Merge pull request #23 from SlavovLab/DO-MS-DIA

v2.0.6

docs/index.md

@@ -87,6 +87,9 @@ DO-MS is distributed by an [MIT license]({{site.github_link}}/blob/master/LICENS
 
 Please feel free to contribute to this project by opening an issue or pull request in the [GitHub repository]({{site.github_link}}).
 
+### Data Availability
+DO-MS reports and example data can be found [Here]({{site.baseurl}}/docs/DO-MS_examples). All raw data and search engine results from the DO-MS DIA paper are avilable on MassIVE under the following id: MSV000091733.
+
 -------------
 
 ## Help!

global.R

@@ -1,4 +1,4 @@
-version <- '2.0.5'
+version <- '2.0.6'
 
 # check R version. required R >= 3.5.0 & R <= 4.0.2
 if(as.numeric(R.Version()$major) < 4) {

modules/dia-nn/020_Ion_Sampling/Instrument_11_ms1_fill_time_dist.R

@@ -1,8 +1,8 @@
 init <- function() {
 
   type <- 'plot'
-  box_title <- 'Ms1 Fill Time Distribution'
-  help_text <- 'Ms1 fill times along gradient'
+  box_title <- 'MS1 Fill Time Distribution'
+  help_text <- 'Histogram showing the distribution of all MS1 fill times.'
   source_file <- 'report'
 
   .validate <- function(data, input) {

modules/dia-nn/020_Ion_Sampling/Instrument_12_ms1_tic.R

@@ -1,8 +1,8 @@
 init <- function() {
 
   type <- 'plot'
-  box_title <- 'Ms1 total Ion Current along Gradient'
-  help_text <- 'The toal Ion Current (TIC) is shown for bins along the retention time gradient.'
+  box_title <- 'MS1 TIC along Gradient'
+  help_text <- 'The total Ion Current (TIC) is shown for bins along the retention time gradient.'
   source_file <- 'report'
 
   .validate <- function(data, input) {

modules/dia-nn/020_Ion_Sampling/Instrument_13_ms1_fill_times.R

@@ -1,8 +1,8 @@
 init <- function() {
 
   type <- 'plot'
-  box_title <- 'Ms1 Fill Times along Gradient'
-  help_text <- 'The averge fill time is shown in magenta for different bins along the retention time gradient. The standard deviation is depicted as area in blue, scans outside this area are shown as single datapoints.'
+  box_title <- 'MS1 Fill Times along Gradient'
+  help_text <- 'The average fill time is shown in magenta for different bins along the retention time gradient. The standard deviation is depicted as an area in blue, and scans outside this area are shown as single data points.'
   source_file <- 'report'
 
   .validate <- function(data, input) {

modules/dia-nn/020_Ion_Sampling/Instrument_14_ms2_fill_time_dist.R

@@ -1,8 +1,8 @@
 init <- function() {
 
   type <- 'plot'
-  box_title <- 'Ms2 Fill Time Distribution'
-  help_text <- 'Ms2 fill times along gradient'
+  box_title <- 'MS2 Fill Time Distribution'
+  help_text <- 'Histogram showing the distribution of all MS2 fill times.'
   source_file <- 'report'
 
   .validate <- function(data, input) {

modules/dia-nn/020_Ion_Sampling/Instrument_15_ms2_fill_times.R

@@ -1,8 +1,8 @@
 init <- function() {
 
   type <- 'plot'
-  box_title <- 'Ms2 Fill Times along Gradient'
-  help_text <- 'The averge fill time is shown in magenta for different bins along the retention time gradient. The standard deviation is depicted as area in blue, scans outside this area are shown as single datapoints.'
+  box_title <- 'MS2 Fill Times along Gradient'
+  help_text <- 'The average fill time is shown in magenta for different bins along the retention time gradient. The standard deviation is depicted as an area in blue, and scans outside this area are shown as single data points.'
   source_file <- 'report'
 
   .validate <- function(data, input) {

modules/dia-nn/020_Ion_Sampling/Instrument_16_ms2_fill_time_matrix.R

@@ -1,8 +1,8 @@
 init <- function() {
 
   type <- 'plot'
-  box_title <- 'Ms2 Fill Time Matrix'
-  help_text <- 'The average Ms2 fill times are shown across the gradient for every distinct Ms2 window.'
+  box_title <- 'MS2 Fill Time Matrix'
+  help_text <- 'The average MS2 fill times are shown across the gradient for every distinct MS2 window.'
   source_file <- 'fill_times'
 
   .validate <- function(data, input) {

modules/dia-nn/020_Ion_Sampling/instrument_01_DIA_MS1_int_ID_Label.R

@@ -2,7 +2,7 @@ init <- function() {
 
   type <- 'plot'
   box_title <- 'Channel wise MS1 Intensity for Precursors'
-  help_text <- 'Plotting the MS1 intensity for all precursors which were associated with one of the defined channels.'
+  help_text <- 'Plotting the MS1 intensity for all precursors associated with one of the defined channels.'
   source_file <- 'report'
 
   .validate <- function(data, input) {

modules/dia-nn/020_Ion_Sampling/instrument_02_IDs_by_RT.R

@@ -2,7 +2,7 @@ init <- function() {
 
   type <- 'plot'
   box_title <- 'Precursors Identified across Gradient'
-  help_text <- 'Precursor are plotted across the chromatographic gradient.'
+  help_text <- 'Plotting the precursors across the chromatographic gradient.'
   source_file <- 'report'
 
