == 0)) {
+        
+        result.tbl = NULL
+            
+    } else {
+        
+        data = suppressMessages(new("topGOdata",
+                                    ontology = ontology_cur,
+                                    allGenes = geneList,
+                                    description = description,
+                                    nodeSize = 5,
+                                    annot = topGO::annFUN.org,
+                                    mapping = mapping, 
+                                    ID = "ensembl"))
+        
+        result = suppressMessages(topGO::runTest(data, algorithm = algorithm, statistic = statistic))
+        # Dont trim GO terms here, happens later when plotting
+        result.tbl = unique(topGO::GenTable(data, pval = result, orderBy = "pval", numChar = 1000, 
+                                            topNodes = length(topGO::score(result))) )
+        result.tbl$GeneRatio = result.tbl$Significant / length(unique(foreground))
+    }
+    
     
     result.list[["results"]][[ontology_cur]] = result.tbl
     result.list[["parameters"]][[ontology_cur]] = stats::setNames(c(statistic, algorithm, backgroundStr), c("statistic", "algorithm", "background"))
@@ -4533,61 +4545,70 @@ calculateCommunitiesStats <- function(GRN, clustering = "louvain", forceRerun =
     futile.logger::flog.info(paste0("Calculating communities for clustering type ", clustering, "..."))
     
     TF_gene.df = .generateGraphTables(GRN)$TF_gene.df
-    graph = igraph::graph_from_data_frame(d = TF_gene.df, directed = FALSE)
     
-    if (clustering == "louvain") {
-      
-      communities_cluster = igraph::cluster_louvain(graph, weights = NA)
-      
-    } else if (clustering == "leading_eigen") {
-      
-      communities_cluster = igraph::cluster_leading_eigen(graph, ...)
-
-    } else if (clustering == "fast_greedy") {
-      
-      communities_cluster = igraph::cluster_fast_greedy(graph, ...)
-      
-    } else if (clustering == "optimal") {
-      
-      communities_cluster = igraph::cluster_optimal(graph, ...)
-      
-    } else if (clustering == "walktrap") {
-      
-      communities_cluster = igraph::cluster_walktrap(graph, ...)
-      
-    }
-    
-    GRN@stats$graph_TF_gene = graph
-    GRN@stats$communityCluster_obj = communities_cluster 
-    
-    communities_count = sort(table(communities_cluster$membership), decreasing = TRUE)
-    
-    communityVertices = tibble::tibble(vertex    = communities_cluster$names,
-                                       community = factor(communities_cluster$membership, levels = names(communities_count)),
-                                       Class     = dplyr::case_when(grepl("^ENS", vertex) ~ "gene",
-                                                                    TRUE ~ "TF")) 
-    
-    GRN@stats$communityVertices = communityVertices
-    
-    
-    communityGraphs = matrix(ncol=3, nrow =0, dimnames = list(c(), c("V1", "V2", "community")))
-    for (communityCur in stats::na.omit(names(communities_count))){ # change this to select communities
-      
-      community_subgraph.df = 
-        igraph::induced_subgraph(graph = graph, 
-                                 vids = communityVertices$vertex[communityVertices$community==communityCur]) %>%
-        igraph::as_long_data_frame() %>% 
-        dplyr::select(c(4,5,3)) %>%
-        dplyr::rename(V1 = names(.)[1], V2 = names(.)[2]) %>%
-        dplyr::mutate(community = communityCur)
-      
-      communityGraphs = rbind(communityGraphs, community_subgraph.df[,c("V1", "V2", "community")])
-      
+    if (nrow(TF_gene.df) == 0) {
+        
+        GRN@stats$communities = NULL
+        GRN@stats$communityVertices = NULL
+        
+    } else {
+        graph = igraph::graph_from_data_frame(d = TF_gene.df, directed = FALSE)
+        
+        if (clustering == "louvain") {
+            
+            communities_cluster = igraph::cluster_louvain(graph, weights = NA)
+            
+        } else if (clustering == "leading_eigen") {
+            
+            communities_cluster = igraph::cluster_leading_eigen(graph, ...)
+            
+        } else if (clustering == "fast_greedy") {
+            
+            communities_cluster = igraph::cluster_fast_greedy(graph, ...)
+            
+        } else if (clustering == "optimal") {
+            
+            communities_cluster = igraph::cluster_optimal(graph, ...)
+            
+        } else if (clustering == "walktrap") {
+            
+            communities_cluster = igraph::cluster_walktrap(graph, ...)
+            
+        }
+        
+        GRN@stats$graph_TF_gene = graph
+        GRN@stats$communityCluster_obj = communities_cluster 
+        
+        communities_count = sort(table(communities_cluster$membership), decreasing = TRUE)
+        
+        communityVertices = tibble::tibble(vertex    = communities_cluster$names,
+                                           community = factor(communities_cluster$membership, levels = names(communities_count)),
+                                           Class     = dplyr::case_when(grepl("^ENS", vertex) ~ "gene",
+                                                                        TRUE ~ "TF")) 
+        
+        GRN@stats$communityVertices = communityVertices
+        
+        
+        communityGraphs = matrix(ncol=3, nrow =0, dimnames = list(c(), c("V1", "V2", "community")))
+        for (communityCur in stats::na.omit(names(communities_count))){ # change this to select communities
+            
+            community_subgraph.df = 
+                igraph::induced_subgraph(graph = graph, 
+                                         vids = communityVertices$vertex[communityVertices$community==communityCur]) %>%
+                igraph::as_long_data_frame() %>% 
+                dplyr::select(c(4,5,3)) %>%
+                dplyr::rename(V1 = names(.)[1], V2 = names(.)[2]) %>%
+                dplyr::mutate(community = communityCur)
+            
+            communityGraphs = rbind(communityGraphs, community_subgraph.df[,c("V1", "V2", "community")])
+            
+        }
+        
+        
+        GRN@stats$communities = communityGraphs %>% dplyr::mutate(community = as.factor(community))
     }
     
