Merge pull request #38 from allaway/drug-selector

drug selector

.Rbuildignore

@@ -1,3 +1,5 @@
+^renv$
+^renv\.lock$
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^data-raw$
@@ -8,3 +10,5 @@ $run_dev.*
 ^README\.Rmd$
 ^CODE_OF_CONDUCT\.md$
 ^\.travis\.yml$
+^app\.R$
+^rsconnect$

.Rprofile

@@ -0,0 +1 @@
+source("renv/activate.R")

DESCRIPTION

@@ -25,7 +25,8 @@ Imports:
     forcats,
     scales,
     ggridges,
-    maftools
+    maftools,
+    pkgload
 RoxygenNote: 7.0.2
 Suggests: 
     testthat (>= 2.1.0),

R/fct_boxplot.R

@@ -59,4 +59,4 @@ create_boxplot <- function(
     I
 
 
-}
+}

R/mod_cell_line_selector.R

@@ -41,13 +41,18 @@ mod_cell_line_selector_server <- function(input, output, session){
   })
 
   output$cell_lines_table <- DT::renderDataTable({
-    models() %>% dplyr::filter(nf1Genotype %in% input$nf1_genotypes,
-                                  nf2Genotype %in% input$nf2_genotypes)
+    models() %>% 
+      dplyr::filter(nf1Genotype %in% input$nf1_genotypes,
+                    nf2Genotype %in% input$nf2_genotypes)
   })
 
-  cell_lines <- reactive({models() %>% 
-                                purrr::pluck('model_name') %>% 
-                                unique()})
+  cell_lines <- reactive({
+    models() %>% 
+      dplyr::filter(nf1Genotype %in% input$nf1_genotypes,
+                    nf2Genotype %in% input$nf2_genotypes) %>% 
+      purrr::pluck('model_name') %>% 
+      unique()
+    })
 }
 
 ## To be copied in the UI

R/mod_compound_selector.R

@@ -10,15 +10,28 @@
 mod_compound_selector_ui <- function(id){
   ns <- NS(id)
   tagList(
-
-  )
+    box(selectizeInput(ns("selected_compounds"), "Select up to 5 compounds",
+                            choices = kairos::drug_names$common_name,  options = list(maxItems = 5))),
+
+  box(DT::DTOutput(ns('drug_table'))))
 }
 
 #' compound_selector Server Function
 #'
 #' @noRd 
 mod_compound_selector_server <- function(input, output, session){
   ns <- session$ns
+
+  output$drug_table <- DT::renderDataTable({
+    kairos::drug_names %>% dplyr::filter(common_name %in% input$selected_compounds)
+  })
+
+  compounds <- reactive({
+      kairos::drug_names %>% 
+      dplyr::filter(common_name %in% input$selected_compounds) %>% 
+      dplyr::distinct() %>%  
+      purrr::pluck('DT_explorer_internal_id')
+    })
 
 }
 

R/mod_dose_response.R

@@ -10,7 +10,10 @@
 mod_dose_response_ui <- function(id){
   ns <- NS(id)
   tagList(
-
+    # DT::dataTableOutput(ns("dr_table")),
+    plotly::plotlyOutput(ns('drug_heatmap')),
+    plotly::plotlyOutput(ns('dr_plot'))
+
   )
 }
 
@@ -19,6 +22,44 @@ mod_dose_response_ui <- function(id){
 #' @noRd 
 mod_dose_response_server <- function(input, output, session, cell_lines, compounds){
   ns <- session$ns
+ dr_data <- reactive({
+   kairos::drug_raw %>% filter(DT_explorer_internal_id %in% compounds(),
+                                        model_name %in% cell_lines()) %>% 
+     dplyr::left_join(kairos::preferred_drug_names) %>% 
+     select(-DT_explorer_internal_id)
+ })
+
+ # output$dr_table <- DT::renderDataTable({
+ #   dr_data()
+ # })
+
+ output$drug_heatmap <- plotly::renderPlotly({
+
+   mat <- kairos::drug_screening %>% dplyr::filter(DT_explorer_internal_id %in% compounds(),
+                                            model_name %in% cell_lines()) %>% 
+     dplyr::filter(response_type == 'IC50_abs') %>%
+     dplyr::select(DT_explorer_internal_id, model_name, response) %>% 
+     dplyr::group_by(DT_explorer_internal_id, model_name) %>% 
+     dplyr::summarize(mean_resp = mean(response)) %>% 
+     tidyr::spread(model_name, mean_resp, drop = F) %>% 
+     dplyr::ungroup() %>% 
+     dplyr::left_join(kairos::preferred_drug_names) %>% 
+     select(-DT_explorer_internal_id) %>% 
+     tibble::column_to_rownames('std_name') %>% 
+     as.matrix() 
+
+   plot_ly(x=colnames(mat),y=rownames(mat),z=mat,type="heatmap",colors=colorRamp(c("darkblue","white","darkred")),colorbar=list(title="Score",len=0.4),hoverinfo='text') %>%
+     layout(yaxis=list(title="Condition"),xaxis=list(title="Gene"))
+ })
+
+ output$dr_plot <- plotly::renderPlotly({
+   p1 <- ggplot(dr_data() %>% 
+                  dplyr::group_by(model_name, dosage, std_name) %>% 
+                  dplyr::summarize(response = mean(response))) +
+     geom_point(aes(x = log10(dosage), y = response, color = model_name)) +
+     facet_wrap( ~ std_name)
+   plotly::ggplotly(p1)
+ })
 