   .validate <- function(data, input) {

modules/dia-nn/020_Ion_Sampling/instrument_05_DIA_MS1_Intersected_int_Norm.R

@@ -2,7 +2,7 @@ init <- function() {
 
   type <- 'plot'
   box_title <- 'Normalized MS1 Intensity for Intersected Precursors'
-  help_text <- 'Plotting the MS1 Intensity for intersected precursors summed over all channels. Experiments are normalized to the first experiment. '
+  help_text <- 'Plotting the MS1 intensity for intersected precursors summed over all channels. Experiments are normalized to the first experiment.'
   source_file <- 'report'
 
   .validate <- function(data, input) {

modules/dia-nn/020_Ion_Sampling/instrument_06_charge_count.R

@@ -2,7 +2,7 @@ init <- function() {
 
   type <- 'plot'
   box_title <- 'Number of Precursors by Charge State'
-  help_text <- 'Number of precursors observed during MS1 scans by charge state'
+  help_text <- 'Number of precursors observed during MS1 scans by charge state.'
   source_file <- 'report'
 
 

modules/dia-nn/020_Ion_Sampling/instrument_17_DIA_MS1_counts.R

@@ -1,10 +1,8 @@
 init <- function() {
 
   type <- 'plot'
-  box_title <- 'Channel wise MS1 Copy Number for Precursors'
-  help_text <- 'Plotting the MS1 copy numbers for all precursors which were associated with one of the defined channels. 
-  The copy numbers are calculated using the signal to noise ratio as described in Derks et al. 2022. By default a resolution of 70,000 is used during preprocessing. It can be changed with the --resolution parameter'
-  source_file <- 'sn'
+  box_title <- 'Channel-wise MS1 Copy Number for Precursors'
+  help_text <- 'Plotting the MS1 copy numbers for all precursors associated with one of the defined channels. The copy numbers are calculated using the signal-to-noise ratio as described in Derks et al. 2022. By default, a resolution of 70,000 is used during preprocessing. It can be changed with the –resolution parameter.'
 
   .validate <- function(data, input) {
     validate(need(data()[['sn']], paste0('Upload report.txt')))

modules/dia-nn/030_Identifications/00_PEP_colored_cumulative_update.R

@@ -2,7 +2,7 @@ init <- function() {
 
   type <- 'plot'
   box_title <- 'Number of Confident Precursor Identifications'
-  help_text <- 'Plotting the number of precursors identified at each given confidence level.'
+  help_text <- 'Plotting the number of precursors identified as a function of the false discovery rate (FDR).'
   source_file <- 'report'
 
   .validate <- function(data, input) {

modules/dia-nn/030_Identifications/01_ms1vsms2_IDs.R

@@ -2,7 +2,7 @@ init <- function() {
 
   type <- 'plot'
   box_title <-  'Precursors by Quantification Strategy'
-  help_text <- 'Number of precursors found based on quantification startegy. Ms2 precursors are counted based on Precursor.Quantity > 0 and Ms1 precursors are counted based on Ms1.Area > 0.'
+  help_text <- 'Number of precursors found based on quantification startegy. MS2 precursors are counted based on Precursor.Quantity > 0 and MS1 precursors are counted based on Ms1.Area > 0.'
   source_file <- 'report'
 
   .validate <- function(data, input) {

modules/dia-nn/030_Identifications/02_modified_IDs.R

@@ -2,7 +2,7 @@ init <- function() {
 
   type <- 'plot'
   box_title <-  'Precursors by Modification'
-  help_text <- 'Number of precursors found based on modification types specified'
+  help_text <- 'Number of precursors found based on modification types specified.'
   source_file <- 'report'
 
   .validate <- function(data, input) {

modules/dia-nn/030_Identifications/05_miscleavage_table.R

@@ -2,7 +2,7 @@ init <- function() {
 
   type <- 'table'
   box_title <- 'Miscleavage Rate (percentage), PEP < 0.01'
-  help_text <- 'Miscleavage rate (percentage) for precursors identified with confidence PEP < 0.01'
+  help_text <- 'Miscleavage rate (percentage) for precursors identified with confidence PEP < 0.01.'
   source_file <- 'report'
 
   .validate <- function(data, input) {

server/generate_report.R

@@ -34,13 +34,19 @@ generate_report <- function(input, filtered_data, exp_sets, file, progress_bar=F
     progress$inc(5/100, detail='Initializing')
   }
 
-  pep_max<-signif(max(filtered_data()[['evidence']][,"PEP"]),2)  
+  pep_max<-signif(max(filtered_data()[['evidence']][,"PEP"]),2)
+
+  pep_info = ''
+
+  if ((pep_max <= 1) && (pep_max >= 0)) {
+    pep_info = paste0(" | PEP < ",pep_max)
+  }
 
   report <- paste(
     '---',
     paste0('title: DO-MS Report'
     ),
-    paste0('date: "`r paste0(\'Version: ', version, " | PEP < ",pep_max, ' | \',  format(Sys.time(), \'Generated: %Y-%m-%d    %H:%M:%S\'))`"'),
+    paste0('date: "`r paste0(\'Version: ', version, pep_info, ' | \',  format(Sys.time(), \'Generated: %Y-%m-%d    %H:%M:%S\'))`"'),
     'output:',
 #    paste0('data filtered to PEP < ',
 #           pep_max),