[FIX] - changes in the way quantCols is given to readQFeatures

R/data.R

@@ -1,12 +1,11 @@
 # This documentation is coming from the scp package.
 # This package use the same tables as example data.
-
 ##' @title Example MaxQuant/SCoPE2 output
 ##'
 ##' @description
 ##'
 ##' A `data.frame` with 1088 observations and 139 variables, as
-##' produced by reading a MaxQuant output file with
+##' produced by reading a MaxQuant output file with 
 ##' [read.delim()].
 ##'
 ##' - Sequence: a character vector
@@ -149,23 +148,23 @@
 ##' - participated: a logical vector
 ##' - peptide: a character vector
 ##'
-##' @usage data("inputTable")
-##'
-##' @details
-##'
-##' The dataset is a subset of the SCoPE2 dataset (version 2, Specht
-##' et al. 2019,
-##' [BioRXiv](https://www.biorxiv.org/content/10.1101/665307v3)). The
-##' input file `evidence_unfiltered.csv` was downloaded from a
+##' @usage data("mqScpData")
+##' 
+##' @details 
+##' 
+##' The dataset is a subset of the SCoPE2 dataset (version 2, Specht 
+##' et al. 2019, 
+##' [BioRXiv](https://www.biorxiv.org/content/10.1101/665307v3)). The 
+##' input file `evidence_unfiltered.csv` was downloaded from a 
 ##' [Google Drive repository](https://drive.google.com/drive/folders/1VzBfmNxziRYqayx3SP-cOe2gu129Obgx).
-##' The MaxQuant evidence file was loaded and the data was cleaned
-##' (renaming columns, removing duplicate fields,...).  MS runs that
-##' were selected in the `scp1` dataset (see `?scp1`) were kept along
+##' The MaxQuant evidence file was loaded and the data was cleaned 
+##' (renaming columns, removing duplicate fields,...).  MS runs that 
+##' were selected in the `scp1` dataset (see `?scp1`) were kept along 
 ##' with a blank run. The data is stored as a `data.frame`.
-##'
-##' @seealso [readSCP()] for an example on how `mqScpData` is
+##' 
+##' @seealso [readSCP()] for an example on how `mqScpData` is 
 ##'     parsed into a [QFeatures] object.
-##'
+##' 
 ##' @md
 "inputTable"
 
@@ -181,23 +180,23 @@
 ##' - sortday: a character vector
 ##' - digest: a character vector
 ##'
-##' @usage data("sampleTable")
-##'
-##' @details
-##'
-##' ##' The dataset is a subset of the SCoPE2 dataset (version 2, Specht
-##' et al. 2019,
-##' [BioRXiv](https://www.biorxiv.org/content/10.1101/665307v3)). The
-##' input files `batch.csv` and `annotation.csv` were downloaded from a
+##' @usage data("sampleAnnotation")
+##' 
+##' @details 
+##' 
+##' ##' The dataset is a subset of the SCoPE2 dataset (version 2, Specht 
+##' et al. 2019, 
+##' [BioRXiv](https://www.biorxiv.org/content/10.1101/665307v3)). The 
+##' input files `batch.csv` and `annotation.csv` were downloaded from a 
 ##' [Google Drive repository](https://drive.google.com/drive/folders/1VzBfmNxziRYqayx3SP-cOe2gu129Obgx).
-##' The two files were loaded and the columns names were adapted for
-##' consistency with `mqScpData` table (see `?mqScpData`). The two
+##' The two files were loaded and the columns names were adapted for 
+##' consistency with `mqScpData` table (see `?mqScpData`). The two 
 ##' tables were filtered to contain only sets present in ``mqScpData`.
 ##' The tables were then merged based on the run ID, hence merging the
-##' sample annotation and the batch annotation. Finally, annotation
-##' for the blank run was added manually. The data is stored as a
+##' sample annotation and the batch annotation. Finally, annotation 
+##' for the blank run was added manually. The data is stored as a 
 ##' `data.frame`.
-##'
+##' 
 ##' @seealso [readSCP()] to see how this file is used.
 ##'
 ##' @md

R/interface_module_box_readQFeatures.R

@@ -41,8 +41,9 @@ box_readqfeatures_ui <- function(id) {
                 ),
                 selectInput(
                     inputId = NS(id, "quant_cols"),
-                    "Quantitative column :",
-                    choices = NULL
+                    "Quantitative column : (Only relevant without sample table)",
+                    choices = NULL,
+                    multiple = TRUE
                 ),
                 checkboxInput(
                     inputId = NS(id, "removeEmptyCols"),

R/server_module_box_readQFeatures.R

@@ -24,8 +24,8 @@ box_readqfeatures_server <- function(id, input_table, sample_table) {
                 "can be quite time consuming for large data sets",
                 sep = " "
             ))
-            print(typeof(unique(sample_table()[[input$quant_cols]])))
-            global_rv$qfeatures <- error_handler(
+            if(is.data.frame(sample_table())){
+                global_rv$qfeatures <- error_handler(
                 QFeatures::readQFeatures,
                 component_name = "QFeatures converting (readQFeatures)",
                 assayData = input_table(),
@@ -34,6 +34,17 @@ box_readqfeatures_server <- function(id, input_table, sample_table) {
                 removeEmptyCols = input$removeEmptyCols,
                 verbose = FALSE
             )
+            }
+            else{
+                global_rv$qfeatures <- error_handler(
+                QFeatures::readQFeatures,
+                component_name = "QFeatures converting (readQFeatures)",
+                assayData = input_table(),
+                runCol = input$run_col,
+                quantCols = input$quant_cols,
+                removeEmptyCols = input$removeEmptyCols,
+                verbose = FALSE
+            )}
             if (input$zero_as_NA && length(global_rv$qfeatures) > 0) {
                 global_rv$qfeatures <- error_handler(
                     QFeatures::zeroIsNA,
@@ -61,11 +72,11 @@ box_readqfeatures_server <- function(id, input_table, sample_table) {
             input$reload_button
             updateSelectInput(session,
                 "run_col",
-                choices = colnames(sample_table())
+                choices = colnames(input_table())
             )
             updateSelectInput(session,
                 "quant_cols",
-                choices = colnames(sample_table())
+                choices = colnames(input_table())
             )
         })