     
-    GRN@stats$communities = communityGraphs %>% dplyr::mutate(community = as.factor(community))
-    
   } else {
   
      futile.logger::flog.info(paste0("Data already exists in object, nothing to do"))
@@ -4628,11 +4649,12 @@ calculateCommunitiesEnrichment <- function(GRN,
   checkmate::assertSubset(selection, c("byRank", "byLabel"))
   checkmate::assertFlag(forceRerun)
   
-  if (is.null(GRN@stats$communities)){
-    #message = "Could not find community results in GRN object. Make sure to generate the communities first using the communitiesStats function."
-    message = "Communities have not been previously calculated it. Calculating communities using the default parameters. \nIf you wish to modify those parameters run the communitiesStats function first."
-    .checkAndLogWarningsAndErrors(NULL, message, isWarning = TRUE)
-    GRN = calculateCommunitiesStats(GRN)
+  
+  # If still NULL, no communities were found
+  if (is.null(GRN@stats$communities)) {
+      message = "No communities found, cannot calculate enrichment. Run the functioncalculateCommunitiesStats first. If you did already, it looks like no communities could be identified before"
+      .checkAndLogWarningsAndErrors(NULL, message, isWarning = TRUE)
+      return (GRN)
   }
   
   if (selection == "byLabel"){

src/GRaNIEdev/R/plot.R

@@ -2019,56 +2019,76 @@ plotGeneralGraphStats <- function(GRN, outputFolder = NULL, forceRerun = FALSE)
       ggtitle(paste0("Vertices (n=", sum(totalVerteces$Count), ")"))
     
