MTXQCvX_part3_template_params.Rmd

---
title: "MTXQCvX - Part3: ManualValidation template"
thanks: "Template MTXQCvX part3 written by Christin Zasada, Kempa Lab"
author:
- affiliation: LABNAME
  name: NAME
- affiliation: LABNAME
  name: NAME
biblio-style: apsr
output:
  pdf_document:
    citation_package: natbib
    fig_caption: yes
    keep_tex: yes
    toc: yes
    latex_engine: pdflatex
    template: config_files/textemplate.tex
  html_document: default
fontsize: 9pt
geometry: margin=1in
keywords: MTXQCvX, manual validation
date: "`r format(Sys.time(), '%V %d, %Y')`"
fontfamily: mathpazo
params:
  spath:
    input: text
    label: "Run on MTXQC-subfolder:"
    value: "test_metmax"
  folder:
    input: text
    label: "Define folder where to export and import files."
    value: "manval"
  prep:
    choices:
    - none
    - PeakArea
    - Incorporation
    input: select
    label: "Prepare data for manual validation:"
    value: none
  eval:
    choices: [none, PeakArea, Incorporation]
    input: select
    label: "Integration of manually validated data:"
    value: Incorporation
  inputformat:
    input: select
    label: "Choose the kind of input format of your data."
    choices: [maui, metmax]
    value: metmax
abstract: This document provides the manual validation of GC-MS derived data (MTXQC part 3). Thats has been processed through MTXQCvX part1 before. It transforms your MAUI exports into easily modifiable tables (PrepData) and re-transform them after manual validation into csv-files usable for another round of MTXQC (EvalQuant; EvalInc). In case of Metmax input files run MTXQC part 4 first.
---

# Data transformation for convenient manual validation

```{r quitknitting_immediately, echo=FALSE}

#if (params$inputformat != "maui") {
 # message("This input format is currently not integrated in this module! Sorry!")
#  knitr::knit_exit()
#}

if ((params$prep != "none") & (params$eval != "none")) {
  message("Please select only one action at a time - eiter data transformation or data integration!")
  knitr::knit_exit()
}


```


```{r init_directory, warning=FALSE, echo=FALSE}
#MOD!
set_input = "input/"
set_output = "output/"

## subfolder for postprocessing
set_val = paste0(params$folder, "/")

if (set_val == "") {
  message("Please define a folder!")
  knitr::knit_exit()
}

#directory definition and figure_name definition
if (params$spath == "") {
  path_setup = ""
  set_fig = paste0(path_setup, 'figure/MTXQCp3-')
} else {
  path_setup = paste0(params$spath, "/")
  set_fig = paste0(path_setup, 'figure/MTXQCp3-')
}

knitr::opts_chunk$set(fig.width = 5, fig.align = 'center', fig.height = 4,
                      fig.path = set_fig,  
                      echo = FALSE,  #TRUE - show R code
                      warning = FALSE, #show warnings
                      message = TRUE) #show messages

#Create a folder and stop processing if it is already present performing PrepData
if (params$prep != "none") {
  if (!dir.exists(file.path(paste0(path_setup, set_output, set_val)))) {
      dir.create(paste0(path_setup, set_output, set_val))
  } else {
     message("Folder already exists! Please define a new folder where to save transformed data.")
  }
}
```

# Choose the mode of the document! 

```{r modeofdoc}

message("Data transformation performed for: ", params$prep)
message("Data integration perfomed for: ", params$eval)

```


# Input files
```{r init_project}
#MOD!

library(reshape2)
library(ggplot2)
library(plyr)

source('R/MTXQC_config.R')
source("R/MTXQC_fcn.R")
source("R/MTXQC_fcn_part3.R")
source("R/MTXQC_fcn_incorp.R")
source("R/MTXQC_fcn_absQ.R")


  #import parameter: MTXQCsetup 
  if (file.exists(paste0(path_setup, "MTXQC_params.csv"))) {
    setup_params <- read.csv(paste0(path_setup, "MTXQC_params.csv"), TRUE)
    message("MTXQCparams.csv imported!")
  } else {
     message("Please run MTXQCvX_ExperimentalSetup in order to create MTXQC_params.csv")
    knitr::knit_exit()
  }

  inp_format = as.character(setup_params[which(setup_params$Parameter == "inputformat"), "Value"])

  #import parameter for input files:
  if (inp_format == "metmax") {
    if (file.exists(paste0(path_setup, "Metmax_params.csv"))) {
      id_settings = read.csv(paste0(path_setup, "Metmax_params.csv"))
      message("Metmax_params.csv imported.")
    } else {
      message("Metmax_params.csv not detected! Check defined input format!")
      knitr::knit_exit()
    }
  } else {
    if (file.exists(paste0(path_setup, "Maui_params.csv"))) {
      id_settings = read.csv(paste0(path_setup, "Maui_params.csv"))
      message("Maui_params.csv imported.")
    } else {
      message("Maui_params.csv not detected! Check defined input format!")
      knitr::knit_exit()
    }
  }

