Merge pull request #35 from CRI-iAtlas/add_sc_cohort_selection

Add sc cohort selection

.gitignore

@@ -2,4 +2,4 @@
 .Rhistory
 .RData
 .Ruserdata
-.DS_Store
+.DS_Store

R/cohort_dataset_selection_server.R

@@ -13,7 +13,8 @@ cohort_dataset_selection_server <- function(
         options <- iatlasGraphQLClient::query_datasets(types = dataset_type()) %>%
           dplyr::select("display", "name") %>%
           tibble::deframe(.)
-        if(dataset_type() == "analysis") return(options)
+
+        if(dataset_type() %in% c("analysis", "scrna")) return(options)
         else return(create_ici_options(options)) #for the ICI Cohort Selection, we have RNA-Seq and Nanostring data. This function returns a list organizing them. 
       })
 
@@ -36,7 +37,14 @@ cohort_dataset_selection_server <- function(
             choices  = choices(),
             selected = default_datasets()
           )
-        }else{ 
+        }else if(dataset_type() == "scrna"){
+            shiny::checkboxGroupInput(
+                inputId  = ns("dataset_choices"),
+                label    = "Select Datasets",
+                choices  = choices(), #list scRNA-seq datasets
+                selected = default_datasets()
+            )
+        }else{ #ICI Cohort selection will have two dataset columns, one for RNA-Seq and the other for Nanostring datasets
           shiny::fluidRow(
             shiny::column(
               width = 6,
@@ -63,7 +71,7 @@ cohort_dataset_selection_server <- function(
       })
 
       dataset_selection <- shiny::reactive({
-        if(dataset_type() == "analysis") return(input$dataset_choices)
+        if(dataset_type() %in% c("analysis", "scrna")) return(input$dataset_choices)
         else return(c(input$dataset_choices, input$dataset_choices_ns)) #ICI Cohort selection can have RNA-Seq and Nanostring ds selected
       })
 

R/server.R

@@ -8,8 +8,9 @@ server <- function(input, output, session) {
     dataset_type     = shiny::reactive("ici"),
     display_module_availibility_string = shiny::reactive(F)
   )
+
   cohort_object3 <- cohort_selection_server(
-      "sc_cohort_selection_module",
+      "scrna_cohort_selection_module",
       default_datasets = shiny::reactive(c("MSK", "Vanderbilt")),
       default_group    = shiny::reactive("Responder"),
       dataset_type     = shiny::reactive("scrna"),

R/ui.R

@@ -5,7 +5,7 @@ ui <- function() {
       shiny::tabPanel("Start", shiny::textOutput("cohort_text_output")),
       shiny::tabPanel("Cohort Selection", cohort_selection_ui("cohort_selection_module")),
       shiny::tabPanel("ICI Cohort Selection", cohort_selection_ui("ici_cohort_selection_module")),
-      shiny::tabPanel("SC Cohort Selection", cohort_selection_ui("sc_cohort_selection_module")),
+      shiny::tabPanel("scRNAseq Cohort Selection", cohort_selection_ui("scrna_cohort_selection_module")),
       shiny::tabPanel("Driver Associations", univariate_driver_ui("driver_module"))
     )
   )

inst/markdown/cohort_selection1.markdown

@@ -1,6 +1,6 @@
 Use this module to create your own custom cohort and to specify how cancer genomics samples are grouped and used for contrasts in iAtlas. You can also upload a file to specify the cohort and sample groups. When you are done, cohort and sample group selection will be saved, and used throughout iAtlas. **You can refer back to this module at any time to review or modify your choices.** A summary of your cohort and sample groups is shown at the bottom of this page.
 
-You can also access the iAtlas data by querying our database. Check our Jupyter Notebooks:
-- [Query the CRI-iAtlas database, get TCGA features and expression](https://github.com/CRI-iAtlas/iatlas-notebooks/blob/main/querying_TCGA_features_and_expression.ipynb)
-- [Immune Checkpoint Inhibition data available in iAtlas](https://github.com/CRI-iAtlas/iatlas-notebooks/blob/main/ici_query_iatlas_data.ipynb)
-- [Single-cell RNA-seq data available in iAtlas](https://github.com/CRI-iAtlas/iatlas-notebooks/blob/main/query_iatlas_single_cell_datasets.ipynb)
+You can also access the CRI iAtlas data by querying our database. We have Jupyter Notebooks detailing how to use the CRI iAtlas API to query our database:
+- [Query the TCGA dataset](https://github.com/CRI-iAtlas/iatlas-notebooks/blob/main/querying_TCGA_features_and_expression.ipynb)
+- Query the Immune Checkpoint Inhibition datasets [in R](https://github.com/CRI-iAtlas/iatlas-notebooks/blob/main/ici_query_iatlas_data.ipynb) and  [in Python](https://github.com/CRI-iAtlas/iatlas-notebooks/blob/main/ici_query_iatlas_data_python.ipynb)
+- [Query the pseudobulk single-cell RNAseq data](https://github.com/CRI-iAtlas/iatlas-notebooks/blob/main/query_iatlas_single_cell_datasets.ipynb)