rename metrics

.gitignore

@@ -10,3 +10,4 @@ testdata_bruker.zip
 testdata_mzML.zip
 Curve
 mzMl
+bayer_export.R

README.md

@@ -1,36 +1,34 @@
-<img src="figures/M2ara_Logo.png" alt="M²ara overview" width="100" height="100">
-
-# M²ara - MALDI MS Bioassays Evaluation and Classification App
+# M2ara <img src="figures/logo_resized.png" align="right" height="120"/>
 
 M²ara is a software tool to facilitate the exploration of metabolomic responses in complex matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) bioassays. The app is intended for the evaluation of metabolomic drug actions by using the mass-to-charge ratios of hundreds of metabolites and it is particularly useful in defining novel pharmacodynamic biomarkers for high-throughput applications.
 
 M²ara is based on the R package [MALDIcellassay](https://github.com/CeMOS-Mannheim/MALDIcellassay) (published in [Unger et. al. 2021](https://www.nature.com/articles/s41596-021-00624-z), Nature Protocols) and extends its capabilities with a GUI and adds helpful features like clustering of curves, PCA analysis as well as the Curve Response Score (CRS) which enables fast screening for molecules regulated by drug treatment.
 
-For more information please check out the [preprint](https://chemrxiv.org/engage/chemrxiv/article-details/663a1d0f418a5379b0aa286b). 
+For more information please check out the [preprint](https://chemrxiv.org/engage/chemrxiv/article-details/663a1d0f418a5379b0aa286b).
 
-<img src="figures/Workflow_figure.png" alt="M²ara workflow overview" width="100%">
+<img src="figures/Workflow_figure.png" alt="M²ara workflow overview" width="100%"/>
 
 ## How to use
 
-This application simplifies the analysis of Molecular High Content Screening (MHCS) MALDI-TOF MS assay data and the evaluation of complex drug actions. After your data has been loaded, you can adjust settings as needed and start the processing. From here, you can analyze your data by selecting entries in the data table, visually inspect and rank mass features using the Curve Response Score (CRS) fingerprints, and save the curve fit and peak profile of your chosen *m/z* value. 
+This application simplifies the analysis of Molecular High Content Screening (MHCS) MALDI-TOF MS assay data and the evaluation of complex drug actions. After your data has been loaded, you can adjust settings as needed and start the processing. From here, you can analyze your data by selecting entries in the data table, visually inspect and rank mass features using the Curve Response Score (CRS) fingerprints, and save the curve fit and peak profile of your chosen *m/z* value.
 
 This app is specifically designed for use with Bruker flex series raw data but also features support for mzML.
 
 For more detailed information please take a look at the [Manual](manual.md) that is also available inside the app.
 
-<img src="figures/gui_example.png" alt="M²ara GUI overview" width="100%">
+<img src="figures/gui_example.png" alt="M²ara GUI overview" width="100%"/>
 
-## How to install 
+## How to install
 
 ### R
 
 Clone the GitHub repository to your local machine (please make sure to have R installed, tested with **R v4.3.2**) and start the app by sourcing the `app.R` file.
 
-```bash
+``` bash
 git clone https://github.com/CeMOS-Mannheim/M2ara.git
 ```
 
-```R
+``` r
 # install all packages needed
 source("install_packages.R")
 
@@ -39,41 +37,42 @@ source("app.R")
 ```
 
 ### Docker
-Install the [docker container](https://hub.docker.com/repository/docker/thomasenzlein/m2ara), run it and access `localhost:3838` to interact with the app.
-Don't forget to change the path `c:/path/to/massSpecData` to your data so that it can be mounted when running the container.
 
-```bash
+Install the [docker container](https://hub.docker.com/repository/docker/thomasenzlein/m2ara), run it and access `localhost:3838` to interact with the app. Don't forget to change the path `c:/path/to/massSpecData` to your data so that it can be mounted when running the container.
+
+``` bash
 docker pull thomasenzlein/m2ara:main
 ```
 
-```bash
+``` bash
 docker run -p 3838:3838 -v  c:/path/to/massSpecData:/mnt thomasenzlein/m2ara:main
 ```
 
 ### Stand-alone installer for Windows
-Use the stand-alone installer (Windows only, no R installation needed).
-The installer can be downloaded [here](https://github.com/CeMOS-Mannheim/M2ara/releases/download/1.4.1/M2ara_1.4.1.exe).
+
+Use the stand-alone installer (Windows only, no R installation needed). The installer can be downloaded [here](https://github.com/CeMOS-Mannheim/M2ara/releases/download/1.4.1/M2ara_1.4.1.exe).
 
