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MTXQCvX_part1_template_params.Rmd
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MTXQCvX_part1_template_params.Rmd
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---
title: 'MTXQCvX2 - Part1: Standards and Reprod.'
author:
- affiliation: Kempa Lab, BIMSB/MDC Berlin-Buch
name: Christin Zasada
params:
spath:
input: text
label: "Run on MTXQC-subfolder:"
value: ""
data:
input: select
choices: [maui, metmax]
label: 'Input file format:'
value: metmax
updated:
input: select
choices: [none, PeakArea, Incorporation, both]
label: "Incorporation manualy validated data:"
value: none
output:
pdf_document:
citation_package: natbib
fig_caption: yes
keep_tex: yes
latex_engine: pdflatex
template: config_files/textemplate.tex
toc: yes
html_document: default
fontfamily: mathpazo
fontsize: 9pt
geometry: margin = 1in
keywords: MTXQCvX, GC-MS, metabolomics, data analysis and processing
biblio-style: apsr
thanks: Kempa Lab - Template MTXQCvX part1 - processed '`r format(Sys.Date(), "%B %d, %Y")`'
abstract: The herein presented report includes the manual validated peak areas of Itaconic acid. Absolute quantities have been determined by additional quantification standards.
---
# MTXQCvX part1
## General project settings
```{r setup_output, echo=FALSE}
#set path for figure export and size
set_input = "input/"
set_output = "output/"
## subfolder for postprocessing
#directory definition and figure_name definition
if (params$spath == "") {
path_setup = ""
set_fig = paste0(path_setup, 'figure/MTXQCp1-')
} else {
path_setup = paste0(params$spath, "/")
set_fig = paste0(path_setup, 'figure/MTXQCp1-')
}
knitr::opts_chunk$set(fig.width = 8, fig.align = 'center', fig.height = 7,
fig.path = set_fig,
echo = FALSE, #TRUE - show R code
warning = FALSE, #show warnings
message = TRUE,
eval = TRUE) #show messages
```
```{r source_rfiles}
source("R/MTXQC_pck.R")
source('R/MTXQC_fcn.R')
source('R/MTXQC_fcn_absQ.R')
source('R/MTXQC_fcn_gcperformance.R')
source('R/MTXQC_fcn_incorp.R')
source('R/MTXQC_fcn_metabolicprofile.R')
source('R/MTXQC_fcn_part3.R')
source('R/MTXQC_colors.R')
source('R/MTXQC_theme.R')
source('R/MTXQC_config.R')
```
## Data import
```{r import_datafiles, tidy = TRUE}
#tracking import errors
imp_error = 0
#import parameter: MTXQCsetup
if (file.exists(paste0(path_setup, "MTXQC_params.csv"))) {
setup_params <- read.csv(paste0(path_setup, "MTXQC_params.csv"), TRUE)
message("MTXQCparams.csv imported!")
} else {
message("Please run MTXQCvX_ExperimentalSetup in order to create MTXQC_params.csv")
knitr::knit_exit()
}
#import parameter for input files:
if (params$data == "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()
}
}
#check if addq-values exits if required
#Check if this whole addQ-file thing is needed!!!
nick_addq_file <- as.character(file_spec[which(file_spec$AssociatedFile == "addQ_file"), "Filename"])
idx_addq <- as.character(setup_params[which(setup_params$Parameter == "addQ"), "Value"])
if (idx_addq == "yes") {
if (!file.exists(file.path(paste0(path_setup, set_input, "add_quant/", nick_addq_file)))) {
message("Please specify the correct file containing additional Quant1-values in the ExperimentalSetup!")
knitr::knit_exit()
} else {
message("Required table containing additional Quant1-values detected!", nick_addq_file)
}
} else {
message("Experimental setup does not include additional quantification standards!")