     geneDist = as.data.frame(table(droplevels(GRN@connections$all.filtered$`0`$gene.type)))
-    gGeneDist = ggplot(geneDist, aes(x= "", y = Freq, fill = Var1)) + geom_bar(stat="identity") +
-      coord_polar("y") +
-      geom_text(aes(label = paste0(round(Freq/sum(Freq) *100, digits = 1 ), "%")),
-                position = position_stack(vjust = 0.5)) +
-      theme_plots +
-      ggtitle(paste0("Genes (n=", sum(geneDist$Freq), ")"))
+    if (nrow(geneDist) == 0) {
+        gGeneDist = ggplot() + annotate("text", x = 4, y = 25, size=5, color = "red", label = "Nothing to plot:\nNo genes found") + 
+            theme_void()
+        
+        
+        message = "No genes found in the GRN object. Make sure the filtered connections contain also genes."
+        .checkAndLogWarningsAndErrors(NULL, message, isWarning = TRUE)
+
+    } else {
+        gGeneDist = ggplot(geneDist, aes(x= "", y = Freq, fill = Var1)) + geom_bar(stat="identity") +
+            coord_polar("y") +
+            geom_text(aes(label = paste0(round(Freq/sum(Freq) *100, digits = 1 ), "%")),
+                      position = position_stack(vjust = 0.5)) +
+            theme_plots +
+            ggtitle(paste0("Genes (n=", sum(geneDist$Freq), ")"))
+    }
+    
     
     totalEdges = as.data.frame(table(TF_peak_gene.df$connectionType))
-    gEdgeDist = ggplot(totalEdges, aes(x="", y=Freq, fill=Var1)) + geom_bar(stat="identity") +
-      coord_polar("y", start=0) +
-      scale_fill_manual(values = c("#BDC367", "#6BB1C1")) +
-      geom_text(aes(label = paste0(round(Freq/sum(Freq) *100, digits = 1 ), "%")),
-                position = position_stack(vjust = 0.5)) +
-      theme_plots +
-      ggtitle(paste0("Edges (n=", sum(totalEdges$Freq), ")"))
+    if (nrow(totalEdges) == 0) {
+        gEdgeDist= ggplot() + annotate("text", x = 4, y = 25, size=5, color = "red", label = "Nothing to plot:\nNo genes found") + 
+            theme_void()
+    } else {
+        gEdgeDist = ggplot(totalEdges, aes(x="", y=Freq, fill=Var1)) + geom_bar(stat="identity") +
+            coord_polar("y", start=0) +
+            scale_fill_manual(values = c("#BDC367", "#6BB1C1")) +
+            geom_text(aes(label = paste0(round(Freq/sum(Freq) *100, digits = 1 ), "%")),
+                      position = position_stack(vjust = 0.5)) +
+            theme_plots +
+            ggtitle(paste0("Edges (n=", sum(totalEdges$Freq), ")"))
+    }
+   
     
     print((gVertexDist + gEdgeDist)/gGeneDist + patchwork::plot_layout(widths = c(2,1), guides = "collect"))
     