 ## Example data
-To test the app please use the example data on [FigShare](https://dx.doi.org/10.6084/m9.figshare.25736541). 
+
+To test the app please use the example data on [FigShare](https://dx.doi.org/10.6084/m9.figshare.25736541).
 
 #### Unger2020_OATP2B1_inhibition_mzML.zip
 
 The file contains mzML data (converted from Bruker Flex using MSConvert) originally published in Unger, et. al., 2020.
 
 To replicate the results shown use the following parameters:
 
-- under Settings set File Format to mzML
-- set Concentration unit to nM
-- set Normalization/re-calibration *m/z* to 354.1418 (D4-E3S, [M-H]<sup>-</sup>)
-- set recalibration tolerance to 0.1 Da
-- set normalization to *m/z*
-- deactivate smoothing and activate baseline removal
-- set Aggregation method to mean
-- set SNR to 3
-- set alignment to 0 mDa (no alignment)
-- set binning tolerance to 100 ppm
-- select the folder `mzML` (parent folder of the mzML files) from the .zip file, please make sure that no other files are in this folder.
+-   under Settings set File Format to mzML
+-   set Concentration unit to nM
+-   set Normalization/re-calibration *m/z* to 354.1418 (D4-E3S, [M-H]<sup>-</sup>)
+-   set recalibration tolerance to 0.1 Da
+-   set normalization to *m/z*
+-   deactivate smoothing and activate baseline removal
+-   set Aggregation method to mean
+-   set SNR to 3
+-   set alignment to 0 mDa (no alignment)
+-   set binning tolerance to 100 ppm
+-   select the folder `mzML` (parent folder of the mzML files) from the .zip file, please make sure that no other files are in this folder.
 
 Alternatively, copy the [this file](https://github.com/CeMOS-Mannheim/M2ara/blob/main/tests/testthat/settings_mzML_data.csv) as `settings.csv` into the main folder of the app.
 
@@ -85,19 +84,18 @@ The file contains data in the Bruker Flex format originally published in Weigt,
 
 To replicate the results shown use the following parameters:
 
-- under Settings set File Format to Bruker Flex
-- set Concentration unit to µM
-- set Normalization/re-calibration *m/z* to 760.5851 (PC(34:1) [M+H]<sup>+</sup>)
-- set recalibration tolerance to 0.1 Da
-- set normalization to TIC
-- activate smoothing and baseline removal
-- set Aggregation method to mean
-- set SNR to 3
-- set alignment to 0 mDa (no alignment)
-- set binning tolerance to 100 ppm
-- select the the folder `curve` from the .zip file, make sure no other files/folders are present.
+-   under Settings set File Format to Bruker Flex
+-   set Concentration unit to µM
+-   set Normalization/re-calibration *m/z* to 760.5851 (PC(34:1) [M+H]<sup>+</sup>)
+-   set recalibration tolerance to 0.1 Da
+-   set normalization to TIC
+-   activate smoothing and baseline removal
+-   set Aggregation method to mean
+-   set SNR to 3
+-   set alignment to 0 mDa (no alignment)
+-   set binning tolerance to 100 ppm
+-   select the the folder `curve` from the .zip file, make sure no other files/folders are present.
 
 Alternatively, copy the [this file](https://github.com/CeMOS-Mannheim/M2ara/blob/main/tests/testthat/settings_bruker_data.csv) as `settings.csv` into the main folder of the app.
 