 }
 

R/mod_latent_variables.R

@@ -85,7 +85,7 @@ mod_latent_variables_server <- function(input, output, session, specimens){
    #   labs(x = 'Latent Variable', y = "Expression")
    # 
    #  plotly::ggplotly(source = 'lv_overview', tooltip = 'x')
-     kairos::create_boxplot(foo2, 
+     create_boxplot(foo2, 
                             source_name = "mod_lv_a", 
                             color_col = NA,
                             sort = "desc") %>% 
@@ -104,8 +104,8 @@ mod_latent_variables_server <- function(input, output, session, specimens){
       dplyr::filter(latent_var == lv)  %>% 
       dplyr::mutate(x = modelOf, y = value)
 
-    kairos::create_boxplot(foo, 
-                           source_name = "mod_lv_b", 
+    create_boxplot(foo, 
+                          source_name = "mod_lv_b", 
                            color_col = NA) %>% 
       remove_legend() 
 
@@ -122,7 +122,7 @@ mod_latent_variables_server <- function(input, output, session, specimens){
       dplyr::filter(latent_var == lv) %>%
       dplyr::mutate(x = hugo_gene, y = loading)
 
-    kairos::create_barplot(foo,
+    create_barplot(foo,
                            source_name = "mod_lv_c",
                            color_col = NA,
                            sort = "desc") %>%

app.R

@@ -0,0 +1,7 @@
+# Launch the ShinyApp (Do not remove this comment)
+# To deploy, run: rsconnect::deployApp()
+# Or use the blue button on top of this file
+
+pkgload::load_all(export_all = FALSE,helpers = FALSE,attach_testthat = FALSE)
+options( "golem.app.prod" = TRUE)
+kairos::run_app() # add parameters here (if any)

data-raw/drug_names.R

@@ -0,0 +1,30 @@
+## code to prepare `drug_names.R` dataset goes here
+library(synapser)
+library(tidyverse)
+synLogin()
+
+drug_screening <- synGet("syn20700260")$path %>% readr::read_csv(
+  col_types = "cccccdcdccccdcdccdc"
+) %>% 
+  select(DT_explorer_internal_id) %>% 
+  distinct()
+
+drug_info <- synTableQuery("SELECT * FROM syn17090820")$filepath %>% readr::read_csv()
+
+drug_names <- drug_info %>% 
+  select(internal_id, common_name) %>%
+  rename(DT_explorer_internal_id = internal_id) %>% 
+  inner_join(drug_screening)
+
+usethis::use_data(drug_names)
+
+preferred_drug_names <- synTableQuery("select internal_id, std_name from syn17090819")$filepath %>% 
+  readr::read_csv() %>% 
+  select(internal_id, std_name) %>% 
+  rename(DT_explorer_internal_id = internal_id) %>% 
+  inner_join(drug_screening)
+
+usethis::use_data(preferred_drug_names,
+                  # overwrite = T
+                  )  
+

data-raw/drug_raw.R

@@ -0,0 +1,12 @@
+## code to prepare `drug_raw` dataset goes here
+library(synapser)
+library(tidyverse)
+synLogin()
+
+drug_raw <- synTableQuery("SELECT * FROM syn20556247", includeRowIdAndRowVersion = F)$filepath %>% 
+  read_csv(col_types = "cddccccccccddcdccccccc") %>% 
+  select(drug_screen_id, model_name, DT_explorer_internal_id, dosage, response) 
+
+usethis::use_data(drug_raw, 
+                  #overwrite = T
+                  )