}
#### Annotation files ####
## File annotation
ann_idx <- as.character(setup_params[which(setup_params$Parameter == "ann"), "Value"])
if (file.exists(file.path(paste0(path_setup, set_input, ann_idx)))) {
ann <- read.csv(paste0(path_setup, set_input, ann_idx), T)
if (ncol(ann) == 1) {
ann = read.csv(paste0(path_setup, set_input, ann_idx), T, sep = ";")
message("colon separator detected: ", ann_idx)
}
} else {
message("FATAL ERROR: Annotation file missing: ", ann_idx)
knitr::knit_exit()
}
check_emptyfile(ann, TRUE, ann_idx)
#Cell count / extract
se_idx <- as.character(setup_params[which(setup_params$Parameter == "sample_ext"), "Value"])
if (file.exists(file.path(paste0(path_setup, set_input, se_idx)))) {
data_extracts <- read.csv(paste0(path_setup, set_input, se_idx), T)
if (ncol(data_extracts) == 1) {
data_extracts = read.csv(paste0(path_setup, set_input, se_idx), T, sep = ";")
message("colon separator detected: ", se_idx)
}
} else {
message("FATAL ERROR: Sample_extracts file missing: ", se_idx)
knitr::knit_exit()
}
check_emptyfile(data_extracts, TRUE, se_idx)
##### (1) GC-MS performance files ####
## import (i) cinnamic acid peak areas, (ii) alkane intensities,
## (iii) mz 73 intensities and (iv) total peak densities from Chromatof
ca_idx <- setup_params[which(setup_params$Parameter == "instd"), "Value"]
mm_ca_idx <- id_settings[which(id_settings$Parameter == "intstd"), "Value"]
if (ca_idx == TRUE) {
if (mm_ca_idx == TRUE) {
nick_ca <- as.character(file_spec[which(file_spec$AssociatedFile == "cin_acid"), "Filename"])
ca_path <- paste0(path_setup, set_input,'gc/', nick_ca)
if (file.exists(ca_path) == TRUE) {
cinacid <- read.csv(ca_path, T, sep = ',')
if (ncol(cinacid) == 1) {
cinacid <- read.csv(ca_path, T, sep = ';')
}
#Which internalstandard exported in InternalStandard.csv
intstd = unique(cinacid$Metabolite)
message("Defined Internal Standard: ", intstd)
check_emptyfile(cinacid, FALSE, "InternalStandard.csv")
is_exit = 0
} else {
message("WARNING: No file detected: InternalStandard.csv")
is_exit = 1
}
} else {
imp_error = imp_error + 1
is_exit = 1
message("WARNING: No internal standard for data input format defined!")
}
} else {
is_exit = 1
message("No internal standard in Experimental Setup defined!")
}
alkane_idx <- as.character(id_settings[which(id_settings$Parameter == "alkanes"), "Value"])
nick_alk <- as.character(file_spec[which(file_spec$AssociatedFile == "alkane_int"), "Filename"])
if (alkane_idx == TRUE) {
data_alk <- read.csv(paste0(path_setup, set_input,'gc/', nick_alk), T, sep = ',')
colnames(data_alk)[grepl("file", colnames(data_alk))] = "File"
check_emptyfile(data_alk, FALSE, nick_alk)
} else {
imp_error = imp_error + 1
message("WARNING: No alkane file defined for this input format!")
}
#m/z 73 Table
nick_73 <- as.character(file_spec[which(file_spec$AssociatedFile == "mz_73"), "Filename"])
mm_73_idx <- id_settings[which(id_settings$Parameter == "mz"), "Value"]
if (mm_73_idx != "") {
if (!file.exists(file.path(paste0(path_setup, set_input,'gc/', nick_73)))) {
message("WARNING: File missing: ", nick_73)
message("Please review MTXQC_ExperimentalSetup!")
soa_73_exit = 1
} else {
data_73 <- read.csv(paste0(path_setup, set_input,'gc/', nick_73), T, sep = ",")
check_emptyfile(data_73, FALSE, nick_73)
soa_73_exit = 0
}
} else {
imp_error = imp_error + 1
soa_73_exit = 1
message("WARNING: No file with m/z 73 values defined for this input format!")
}
### Peak-Chroma Table
nick_peaks <- as.character(file_spec[which(file_spec$AssociatedFile == "peak_densities"), "Filename"])
mm_peaks_idx <- id_settings[which(id_settings$Parameter == "peakchroma"), "Value"]
if (mm_peaks_idx == TRUE) {
if (!file.exists(file.path(paste0(path_setup, set_input,'gc/', nick_peaks)))) {
message("WARNING: File missing: ", nick_peaks)
message("Please review MTXQC_ExperimentalSetup!")