     
     # Get degree stats and central verteces in the TF-peak-gene graph
     TF_peak_gene.degree.stats = .getDegreeStats(GRN, TF_peak_gene.df)
-    #degreeDist.tbl = TF_peak_gene.degree.stats$tbl$degrees
-    gDegrees = TF_peak_gene.degree.stats$figures$degreeDist + ggtitle("Distribution of vertex degrees in the filtered GRN")
-    gTopGenes = TF_peak_gene.degree.stats$figures$topGenes + ggtitle("Top degree-central genes in the filtered GRN")
-    gTopTFs = TF_peak_gene.degree.stats$figures$topTFs + ggtitle("Top degree-central TFs in the filtered GRN")
-    
-    eigen_stats = .getEigenCentralVertices(GRN, TF_peak_gene.df)
-    gTopEigenGenes = eigen_stats$topGenes + ggtitle("Top eigenvector-central genes in the filtered GRN")
-    gTopEigenTFs = eigen_stats$topTFs + ggtitle("Top eigenvector-central TFs in the filtered GRN")
-    
-    print(gDegrees)
-    print(gTopGenes/gTopTFs)
-    print(gTopEigenGenes/gTopEigenTFs)
-    
-    # Get degree stats and central verteces in the filtered GRN
-    TF_gene.degree.stats = .getDegreeStats(GRN, TF_gene.df)
-    degreeDist.tbl = TF_gene.degree.stats$tbl$degrees
-    gDegrees = TF_gene.degree.stats$figures$degreeDist + ggtitle("Distribution of Vertex Degrees in the filtered GRN")
-    gTopGenes = TF_gene.degree.stats$figures$topGenes + ggtitle("Top degree-central genes in the filtered GRN")
-    gTopTFs = TF_gene.degree.stats$figures$topTFs + ggtitle("Top degree-central TFs in the filtered GRN")
-    
-    # get top eigenvalue central TFs and genes
-    eigen_stats = .getEigenCentralVertices(GRN, TF_peak_gene.df)
-    gTopEigenGenes = eigen_stats$topGenes + ggtitle("Top eigenvector-central genes in the filtered GRN")
-    gTopEigenTFs = eigen_stats$topTFs + ggtitle("Top eigenvector-central TFs in the filtered GRN")
-    
+    if (nrow(degreeDist.tbl) > 0) {
+        #degreeDist.tbl = TF_peak_gene.degree.stats$tbl$degrees
+        gDegrees = TF_peak_gene.degree.stats$figures$degreeDist + ggtitle("Distribution of vertex degrees in the filtered GRN")
+        gTopGenes = TF_peak_gene.degree.stats$figures$topGenes + ggtitle("Top degree-central genes in the filtered GRN")
+        gTopTFs = TF_peak_gene.degree.stats$figures$topTFs + ggtitle("Top degree-central TFs in the filtered GRN")
+        
+        eigen_stats = .getEigenCentralVertices(GRN, TF_peak_gene.df)
+        gTopEigenGenes = eigen_stats$topGenes + ggtitle("Top eigenvector-central genes in the filtered GRN")
+        gTopEigenTFs = eigen_stats$topTFs + ggtitle("Top eigenvector-central TFs in the filtered GRN")
+        
+        print(gDegrees)
+        print(gTopGenes/gTopTFs)
+        print(gTopEigenGenes/gTopEigenTFs)
+        
+        # Get degree stats and central verteces in the filtered GRN
+        TF_gene.degree.stats = .getDegreeStats(GRN, TF_gene.df)
+        degreeDist.tbl = TF_gene.degree.stats$tbl$degrees
+        gDegrees = TF_gene.degree.stats$figures$degreeDist + ggtitle("Distribution of Vertex Degrees in the filtered GRN")
+        gTopGenes = TF_gene.degree.stats$figures$topGenes + ggtitle("Top degree-central genes in the filtered GRN")
+        gTopTFs = TF_gene.degree.stats$figures$topTFs + ggtitle("Top degree-central TFs in the filtered GRN")
+        
+        # get top eigenvalue central TFs and genes
+        eigen_stats = .getEigenCentralVertices(GRN, TF_peak_gene.df)
+        gTopEigenGenes = eigen_stats$topGenes + ggtitle("Top eigenvector-central genes in the filtered GRN")
+        gTopEigenTFs = eigen_stats$topTFs + ggtitle("Top eigenvector-central TFs in the filtered GRN")
+        
+        
+        print(gDegrees)
+        print(gTopGenes/gTopTFs)
+        print(gTopEigenGenes/gTopEigenTFs)
+    }
     
-    print(gDegrees)
-    print(gTopGenes/gTopTFs)
-    print(gTopEigenGenes/gTopEigenTFs)
     
      grDevices::dev.off()
     
@@ -2216,57 +2236,70 @@ plotCommunitiesStats <- function(GRN, outputFolder = NULL, display = "byRank", c
     
     futile.logger::flog.info(paste0("Plotting community statistics to file ", fileCur))
     
-     grDevices::pdf(fileCur, width = 10)
+    grDevices::pdf(fileCur, width = 10)
     