 The target is *m/z* 826.5722 (PC(36:1) [M+K]<sup>+</sup>) and *m/z* 616.1767 (Heme B [M+H]<sup>+</sup>) the pIC50 values should be 9.5 and 9.7.
-

components/mainTab.R

@@ -53,9 +53,9 @@ mainTab <- function() {
                                     selectInput(inputId = "metric",
                                                 label = "Metric",
                                                 choices = c("CRS",
-                                                            "Z'",
-                                                            "V'",
-                                                            "SSMD",
+                                                            "FZ",
+                                                            "FV",
+                                                            "FS",
                                                             "log2FC",
                                                             "pEC50"),
                                                 selected = defaults$errorbars,

components/server.R

@@ -51,6 +51,7 @@ server <- function(input, output, session) {
                         normMz = input$normMz,
                         normTol = input$normTol,
                         normMeth = input$normMeth,
+                        smoothHalfWindowSize = 3,
                         alignTol = input$alignTol * 1e-3,
                         halfWindowSize = input$halfWindowSize,
                         peakMethod = input$peakMethod)
@@ -118,7 +119,6 @@ server <- function(input, output, session) {
                            dir = appData$selected_dir
                          )
       )
-
       message(MALDIcellassay:::timeNow(),  " processing done\n")
 
       # write everything needed into appData

functions/helpers.R

@@ -106,3 +106,14 @@ checkMetaData <- function(object) {
 
  return(TRUE)
 }
+
+#' Extract directory path
+#'
+#' @param object Object of class MALDIassay
+#'
+#' @return
+#' List, containing the data used to do the fits as well as the nlpr curve fit .
+getDirectory <- function(object) {
+  MALDIcellassay:::stopIfNotIsMALDIassay(object)
+  return(object@settings$dir)
+}

functions/plotFunctions.R

@@ -267,15 +267,15 @@ plateMapPlot <- function(appData,
   return(p)
 }
 
-scorePlot <- function(stats, metric = c("CRS", "V'", "Z'", "log2FC", "pEC50", "SSMD")) {
+scorePlot <- function(stats, metric = c("CRS", "FV", "FZ", "log2FC", "pEC50", "FS")) {
   metric <- match.arg(metric)
 
   df <- stats %>%
     mutate(direction = if_else(log2FC < 0, "down", "up")) %>%
     select(c("mz", "direction")) %>%
     mutate(value = pull(stats, metric))
 
-  if(metric %in% c("V'", "Z'")) {
+  if(metric %in% c("FV", "FZ")) {
     # cut V' and Z' at zero as lower values then zero just indicate bad models
     # and its prettier for visualization
     df <- df %>%
@@ -284,7 +284,7 @@ scorePlot <- function(stats, metric = c("CRS", "V'", "Z'", "log2FC", "pEC50", "S
     limits <- c(-1, 1)
   }
 
-  if(metric %in% c("CRS", "V'", "Z'", "SSMD")) {
+  if(metric %in% c("CRS", "FV", "FZ", "FS")) {
     df <- df %>%
       mutate(value = if_else(direction == "down", -value, value))
   }
@@ -300,14 +300,14 @@ scorePlot <- function(stats, metric = c("CRS", "V'", "Z'", "log2FC", "pEC50", "S
          y = ylab,
          col = NULL)
 
-  if(metric %in% c("V'", "Z'")) {
+  if(metric %in% c("FV", "FZ")) {
     p <- p +
       scale_y_continuous(limits = limits,
                          breaks = c(-1, -0.5, 0, 0.5, 1),
                          labels = c(1, 0.5, 0, 0.5 , 1))
   }
 
-  if(metric %in% c("log2FC", "SSMD")) {
+  if(metric %in% c("log2FC", "FS")) {
     absVal <- abs(df$value)
     absVal <- absVal[!is.infinite(absVal)]
     absMax <- max(absVal, na.rm = TRUE)

functions/storeResults.R

@@ -1,6 +1,14 @@
 storeResults <- function(appData, res, input, stats) {
   appData$res <- res
   appData$preprocessing <- appData$preprocessing
+
+  # rename Z', V', SSMD to FZ, FV and FS
+  stats <- stats %>%
+    rename("FZ" = `Z'`,
+           "FV" = `V'`,
+           "FS" = SSMD)
+
+
   appData$stats_original <- stats # copy of original stats for updates
   appData$stats <- stats