soa_exit = 1
} else {
total_peaks <- read.csv(paste0(path_setup, set_input,'gc/', nick_peaks), T)
check_emptyfile(total_peaks, FALSE, nick_peaks)
soa_exit = 0
}
} else {
imp_error = imp_error + 1
soa_exit = 1
message("WARNING: No file defined for this input format! No sum of area normalisation! ", nick_peaks)
}
#### (2) Absolute quantification ####
## (2.1) Exported peak areas according to top5 or pTop5 approach
if ((params$updated == "PeakArea") | (params$updated == "both")) {
#incorporation of manualy validated data
nick_samples <- as.character(file_spec_manVal[which(file_spec_manVal$AssociatedFile == "sample_area"), "Filename"])
if (!file.exists(file.path(paste0(path_setup, set_input, 'quant/', nick_samples)))) {
message("FATAL ERROR: Manual validated peak area file not detected: ", nick_samples)
knitr::knit_exit()
} else {
data_area <- read.csv(paste0(path_setup, set_input, 'quant/', nick_samples), T)
if (ncol(data_area) == 1) {
data_area <- read.csv(paste0(path_setup, set_input, 'quant/', nick_samples), T, sep = ";")
}
check_emptyfile(data_area, TRUE, nick_samples)
}
} else {
#only original data
nick_samples <- as.character(file_spec[which(file_spec$AssociatedFile == "sample_area"), "Filename"])
if (!file.exists(file.path(paste0(path_setup, set_input, 'quant/', nick_samples)))) {
message("FATAL ERROR: Peak area table not detected: ", nick_samples)
knitr::knit_exit()
} else {
data_area <- read.csv(paste0(path_setup, set_input, 'quant/', nick_samples), T)
if (ncol(data_area) == 1) {
data_area <- read.csv(paste0(path_setup, set_input, 'quant/', nick_samples), T, sep = ";")
}
check_emptyfile(data_area, TRUE, nick_samples)
}
}
#### (3) Incorporation data ####
## only needed for pSIRM experiment
## (i) MID exports and (ii) calculated isotope incorporation
inc_idx <- setup_params[which(setup_params$Parameter == "data"), "Value"]
evalinc_idx <- setup_params[which(setup_params$Parameter == "substr"), "Value"]
mm_inc_idx <- id_settings[which(id_settings$Parameter == "inc"), "Value"]
if ((params$updated == "none") | (params$updated == "PeakArea")) { #run if not Inc manually validated
if (inc_idx != "qMTX") {
if (mm_inc_idx != FALSE) {
if (evalinc_idx != FALSE | evalinc_idx == "none") {
#Mass isotopomer distributions
nick_psirm <- as.character(file_spec[which(file_spec$AssociatedFile == "pSIRM_se"), "Filename"])
if (!file.exists(file.path(paste0(path_setup, set_input,'inc/', nick_psirm)))) {
message("FATAL ERROR: Essential file not detected: ", nick_psirm)
knitr::knit_exit()
} else {
data_mid <- read.csv(paste0(path_setup, set_input,'inc/', nick_psirm), T)
if (ncol(data_mid) == 1) {
data_mid <- read.csv(paste0(path_setup, set_input, 'inc/', nick_psirm), T, sep = ";")
}
check_emptyfile(data_mid, TRUE, nick_psirm)
}
#13C-Incorporation
nick_inc <- as.character(file_spec[which(file_spec$AssociatedFile == "inc"), "Filename"])
if (!file.exists(file.path(paste0(path_setup, set_input,'inc/', nick_inc)))) {
message("FATAL ERROR: Essential file missing! ", nick_inc)
message("Are you sure it's a pSIRM experiment???")
knitr::knit_exit()
} else {
data_inc <- read.csv(paste0(path_setup, set_input,'inc/', nick_inc), T)
if (ncol(data_inc) == 1) {
data_inc <- read.csv(paste0(path_setup, set_input, 'inc/', nick_inc), T, sep = ";")
}
check_emptyfile(data_inc, TRUE, nick_inc)
}
} else {
message("No stable isotope incorporation evaluated.")
}
} else (
message("No MID and 13Inc-calculation defined for this input format.")
)
} else {
message("It's not a pSIRM experiment!")
}
} else {
#run if Inc == manually validated
if (inc_idx != "qMTX") {
if (mm_inc_idx != FALSE) {
if (evalinc_idx != FALSE | evalinc_idx == "none") {
#Mass isotopomer distributions
nick_psirm <- as.character(file_spec_manVal[which(file_spec_manVal$AssociatedFile == "pSIRM_se"), "Filename"])
if (!file.exists(file.path(paste0(path_setup, set_input,'inc/', nick_psirm)))) {
message("FATAL ERROR: Essential file missing: ", nick_psirm)
message("Did you really validated the incorporation data?")
knitr::knit_exit()
} else {
data_mid <- read.csv(paste0(path_setup, set_input,'inc/', nick_psirm), T)
if (ncol(data_mid) == 1) {
data_mid <- read.csv(paste0(path_setup, set_input, 'inc/', nick_psirm), T, sep = ";")
}
check_emptyfile(data_mid, TRUE, nick_psirm)
}
#13C-Incorporation
nick_inc <- as.character(file_spec_manVal[which(file_spec_manVal$AssociatedFile == "inc"), "Filename"])
if (!file.exists(file.path(paste0(path_setup, set_input,'inc/', nick_inc)))) {
message("FATAL ERROR: Essential file missing: ", nick_inc)
message("Did you really validated the incorporation data?")
knitr::knit_exit()
} else {
data_inc <- read.csv(paste0(path_setup, set_input,'inc/', nick_inc), T)
if (ncol(data_inc) == 1) {
data_inc <- read.csv(paste0(path_setup, set_input, 'inc/', nick_inc), T, sep = ";")
}
check_emptyfile(data_inc, TRUE, nick_inc)
}
} else {
message("No stable isotope incorporation evaluated.")