-    if (!is.null(communities)) {
-      if (display == "byRank"){
-        # Only display communities we have data for, in a reasonable order
-        communitiesDisplay = stats::na.omit(names(table(GRN@stats$communityVertices$community)[communities]))
-      } else{ # byLabel
-        communitiesDisplay = as.character(communities)
-      }
+    if (is.null(GRN@stats$communityVertices)) {
+        
+        g = ggplot() + annotate("text", x = 4, y = 25, size=5, color = "red", 
+                                label = "Nothing to plot:\nNo communities found") + theme_void()
+        plot(g)
+        
+        message = "No communities found in GRN object. Make sure the filtered connections contain also genes."
+        .checkAndLogWarningsAndErrors(NULL, message, isWarning = TRUE)
+        
     } else {
-      communitiesDisplay = unique(GRN@stats$communityVertices$community)
-    }
- 
-    communityVertices = GRN@stats$communityVertices %>%
-      dplyr::filter(community %in% communitiesDisplay)
-    
-    gCommunityVertices = ggplot(communityVertices, aes(x = community, fill = Class)) +
-      geom_bar(position = "stack") +
-      ggtitle("Vertices per community") +
-      xlab("community") +
-      ylab("vertex count") +
-      scale_fill_manual(values = c("#3B9AB2", "#E1AF00"))
-    
-    plot(gCommunityVertices)
-    
-    for (communityCur in as.character(communitiesDisplay)) { # change this to select communities
-      
-      community_subgraph.df = 
-        igraph::induced_subgraph(graph = GRN@stats$graph_TF_gene, 
-                                 vids = communityVertices$vertex[as.character(communityVertices$community)==communityCur]) %>%
-        igraph::as_long_data_frame() %>% 
-        dplyr::select(c(4,5,3)) %>%
-        dplyr::rename(V1 = names(.)[1], V2 = names(.)[2]) %>%
-        dplyr::mutate(community = communityCur)
-      
-      community.degreeStats = .getDegreeStats(GRN, community_subgraph.df)
-      gDegreeDist = community.degreeStats$figures$degreeDist + ggtitle("Degree Distribution")
-      gTopGenes   = community.degreeStats$figures$topGenes + ggtitle("Top degree-central genes")
-      gTopTFs     = community.degreeStats$figures$topTFs + ggtitle("Top degree-central TFs")
-      
-      community.eigenStats = .getEigenCentralVertices(GRN, df= community_subgraph.df)
-      gTopEigenGenes = community.eigenStats$topGenes + ggtitle("Top eigenvector-central genes")
-      gTopEigenTFs   = community.eigenStats$topTFs   + ggtitle("Top eigenvector-central genes")
-      
-      plot(gDegreeDist + (gTopGenes/gTopTFs) + 
-             patchwork::plot_layout(widths = c(2,2)) + 
-             patchwork::plot_annotation(title = paste0("Community ", communityCur)) )
-      
-      plot(gTopEigenGenes/gTopEigenTFs)
+        if (!is.null(communities)) {
+            if (display == "byRank"){
+                # Only display communities we have data for, in a reasonable order
+                communitiesDisplay = stats::na.omit(names(table(GRN@stats$communityVertices$community)[communities]))
+            } else{ # byLabel
+                communitiesDisplay = as.character(communities)
+            }
+        } else {
+            communitiesDisplay = unique(GRN@stats$communityVertices$community)
+        }
+        
+        communityVertices = GRN@stats$communityVertices %>%
+            dplyr::filter(community %in% communitiesDisplay)
+        
+        gCommunityVertices = ggplot(communityVertices, aes(x = community, fill = Class)) +
+            geom_bar(position = "stack") +
+            ggtitle("Vertices per community") +
+            xlab("community") +
+            ylab("vertex count") +
+            scale_fill_manual(values = c("#3B9AB2", "#E1AF00"))
+        
+        plot(gCommunityVertices)
+        
+        for (communityCur in as.character(communitiesDisplay)) { # change this to select communities
+            
+            community_subgraph.df = 
+                igraph::induced_subgraph(graph = GRN@stats$graph_TF_gene, 
+                                         vids = communityVertices$vertex[as.character(communityVertices$community)==communityCur]) %>%
+                igraph::as_long_data_frame() %>% 
+                dplyr::select(c(4,5,3)) %>%
+                dplyr::rename(V1 = names(.)[1], V2 = names(.)[2]) %>%
+                dplyr::mutate(community = communityCur)
+            
+            community.degreeStats = .getDegreeStats(GRN, community_subgraph.df)
+            gDegreeDist = community.degreeStats$figures$degreeDist + ggtitle("Degree Distribution")
+            gTopGenes   = community.degreeStats$figures$topGenes + ggtitle("Top degree-central genes")
+            gTopTFs     = community.degreeStats$figures$topTFs + ggtitle("Top degree-central TFs")
+            
+            community.eigenStats = .getEigenCentralVertices(GRN, df= community_subgraph.df)
+            gTopEigenGenes = community.eigenStats$topGenes + ggtitle("Top eigenvector-central genes")
+            gTopEigenTFs   = community.eigenStats$topTFs   + ggtitle("Top eigenvector-central genes")
+            
+            plot(gDegreeDist + (gTopGenes/gTopTFs) + 
+                     patchwork::plot_layout(widths = c(2,2)) + 
+                     patchwork::plot_annotation(title = paste0("Community ", communityCur)) )
+            
+            plot(gTopEigenGenes/gTopEigenTFs)
+        }
+        
     }
     
