Cibersort & mcpcounter

DESCRIPTION

@@ -22,7 +22,8 @@ Imports:
     readr,
     shinycssloaders,
     wrapr,
-    forcats
+    forcats,
+    ggridges
 RoxygenNote: 7.0.2
 Suggests: 
     testthat (>= 2.1.0),

NAMESPACE

@@ -11,6 +11,8 @@ export(mod_explore_page_server)
 export(mod_explore_page_ui)
 export(mod_gene_variant_server)
 export(mod_gene_variant_ui)
+export(mod_immune_signatures_server)
+export(mod_immune_signatures_ui)
 export(mod_latent_variables_server)
 export(mod_latent_variables_ui)
 export(mod_resources_page_server)

R/mod_explore_page.R

@@ -18,7 +18,7 @@ mod_explore_page_ui <- function(id){
   tagList(
       dashboardPage(
         dashboardHeader(disable = T),
-        dashboardSidebar(
+        dashboardSidebar(sidebarMenu(
           id = "explorertabs",
           menuItem("Explore Home",
                    tabName = "dashboard",
@@ -27,7 +27,7 @@ mod_explore_page_ui <- function(id){
           menuItem("Analysis Modules",
                    icon = icon("chart-area"), startExpanded = TRUE,
                    menuSubItem(
-                     "Immune Infiltration",
+                     "Immune Cell Deconvolution",
                      tabName = "immune_infiltration",
                      icon = icon("cog")
                    ),
@@ -39,15 +39,16 @@ mod_explore_page_ui <- function(id){
                    menuSubItem(
                      "Latent Variables",
                      tabName = "latent_variables",
-                     icon = icon("cog")))),
+                     icon = icon("cog"))))),
         dashboardBody(
           tagList(
             tabItems(
               tabItem(
                 tabName = "dashboard"
                 ),
               tabItem(
-                tabName = 'immune_infiltration'
+                tabName = 'immune_infiltration',
+                mod_immune_signatures_ui(ns("immune_signatures_ui_1"))
                 ),
               tabItem(
                 tabName = "gene_var",
@@ -74,6 +75,7 @@ mod_explore_page_server <- function(input, output, session, specimens){
 
   callModule(mod_gene_variant_server, "gene_variant_ui")
   callModule(mod_latent_variables_server, "latent_variables_ui_1", specimens)  
+  callModule(mod_immune_signatures_server, "immune_signatures_ui_1", specimens)
 }
 
 ## To be copied in the UI

R/mod_immune_signatures.R

@@ -0,0 +1,93 @@
+# Module UI
+
+#' @title   mod_immune_signatures_ui and mod_immune_signatures_server
+#' @description  A shiny Module.
+#'
+#' @param id shiny id
+#' @param input internal
+#' @param output internal
+#' @param session internal
+#'
+#' @rdname mod_immune_signatures
+#'
+#' @keywords internal
+#' @export 
+#' @importFrom shiny NS tagList 
+mod_immune_signatures_ui <- function(id){
+    ns <- NS(id)
+
+    tagList(
+      h2("Immune Cell Deconvolution"),
+      box(h4('Module Summary'), 
+          p('This immune cell deconvolution module can be used to estimate the presence of different types of immune cells from RNA-Seq tumor data. Two algorithms, ', 
+          a(href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5895181/', 'CIBERSORT', .noWS = "outside"), ' and ',
+          a(href='https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1070-5', 'MCPcounter', .noWS = "outside"),
+          ', were used to estimate immune cell abundance in NF tumor samples. A rightward shift of the peak indicates that the group of samples is predicted to have more of a given cell type. Please note that cell lines and non-human models are not available; only patient samples and PDX models of human tumors are available for analysis.',
+          .noWS = c("after-begin", "before-end")), 
+          width = 12),
+      box(title = "CIBERSORT", 
+          status = "primary", solidHeader = TRUE,
+          width = 12,
+          collapsible = FALSE,
+          plotOutput(ns('cibersort_ridgeplot'))
+                               # %>% shinycssloaders::withSpinner(custom.css=T) ##throws an error that looks to be css related
+          ),
+      box(title = "MCPcounter", 
+          status = "primary", solidHeader = TRUE,
+          width = 12,
+          collapsible = FALSE,
+          plotOutput(ns('mcpcounter_ridgeplot')
+                               # %>% shinycssloaders::withSpinner(custom.css=T) ##throws an error that looks to be css related
+      ))
+    )
+
+}
+
+# Module Server
+
+#' @rdname mod_immune_signatures
+#' @export
+#' @keywords internal
+
+mod_immune_signatures_server <- function(input, output, session, specimens){
+  ns <- session$ns
+
+  output$cibersort_ridgeplot <- renderPlot({
+
+    foo <- kairos::immune_predictions %>% 
+      dplyr::filter(specimenID %in% specimens() &
+                      method == 'cibersort') 
+    ggplot(foo) +
+      ggridges::geom_density_ridges(aes(x = score, y=cell_type, fill = cell_type)) +
+      facet_grid(cols = vars(tumorType)) +
+      theme_minimal() + 
+      theme(legend.position = 'none',
+            text  = element_text(size = 12)) + 
+      labs(x = 'Cell Type', y = "Density")
+
+  })
+
+  output$mcpcounter_ridgeplot <- renderPlot({
+
+    foo <- kairos::immune_predictions %>% 
+      dplyr::filter(specimenID %in% specimens() &
+                      method == 'mcp_counter') 
+
+    ggplot(foo) +
+      ggridges::geom_density_ridges(aes(x = score, y=cell_type, fill = cell_type)) +
+      facet_grid(cols = vars(tumorType)) +
+      theme_minimal() + 
+      theme(legend.position = 'none',
+            text  = element_text(size = 12)) + 
+      labs(x = 'Cell Type', y = "Density") 
+
+  })
+
+}
+
+## To be copied in the UI
+# mod_immune_signatures_ui("immune_signatures_ui_1")
+
+## To be copied in the server
+# callModule(mod_immune_signatures_server, "immune_signatures_ui_1")
+

R/mod_latent_variables.R

@@ -45,6 +45,8 @@ mod_latent_variables_server <- function(input, output, session, specimens){
       dplyr::summarize(sdev = sd(value)) %>% 
     dplyr::top_n(10, sdev) 
 
+   validate(need(nrow(foo)>0, "No latent variable data found. Please modify your cohort."))
+
    foo2 <- dplyr::filter(kairos::latent_var,
                          latent_var %in% foo$latent_var) %>% 
      dplyr::mutate(latent_var = stringr::str_trunc(latent_var, 15)) %>% 

data-raw/immune_predictions.R

@@ -0,0 +1,9 @@
+## code to prepare `immune_predictions` dataset goes here
+library(synapser)
+library(dplyr)
+synLogin()
+immune_predictions <- synTableQuery("SELECT * FROM syn21177277")$filepath %>% 
+  readr::read_csv() %>% 
+  select(method, cell_type, score, specimenID, tumorType, sex, diagnosis)
+
+usethis::use_data(immune_predictions)