}
} else {
message("No MID and 13Inc-calculation defined for this input format.")
}
} else {
message("It's not a pSIRM experiment!")
}
}
```
```{r check_input_files}
imp_error <- MTXQCp1_checkinput(data_extracts, ann)
if (imp_error != 0) {
message("Check the imported files messages! Number of files without import: ", imp_error)
} else {
message("Annotation and Sample_extract.csv correctly imported!")
}
```
# MTXQC - GC-MS perfomance
## Alkane standards
```{r alkanes_QCmetric}
if (alkane_idx == TRUE) {
data_alk = file_shaping(data_alk, shape = "long", ann, type = "sample")
alk_qc = gc_metric_calc(data_alk, title = "alkanes")
}
```
```{r alkanes, fig.height = 5, fig.width = 5, fig.cap = "Distribution of Alkanes (c10 - c36)"}
if (alkane_idx == TRUE) {
if (!nrow(data_alk) == 0) {
ggplot(data_alk, aes(File, intensity)) +
geom_boxplot() +
coord_flip() +
facet_wrap(~Batch_Id, scales = "free") +
#ggtitle('Distribution of Alkanes (#c10 - #c36)') +
geom_point(aes(color = metabolite)) +
scale_color_manual(values = brewer.pal(9, "Paired"),guide = guide_legend(title = 'Alkane')) +
xlab('File') +
ylab('Peak area in (-)') +
theme(text = element_text(size = 8))
} else {
message("This data frame is empty!")
}
} else {
message("Evaluation of alkanes not activated and data present.")
}
```
## Data normalization
### Internal standard evaluation
```{r internalstandard_qc, tidy = TRUE}
if (is_exit != 1) {
if (mm_ca_idx == TRUE) {
if (ca_idx == TRUE) {
cin_data = file_shaping(cinacid, shape = "wide",
file_annotation = ann,
type = "sample",
inc = FALSE)[,c("File", "PeakArea", "Batch_Id")]
cinacid_qc = gc_metric_calc(cin_data, title = "internalstandard")
} else {
Batch_Id = unique(alk_qc$Batch_Id)
cinacid_qc = data.frame(Batch_Id, qc_metric = rep(NA, length(Batch_Id)))
write.csv(cinacid_qc, paste0(path_setup,'output/gc/qcmetric_IntStandard.csv'), row.names = F)
}
}
}
```
```{r internalstandard_plot, echo = F, fig.cap = "Quantification of internal extraction standard", fig.width = 5, fig.height = 5}
if (ca_idx == TRUE && mm_ca_idx == TRUE && is_exit == 0) {
cinacid_statistics = read.csv(paste0(path_setup, 'output/gc/IntStandard_stats.csv'), TRUE)
if (nrow(cinacid_statistics) != 0) {
cin_f = c('below','within','above')
cinacid_statistics$CA_eval = factor(cinacid_statistics$IntStd_eval, levels = cin_f)
ggplot(cinacid_statistics, aes(File, PeakArea, color = IntStd_eval)) +
geom_point() +
#geom_bar(stat = "identity", size = .3, color = "black") +
coord_flip() +
geom_hline(aes(yintercept = mean_batch), color = 'red', linetype = 'dotdash') +
geom_hline(aes(yintercept = mean_batch - sd_batch), color = c('black'), linetype = 'dotdash') +
geom_hline(aes(yintercept = mean_batch + sd_batch), color = c('black'), linetype = 'dotdash') +
theme_bw() +
theme(text = element_text(size = 8)) +
facet_wrap(~ Batch_Id, scales = "free") +
scale_color_manual(values = color_ca,
guide = guide_legend(title = 'Eval: Internal Standard')) +
ylab('PeakArea in (-)') +
theme(legend.position = "bottom")
} else {
message("Empty data frame OR no peak areas for internal standard detectable!")
}
} else {
message("Empty data frame OR no peak areas for internal standard detectable!")
