Making suggested revisions

app.R

@@ -55,24 +55,34 @@ celltypeList <- c("Astrocytes")
 
 ###################################################
 
-ui <- navbarPage("Visualize NSCI scRNAseq Data", theme = shinytheme("slate"),
+ui <- navbarPage(title=" ",theme = shinytheme("slate"), windowTitle="AlzApp: Visualize FTD scRNAseq Data",
 
                  #shinythemes::themeSelector(),
 
                  tabPanel("Overall Dataset",
+
+                          # Logos
+                          div(
+                              img(src="IDEA_Logo_WHITE.png",align="right",width="200px"),
+                              img(src="NSCI_Full_Logo.png",align="left",width="150px")
+                          ),
+
+
                           # Application title
-                          titlePanel("Tool for Exploration and Visualization of Frontotemporal Dementia scRNAseq Data"),
-                          tags$div(class="header", checked=NA,
-                                   tags$h6("This application draws on a large dataset of single-cell RNA Seq data made available by Bowles et al. with the Neural Stem Cell Institute, as described in the 2021 paper"),
-                                   tags$a(href="https://doi.org/10.1016/j.cell.2021.07.003", "ELAVL4, splicing, and glutamatergic dysfunction
+                          fixedRow(h2("Tool for Exploring scRNAseq Data from Frontotemporal Dementia Organoids", align="center"),
+                                   div(align="center",
+                                       h6("This application exposes the large dataset of single-cell RNA Sequencing data from dementia-affected brain organoids
+                                described in the paper"),
+                                       a(href="https://doi.org/10.1016/j.cell.2021.07.003", "ELAVL4, splicing, and glutamatergic dysfunction
                                         precede neuron loss in MAPT mutation cerebral
-                                        organoids."),
-                                   tags$h6("Use the \"Gene Explorer\" and \"Cell Explorer\" top-level views to visualize dataset trends by individual gene or cell type."),
-                                   tags$hr()
+                                        organoids (Bowles et al. 2021)."),
+                                       h6("Use the \"Gene Explorer\" and \"Cell Explorer\" top-level views 
+                               to visualize dataset trends by individual gene or cell type.")),
+                                   hr()
                           ),
                           fixedRow(
                               splitLayout(cellWidths = c("50%", "50%"),
-                                          cellArgs = list(style = "padding: 15px"),
+                                          cellArgs = list(style = "text-align:center;padding:15px"),
                                           tagList(tags$p("Dimensional reduction plot of the full dataset of ~370,000 single cells, colored by neuronal cell type"), 
                                                   plotOutput("CellFeaturePlot",height="450px")),
 
@@ -110,7 +120,7 @@ ui <- navbarPage("Visualize NSCI scRNAseq Data", theme = shinytheme("slate"),
                                   ),
 
                                   tabPanel(title = "Time Trends - Expression Distributions",
-                                           fixedRow(tags$p("Expression distributions over time, by cell type and variant"),
+                                           fixedRow(tags$p("Expression distributions over time, by variant"),
                                                     plotOutput("GeneVlnPlots",height="600px",width="70%"),align="center")
                                   )
 
@@ -130,17 +140,19 @@ ui <- navbarPage("Visualize NSCI scRNAseq Data", theme = shinytheme("slate"),
                                                 plotOutput("CellTypeExplorerPlots",height="600px"),align="center")
                               ),
                               tabPanel(title= "Cell Type Marker Genes",
-                                       tags$h5("Left, marker genes distinguishing this cell type from all other cell types represented in the dataset, 
-                                                       as ranked by average log2 fold change. Right, REVIGO semantic summarization of the top 100 enriched GO terms for the genes differentially expressed in this cell type."),
+
+                                       HTML("<p>Left, marker genes distinguishing this cell type from all other cell types represented in the dataset, 
+                                                       as ranked by average log2 fold change. Right, <a href='https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0021800'>REVIGO</a> semantic summarization of GO terms enriched 
+                                                    in the top 100 cell type marker genes.</p>"),
                                        tags$em("Only Astrocyte statistics available currently- data for more cell types coming soon!"),
                                        tags$hr(),
                                        sidebarLayout(
                                            sidebarPanel(
                                                selectInput(inputId = "celltype",
-                                                           label = "Cell type",
+                                                           label = "Select a cell type:",
                                                            choices = celltypeList),
                                                dataTableOutput("celltypeTable"),
-                                               style = "padding: 10px;"
+                                               style = "padding: 25px;"
                                            ),
 
                                            mainPanel(
@@ -150,18 +162,22 @@ ui <- navbarPage("Visualize NSCI scRNAseq Data", theme = shinytheme("slate"),
                                                                             plotOutput("REVIGOtree",height="450px")),
 
                                                                     tagList(tags$p("Semantic distances between enriched GO terms"), 
-                                                                            plotOutput("REVIGOscatter",height="450px"))
+                                                                            plotOutput("REVIGOscatter",height="450px"),)
                                                )
                                                ),fixedRow(splitLayout(cellWidths=c("45%","45%"),
                                                                       cellArgs = list(style = "padding: 15px"),
                                                                       tagList(tags$p(" ")),
                                                                       tagList(tags$p("Dot size, GO term significance."),
-                                                                              tags$p("Labels, most-populated reduced term categories."))
+                                                                              tags$p("Labels, most-populated reduced term categories."),
+                                                                              br(),br(),br(), 
+                                                                              div(tags$p("Read more about REVIGO visualizations "), 
+                                                                                  a(href="http://revigo.irb.hr/FAQ.aspx#q01", "here.")))
                                                )
                                                )
                                            )
 