+    
      grDevices::dev.off()
     
   } else {
@@ -2313,8 +2346,9 @@ plotCommunitiesEnrichment <- function(GRN, outputFolder = NULL,
   checkmate::assert(checkmate::testNull(outputFolder), checkmate::testDirectoryExists(outputFolder))
   
   if (is.null(GRN@stats$communities)){
-    message = "No communities have been previously generated. TODO better message."
-    .checkAndLogWarningsAndErrors(NULL, message, isWarning = FALSE)
+    message = "No communities have been previously generated or none were found. make sure to run the function calculateCommunitiesEnrichment first."
+    .checkAndLogWarningsAndErrors(NULL, message, isWarning = TRUE)
+    return(GRN)
   }
   
   outputFolder = .checkOutputFolder(GRN, outputFolder)

src/GRaNIEdev/docs/404.html

@@ -6,14 +6,14 @@
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-<title>Page not found (404) • GRNdev</title>
+<title>Page not found (404) • GRaNIEdev</title>
 
 
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@@ -54,16 +54,6 @@
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@@ -80,31 +70,34 @@
         <span class="icon-bar"></span>
       </button>
       <span class="navbar-brand">
-        <a class="navbar-link" href="index.html">GRNdev</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.13.4</span>
+        <a class="navbar-link" href="index.html">GRaNIEdev</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.13.10</span>
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       <ul class="nav navbar-nav">
         <li>
-  <a href="index.html"></a>
+  <a href="index.html">
+    <span class="fas fa-home fa-lg"></span>
+     
+  </a>
 </li>
 <li>
-  <a href="articles/quickStart.html">Getting Started</a>
+  <a href="reference/index.html">Reference</a>
 </li>
 <li class="dropdown">
   <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Vignettes
+    Articles
      
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
     <li>
-      <a href="articles/quickStart.html">Getting Started</a>
+      <a href="articles/Introduction.html">Introduction and Methodological Details</a>
     </li>
     <li>
-      <a href="articles/Introduction.html">Introduction</a>
+      <a href="articles/quickStart.html">Get Started with the *GRaNIE* packages from the Zaugg Lab</a>
     </li>
     <li>
       <a href="articles/workflow.html">Workflow example</a>
@@ -112,10 +105,7 @@
   </ul>
 </li>
 <li>
-  <a href="reference/index.html">Reference</a>
-</li>
-<li>
-  <a href="news/index.html">Changelog &amp; News</a>
+  <a href="news/index.html">Changelog</a>
 </li>
       </ul>
       <ul class="nav navbar-nav navbar-right">
@@ -152,7 +142,7 @@ Content not found. Please use links in the navbar.
 
       <footer>
       <div class="copyright">
-  <p>Developed by Christian Arnold, Judith Zaugg.</p>
+  <p>Developed by Christian Arnold, Rim Moussa.</p>
 </div>
 
 <div class="pkgdown">

src/GRaNIEdev/docs/articles/index.html

@@ -6,14 +6,14 @@
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-<title>Articles • GRNdev</title>
+<title>Articles • GRaNIEdev</title>
 
 
 <!-- jquery -->
 <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
 <!-- Bootstrap -->
-<link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/flatly/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous" />
 
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" />
 
 <script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script>
 
@@ -54,16 +54,6 @@
 <![endif]-->