}
```
### Sum of Area of annotated metabolites
```{r sumArea_qc, tidy = TRUE}
if (mm_peaks_idx == TRUE) {
suma_data <- file_shaping(data_area, shape = 'wide',
ann, type = "sample",
inc = FALSE)
suma_qc <- gc_metric_calc(suma_data, title = 'sumofarea')
#50% of annotated values level
nmin = read.csv(paste0(path_setup, 'output/gc/Min_Annotation.csv'), TRUE)
knitr::kable(nmin, format = 'markdown', caption = '50& quartile of annotated metabolites per batch.')
} else {
message("SumOfArea normalisation has not been selected in MTXQC part 4!")
}
```
```{r nb_annotation, echo = F, fig.cap = "Count N: Annotated intermediates per file. Evaluate careful for SumOfArea normalisation.", fig.width = 5, fig.height = 5}
if (mm_peaks_idx == TRUE) {
area_plot = read.csv(paste0(path_setup, 'output/gc/SumArea_stats.csv'), TRUE)
if (nrow(area_plot != 0)) {
ggplot(area_plot, aes(File, n_area)) +
geom_point(size = 2) +
ggtitle("Count: Annotated metabolites per file") +
coord_flip() +
facet_wrap(~ Batch_Id, scales = "free_y") +
geom_hline(aes(yintercept = n_50), color = "red",linetype = "dashed") +
ylab("Count N (-)") +
xlab('File') +
theme(legend.position = "bottom") +
theme(text = element_text(size = 8))
} else {
message("No SumArea_stats.csv file detected!")
}
}
```
```{r SumofArea, echo = F, fig.cap = "Total peak area of all annotated metabolite per file.", fig.width = 5, fig.height = 5}
if (mm_peaks_idx == TRUE) {
area_plot = read.csv(paste0(path_setup, 'output/gc/SumArea_stats.csv'), TRUE)
if (nrow(area_plot != 0)) {
ggplot(area_plot, aes(File, sum_area)) +
geom_bar(stat = "identity") +
ggtitle('Normalization: SumOfArea') +
coord_flip() +
geom_hline(aes(yintercept = mean_batch), color = 'red', linetype = 'dotdash') +
geom_hline(aes(yintercept = mean_batch - sd_batch), color = c('black'), linetype = 'dotdash') +
geom_hline(aes(yintercept = mean_batch + sd_batch), color = c('black'), linetype = 'dotdash') +
ylab('Total peak area in (-)') +
facet_wrap(~ Batch_Id, scales = "free_y") +
theme(legend.position = "bottom") +
theme(text = element_text(size = 8))
} else {
message("No SunArea_stats.csv file detected!")
}
}
```
## Derivatization check
```{r mass73_stats, tidy = TRUE}
#m/z 73 and total peaks data
if (mm_73_idx != "none" && mm_peaks_idx == TRUE) {
data_deriv = file_shaping(data_73, shape = 'long', ann, type = "sample", inc = FALSE)
total_peaks = file_shaping(total_peaks, 'long', ann, type = "sample", inc = FALSE)
deriv_comb = gc_metric_calc(dataframe = data_deriv, dataframe2 = total_peaks, title = 'mz73')
} else {
message("No input files detected. Either MassSum-73.csv or PeakDensities-Chroma.csv")
}
```
## HeatMap - GC-MS performance
```{r HM_GC, fig.height = 2, fig.width = 4, fig.cap = "Heatmap summarising parameters of GC-MS performance"}
#import qcm_metrics
qcm_values = do.call(rbind, lapply(list.files(path = paste0(path_setup, 'output/gc'),
pattern = "qcmetric_",
recursive = TRUE,
full.names = TRUE), read.csv, header = TRUE))
write.csv(qcm_values, paste0(path_setup,'output/gc/HM_GC_values.csv'), row.names = F)
knitr::kable(qcm_values, caption = "Summary of parameter evaluating GC-Performance", format = "pandoc")
message('Export of GC-Performance values done!')