                                        )
+
                               )
                           )
 
@@ -187,11 +203,13 @@ server <- function(input, output, session) {
 
         plot <-  ggplot(celltype_data,aes_string(x = 'UMAP_1', y = 'UMAP_2', color = 'CellType')) +
             geom_point(size = 0.1, alpha = 0.1) +
-            scale_colour_discrete(name = "Cell type") +
+            scale_colour_discrete(name = "Cell Type") +
             xlab("UMAP 1") + ylab("UMAP 2") + 
-            theme_minimal() + 
-            guides(colour = guide_legend(override.aes = list(size=5)))
-        #coord_fixed(ratio = 1) +
+            guides(colour = guide_legend(override.aes = list(size=4,alpha=1),byrow=T)) +
+            theme(legend.text = element_text(size=10), legend.title=element_text(size=10),
+                  legend.spacing.y = unit(0.9, 'cm')) +
+            coord_fixed(ratio = 1) +
+            theme_minimal() 
         #coord_fixed(xlim=c(-11,11),ylim=c(-11,11)) +
 
         plot
@@ -205,6 +223,11 @@ server <- function(input, output, session) {
             labs( 
                 x = "Developmental Timepoint of Organoid Cells",
                 y = "Proportion of Cells", fill="Cell Type" ) +
+
+            guides(fill = guide_legend(override.aes = list(size=4,alpha=1),byrow=T)) +
+            theme(legend.text = element_text(size=10), legend.title=element_text(size=10),
+                  legend.spacing.y = unit(0.9, 'cm')) +
+
             theme_minimal()
 
 
@@ -242,17 +265,24 @@ server <- function(input, output, session) {
         gene_plot_data <- FetchData(seurat.all, c('Age','Mt','CellType',input$gene))
         gene_plot_data <- setCellTypeNames(gene_plot_data)
 
-        ggplot(gene_plot_data, aes(x=Age, y=input$gene, fill=Mt)) + 
+        ggplot(gene_plot_data, aes(x=Age, y=.data[[input$gene]], fill=Mt)) + 
             geom_violin(trim=FALSE) + scale_fill_brewer(palette="Paired",name="Variant")  +
             facet_wrap(~CellType, scales="free") +
             labs(x="Developmental Timepoint",y="Expression Level") + 
             theme_minimal()
+        # ggplot(plotdata1, aes(x=Age, y=input$gene, fill=Mt)) + 
+        #     geom_violin(trim=FALSE) + scale_fill_brewer(palette="Paired",name="Variant")  +
+        #     scale_y_continuous(breaks=c(0,1,2,3),name=sprintf("%s Expression Level",input$gene)+
+        #     ylab("Expression Level") + 
+        #     xlab("Developmental Timepoint") + 
+        #     theme_minimal()
+        # 
     })
     # plot expression of input gene over time for each celltype by variant
     # as line graphs
     output$CellTypesByVariant <- renderPlot({
         expression_means_for_this_gene <- G6cells %>% 
-            select(all_of(input$gene),Mt,Age,CellType) %>% 
+            dplyr::select(all_of(input$gene),Mt,Age,CellType) %>% 
             group_by(Age, CellType, Mt) %>% 
             summarise(mean_expr = mean(.data[[input$gene]])) 
 
@@ -287,7 +317,7 @@ server <- function(input, output, session) {
     output$CellTypeExplorerPlots <- renderPlot({
         plot2 <- ggplot(celltype_data,aes_string(x = 'UMAP_1', y = 'UMAP_2', color="CellType")) +
             # render scatterplot, optionally adjust point size
-            geom_point(size = 0.1,alpha=0.2) +
+            geom_point(size = 0.1,alpha=0.3) +
             # adjust plot aspect ratio
             coord_fixed(ratio = 1) +
             # specify color gradient and a name for the associated legend
@@ -308,9 +338,9 @@ server <- function(input, output, session) {
         #showdata <- celltype_data %>% filter(CellType==input$celltype) 
         showdata <- tibble::rownames_to_column(celltype_markers)
         colnames(showdata)[1] <- "Gene"
-        showdata <- showdata %>% filter(avg_log2FC>=2) %>% select(Gene,avg_log2FC,p_val)
+        showdata <- showdata %>% filter(avg_log2FC>=2) %>% dplyr::select(Gene,avg_log2FC,p_val)
         showdata
-    })
+    }, options=list(searching=F))
 
     # Show GO output for the celltype marker genes of input celltype
     # output$GOtable <- renderDataTable({
@@ -336,7 +366,7 @@ server <- function(input, output, session) {
         simMatrix <- readRDS(sprintf("REVIGO_simMatrix_%s.rds",ct_select))
         reducedTerms <- readRDS(sprintf("REVIGO_reducedTerms_%s.rds",ct_select))
 
-        rrvgo::scatterPlot(simMatrix, reducedTerms)
+        scatterPlot(simMatrix, reducedTerms)
 
     })
     output$REVIGOtree <- renderPlot({
@@ -345,7 +375,7 @@ server <- function(input, output, session) {
 
         # File read-in must be here because data depends on a reactive input value
         reducedTerms <- readRDS(sprintf("REVIGO_reducedTerms_%s.rds",ct_select))
-        rrvgo::treemapPlot(reducedTerms)
+        treemapPlot(reducedTerms)
 
     })
 }