```


```{r prepdata}

 if (params$prep == "PeakArea") {
    ## (1) Absolute Quantification ##
    #PeakArea (derived from: MAUI or Metmax (after processed by MTXQC part 4))
   
   if (inp_format == "maui") {
     nick_peakarea = as.character(id_settings[which(id_settings$Parameter == "matrix"), "Value"])
      data_quant = read.csv(paste0(path_setup, set_input, "quant/", nick_peakarea), T)
      message("PeakAreaMatrix imported: ", nick_peakarea)
   } else {
     data_quant = read.csv(paste0(path_setup, set_input, "quant/quantMassAreasMatrix.csv"), T)
      message("PeakAreaMatrix imported: quant/quantMassAreasMatrix.csv")
   }
    
    #PeakAreaMatrix
    transform_quant(data_quant)
  }

 
if (params$prep == "Incorporation") {
    ## (2) Isotope Incorporation ##
  # (i) SpectraExport (MAUI or Metmax & MTXQC part 4), (ii) calculated isotope
  # incorporation rates (MAUI, Metmax), (iii) MTXQCp1 generated output file 
  # evaluation MID quality
    
    #nick_mid = as.character(id_settings[which(id_settings$Parameter == "mid"), "Value"])
    
    #independent import of pSIRM_SpectraData.csv of input format
    data_mid = read.csv(paste0(path_setup, set_input, "inc/pSIRM_SpectraData.csv"), TRUE)
    message("Checked: MIDs imported: inc/pSIRM_SpectraData.csv")
  
    
    #nick_inc = as.character(id_settings[which(id_settings$Parameter == "inc_data"), "Value"])
    #data_original = read.csv(paste0(path_setup, set_input, "inc/", nick_inc), T)
    #message("Imported Incorporation data: ", nick_inc)
    
    se_val = read.csv(paste0(path_setup, set_output, 
                                    "inc/SE_classification.csv"), T)
    
    message("Imported SE_classification.csv generated by MTXQC_part1.")
    
    #Incorporation
    transform_inc(df_se_val = se_val, df_mid = data_mid, conversion_table = con_se)
    
  }  		
```


```{r evalQuant, tidy = TRUE, results='asis'} 

if (params$eval == 'PeakArea') {
  
    ## quantities
	  quant_man = read.csv(paste0(path_setup, set_output, set_val, 'ManVal_PeakAreas.csv'),  T)
    evaluate_peakareas(quant_man)
}
```



```{r evalInc, tidy = TRUE, results='asis', fig.width=5}

	if (params$eval == 'Incorporation') {
	  
    ifelse(!dir.exists(file.path(paste0(path_setup, set_output, set_val, 'inc'))), 
	  dir.create(paste0(path_setup, set_output, set_val, 'inc')), 
		FALSE)
    		
    #MID import of manual validated values
    table_mid = read.csv(paste0(path_setup, set_output, set_val, 
    		            'MID_validation_values.csv'),  T)
	    
    #Extraction of updated values
    cor_mids = evaluate_modified_mids(df_check = table_mid)
    
    #Original MID-File
    orig_mids = read.csv(paste0(path_setup, set_input, "inc/pSIRM_SpectraData.csv"), T)

    #Integration updated MIDs into original table
    pSIRM_manVal <- integrate_manVal_MIDs(dataframe = table_mid,    #all MIDs including updated values
                                              corrected_mids = cor_mids, #extracted unique entries manVal
                                              original_mids = orig_mids) #original MID data
        
    #Calculate corrected isotope incorporation 
    table_sel = merge(table_mid, cor_mids)
    inc_calc_updated = calculate_isotope_incorporation(table_sel, backup_mids, mass_li, manval = TRUE)
	    
    #integration new values into original file -> DataMatrix.csv
    nick_inc = as.character(id_settings[which(id_settings$Parameter == "inc_data"), "Value"])
    data_original = read.csv(paste0(path_setup, set_input, "inc/", nick_inc), T)
    
    integrate_calc_inc(data_original, inc_calc_updated)
        
  }

```