#plot: range [0,1] = [white, dodgerblue4], NA = grey30
p <- ggplot(qcm_values, aes(Batch_Id, title)) +
geom_tile(aes(fill = qc_metric), color = 'white') +
coord_flip() +
scale_fill_gradient2(mid = 'ghostwhite', high = '#d94801', limits = c(0,1),
na.value = 'ghostwhite',
guide = guide_legend(title = 'Score'))
base_size = 7
p_gc <- p + theme_bw(base_size = base_size) +
labs(x = '', y = '') +
scale_y_discrete(expand = c(0,0)) +
scale_x_discrete(expand = c(0,0)) +
theme(axis.text.x = element_text(size = base_size, angle = 330, hjust = 0, colour = 'grey50')) +
#theme(axis.text.y = element_blank()) +
#theme(legend.position = 'none') +
ggtitle('GC-Performance') +
theme(text = element_text(size = 8))
```
\newpage
# MTXQC - Quantitative metabolomics
## Generation of ManualQuantTable: Quant-Standards (Qstd)
```{r import_quant1_std}
#import Quant1-values for Quant-Mix
quant1 = read.csv("config_mtx/quant1_values.csv", header = T)
check_emptyfile(quant1, TRUE, "quant1_values.csv")
#which quant-version has been used
q_idx = as.character(setup_params[which(setup_params$Parameter == "quant"), "Value"])
quant_table = quant1[,c("Letter_Derivate", q_idx)]
quant_table = merge(quant_table, pathway_profile)[,c("Letter_Derivate", "Lettercode", as.character(q_idx))]
```
```{r check_mqt}
if (params$data == "maui") {
#check ManualQuantTable.tsv and defined Batch-Ids in annotation file
batch_id_def = check_mqt_batchids(ann, path_setup)
nb_id = length(batch_id_def)
#extract tsv-file names
file_names <- dir(paste0(path_setup, set_input, "quant/"), pattern = ".tsv")
#combine all tsv-files, add Batch-Id
cual <- do.call(rbind, lapply(file_names,
function(x) cbind(read.csv(paste0(path_setup, set_input,
"quant/", x), sep = "\t"),
Batch_Id = strsplit(x,'\\.')[[1]][1])))
write.csv(cual, paste0(path_setup, set_input, "quant/MQT_combined.csv"), row.names = FALSE)
#generate ManualQuantTable
mqt_std = create_manualquanttable(cual, q1_values = quant_table, con_se, FALSE)
message("ManualQuantTable generated and exported!")
}
if (params$data == "metmax") {
cual = read.csv(paste0(path_setup, set_input, "quant/ManualQuantTable_calc_", q_idx,".csv"))
message("ManualQuantTable imported!")
}
```
## Generation of ManualQuantTable: Additional calibration curves (Qadd)
```{r add_calcurves, fig.height = 2.5, fig.width =5, fig.cap = "Additional Calibration curves"}
#identify param settings
addq_idx = as.character(setup_params[which(setup_params$Parameter == "addQ"), "Value"])
nick_addq = as.character(file_spec[which(file_spec$AssociatedFile == "addQ"), "Filename"])
addq_integration = as.character(setup_params[which(setup_params$Parameter == "addQ_Int"), "Value"])
if (addq_idx == 'yes') {
#additional quant1_1 values > check for column names
quant_1add <- read.csv(paste0(path_setup, set_input, "add_quant/", nick_addq), T)
message(paste("Additional quant1-values imported for metabolites: ", length(unique(quant_1add$Lettercode)) ))
if (ncol(quant_1add) == 1) {
quant_1add <- read.csv(paste0(path_setup, set_input, "add_quant/", nick_addq), T, sep = ";")
message(paste0("Colon separator detected in: ", nick_addq))
}
#select Q_sel == "x" -> requires to check all metabolites that should be checked for absolute quantification
con_se_quant = subset(con_se, con_se$Q_sel == "x")
#Extract quant1-values for additional substances defined input/add_quant/ nick_addq
quant_1add <- merge(quant_1add, con_se_quant[,c("Lettercode", "Metabolite")])
knitr::kable(quant_1add, format = 'pandoc',
caption = 'Quant1:1-values for additional calibration curves.')
#Check annotation file for "addQ"
#identifer phrase in annotation file for additional Quant-samples
addq_phrase <- "addQ"
ann_addq <- extract_addQ_annotation(annotation_file = ann, phrase = "addQ")
addq_batchid = create_batchid(ann_addq)
#check if addq contains all batch ids from the complete setup
if (length(addq_batchid) == nb_id) {
message("Additional calibration curves have been defined for all included batches!")
} else {
message("Additional calibration curves have been not defined for all batches included in the annotation file!")
missing_ids = c(addq_batchid[!(addq_batchid %in% batch_id_def)], batch_id_def[!(batch_id_def %in% addq_batchid)])
message("Batch Id containing additional calibration curves: ", addq_batchid)
}
#peak areas from quantmassareamatrix
df_peakareas <- file_shaping(data_area,
shape = 'wide',
file_annotation = ann_addq,
complete_ann = TRUE,
type = "addQ",
inc = FALSE)
#add annotation lists for metabolite names
df_peakareas <- merge(df_peakareas, con_se[,c("Metabolite","Lettercode")])
#select intermediates based on additional quant1_1 values
addq_sel <- subset(df_peakareas, Lettercode %in% unique(quant_1add$Lettercode))
#duplicate values for each batch -> addq_integration == "yes"
if (addq_integration == "yes") {
#remove column Bacth Id
idx_batchid = which(grepl("Batch_Id", colnames(addq_sel)))
trial = addq_sel[,-idx_batchid]
#create new dataframe containing Batch Ids and Origin Qadd
merge_sets = data.frame(Batch_Id = batch_id_def, Origin = rep("Qadd", length(batch_id_def)))
trial_c = merge(trial, merge_sets)
addq_sel = trial_c
message("Additional calibration curves have been duplicated and added for all batches!")
}
###### Create ManualQuantTable with additional measurements
mqt_addq <- create_manualquanttable(addq_sel, quant_1add, met_translation = con_se,
plot = TRUE)
}
```
```{r merge_mqts}
if (addq_idx == 'yes') {
#prepare Quant-Standards
colnames(mqt_std)[grep("Quant", colnames(mqt_std))] <- "Quant1"
#mqt_std$Origin = rep("Qstd", length(mqt_std$Lettercode))
#prepare add-Quants
colnames(mqt_addq)[grepl("Quant", colnames(mqt_addq))] <- "Quant1"
#mqt_addq$Origin = rep("Qadd", length(mqt_addq$Lettercode))
mqt_addq = subset(mqt_addq, mqt_addq$Batch_Id %in% batch_id_def)
mqt_combined = rbind(mqt_std, mqt_addq)
write.csv(mqt_combined, paste0(path_setup, set_input,
"quant/ManualQuantTable_integrated.csv"), row.names = FALSE)
message("Additional Quant-Standards have been added to MQT_integrated.csv")
}
```
## Determination of calibration curves
```{r cal_curves, tidy = TRUE, warning=FALSE}
#calibration curve data
#cual = read.delim(paste0(path_setup,'input/quant/ManualQuantTable.tsv'), sep = '\t', header = T)
if (addq_idx == 'yes') {
cal_data = read.csv(paste0(path_setup, set_input, "quant/ManualQuantTable_integrated.csv"))
} else {
cal_data = read.csv(paste0(path_setup, set_input, "quant/ManualQuantTable_calc_", q_idx,".csv"))
}
#add annotation: Metabolite_short
cal_data <- merge(cal_data, con_se[,c("Lettercode", "Metabolite_short", "Q_sel")])
#clean-up
cal_clean <- subset(cal_data, !is.na(cal_data$Concentration))
cal_clean <- subset(cal_clean, cal_clean$ChromIntensities != -99)
cal_clean <- subset(cal_clean, cal_clean$Q_sel == "x")
#calculate adj. Rsquare, intercept and slope
qcurve_top5_rsquare(cal_clean, path_setup)
#read updated file (containing adj. r-square, slope and intercept)
qt <- read.table(file = paste0(path_setup, 'output/quant/top5_QMQcurveInfo.csv'), T)
#check empty file
if (is.data.frame(qt) && !nrow(qt == 0)) {
message("Empty file detected: quant/top5QMQcurveInfo.csv")
message("Please check the annotation of your metabolites.")
knitr::knit_exit()
} else {
message("top5_QMQcurveInfo.csv imported!")
#Determine QC-params for Quantification
qc_calcurve <- quant_metric_calc_new(qt)
}
```
```{r calcurve_par, fig.height = 5, fig.width = 5, fig.cap = "Calibration curves: Nb. of data points."}
if (is.data.frame(qc_calcurve) && nrow(qc_calcurve != 0)) {
ggplot(qc_calcurve, aes(Lettercode, Par_value, color = Parameter)) +
geom_point(aes(shape = Parameter), size = 3) +
coord_flip() +
ggtitle('Calibration curve: adj. R square and nb of data points') +
ylim(0,1) +
geom_hline(aes(yintercept = 0.75), linetype = 'dashed', color = 'grey30') +
scale_color_manual(values = c('tomato3','black')) +
scale_shape_manual(values = c(17,20)) +
facet_grid(Origin ~ Batch_Id, scales = "free_y") +
xlab('Derivate') +
ylab('Parameter value in (-)') +
theme(legend.position = "bottom")
}
```
```{r limits}
qt_limits = melt(qt, id = c('Lettercode', 'Batch_Id', 'Origin'), measure.vars = c('max','min'),
variable.name = "Param", value.name = "Limits")
qt_limits = unique(qt_limits)
```
```{r calcurve_linrange, echo = FALSE, fig.width = 5, fig.height = 5, fig.cap = "Limits of quantifiable range per metabolite"}
ggplot(qt_limits, aes(Lettercode, Limits)) +
geom_point(aes(shape = Param)) +
ggtitle('Limits of quantification (pmol)') +
scale_shape_manual(values = c(17,21), guide = guide_legend(title = 'Quant. Range')) +
coord_flip() +
scale_y_log10() +
facet_grid(Origin ~ Batch_Id, scales = "free_y") +
xlab('Derivate') +
ylab('Quantity in (pmol)') +
theme(legend.position = "bottom") +
theme(text = element_text(size = 8))
```
## Evaluation of experimental data
### Determination extraction factor
```{r extr_fac, tidy = TRUE}
extr_fac = eval_extractionfactor(setup_params)
```
### Quantification range and limits
```{r pools_linrange, tidy = TRUE}
#area import
#data = read.csv(paste0(path_setup, 'input/quant/quantMassAreasMatrix.csv'), header = T, sep = ',')
data_exp = file_shaping(data_area, shape = 'wide', ann, FALSE, type = "sample", inc = FALSE)
data_ann = merge(data_exp, con_se)
#clean-up: Q_sel == "x" or nopsirm == "x" (its required to remove technical replicates due to pSIRM)
data_clean = subset(data_ann, data_ann$Q_sel == "x" | data_ann$nopsirm == "x")
```
```{r samples_lincheck, tidy = TRUE, warning=FALSE}
#add a column to state if there is an calibration curve available
uni_qt = unique(qt[,c("Metabolite", "Batch_Id", "Origin")])
qt_check = unique(qt[,c('Metabolite', 'Batch_Id', 'Origin','adj_r_squared',
'slope', 'intercept')])
data_qt_check = merge(data_clean, qt_check, all.x = T)
data_qt_check$calc_curve = factor(ifelse(is.na(data_qt_check$adj_r_squared),
'no_cal', 'yes_cal'),
levels = c('yes_cal', 'no_cal'))
#Check linearity range
lin_cal = evaluate_qt_lin(data_qt_check)
```
```{r samples_range, echo = FALSE, fig.width = 5, fig.height = 7, fig.cap = "Distribution of data points regarding linear range of the calibration curve"}
if (nrow(lin_cal != 0)) {
if (addq_idx == "yes") {
print(
#ggplot(subset(lin_cal, lin_cal$Origin == "Qstd" | lin_cal$Origin == NA),
ggplot(subset(lin_cal, lin_cal$Origin == "Qstd" | is.na(lin_cal$Origin)),
aes(x = Lettercode, y = prop, fill = islinear)) +
geom_bar(stat = 'identity', size = .25, color = 'black') +
scale_fill_manual(values = color_linearity,
guide = guide_legend(title = 'Evaluation')) +
coord_flip() +
facet_wrap(Origin ~ Batch_Id, scales = "free_y") +
xlab('Derivate') +
ylab('Fraction of data points (%)') +
theme(legend.position = "bottom") +
theme(text = element_text(size = 8))
)
print(
ggplot(subset(lin_cal, lin_cal$Origin == "Qadd"),
aes(x = Lettercode, y = prop, fill = islinear)) +
geom_bar(stat = 'identity', size = .25, color = 'black') +
scale_fill_manual(values = color_linearity,
guide = guide_legend(title = 'Evaluation')) +
coord_flip() +
facet_wrap(Origin ~ Batch_Id, scales = "free_y") +
xlab('Derivate') +
ylab('Fraction of data points (%)') +
theme(legend.position = "bottom") +
theme(text = element_text(size = 8))
)
} else {
ggplot(lin_cal, aes(x = Lettercode, y = prop, fill = islinear)) +
geom_bar(stat = 'identity', size = .25, color = 'black') +
scale_fill_manual(values = color_linearity,
guide = guide_legend(title = 'Evaluation')) +
#ggtitle('Fraction of measurements regarding quantification curve') +
coord_flip() +
facet_wrap(Origin ~ Batch_Id, scales = "free_y") +
xlab('Derivate') +
ylab('Fraction of data points (%)') +
theme(legend.position = "bottom") +
theme(text = element_text(size = 8))
}
} else {
message("Empty data frame: Linear range")
}
```
### Absolute quantification samples
```{r calc_quantity, tidy = TRUE}
#Check linearity range
data_calcheck = read.csv(paste0(path_setup, set_output, 'quant/calcheck_linearity.csv'), TRUE)
data_quantified = data_calcheck
data_quantified$absconc = data_quantified$slope * data_quantified$PeakArea + data_quantified$intercept
#consider extraction and preparation factors
data_quantified$extr_fac = rep(extr_fac, length(data_quantified$Lettercode))
data_quantified$corr_absconc = data_quantified$absconc * data_quantified$extr_fac
data_q_plot = data_quantified
data_q_plot <- data_q_plot %>%
mutate(corr_absconc = replace(corr_absconc, is.na(corr_absconc), 0))
```
```{r calcurve_sample, fig.width = 6, fig.height = 6}