Parallel Annotation"
In peakPantheR: Peak Picking and Annotation of High Resolution Experiments

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

if (getRversion() >= "3.6") {
  knitr::opts_chunk$set(eval = FALSE)
}
required <- c("faahKO")
if (!all(unlist(lapply(required, function(pkg) requireNamespace(pkg, quietly = TRUE))))) {
  knitr::opts_chunk$set(eval = FALSE)
}

The peakPantheR package is designed for the detection, integration and reporting of pre-defined features in MS files.

The Parallel Annotation is set to detect and integrate multiple compounds in multiple files in parallel and store results in a single object.

Using an example dataset, this vignette will:

Detail the Parallel Annotation concept
Apply the Parallel Annotation to an example dataset

Parallel Annotation Concept

knitr::include_graphics("../man/figures/parallelAnnotation.png")

Parallel compound integration is set to:

process multiple compounds in multiple files in parallel, store results in a single object
load list of expected RT / m/z ROI and list of files to process
initialise output object with expected ROI and file paths
first pass (without peak filling):
- for each file, detect features in each ROI and keep highest intensity
- determine peak statistics for each feature
- store results + EIC for each ROI
visual inspection of first pass results, update ROI:
- diagnostic plots: all EICs, peak apex RT / m/z & peak width evolution
- correct ROI (remove interfering feature, correct RT shift)
- define fallback integration regions (FIR) if no feature is detected (median RT / m/z start and end of found features)
initialise new output object, with updated regions of interest (uROI) and fallback integration regions (FIR)
second pass (with peak filling):
- for each file, detect features in each uROI and keep highest intensity
- determine peak statistics for each feature
- integrate FIR when no peaks found
- store results + EIC for each uROI
summary statistics:
- plot EICs, apex and peakwidth evolution
- compare first and second pass
return result object and/or table (row: file, col: compound)

Parallel Annotation Example

We can target 2 features in 3 MS spectra file from the faahKO package with peakPantheR_parallelAnnotation():

setRepositories(ind=1:4)
install.packages('faahKO')

Input Data

Input spectra are selected:

# hide package load message
library(faahKO)

library(faahKO)
## file paths
input_spectraPaths  <- c(system.file('cdf/KO/ko15.CDF', package = "faahKO"),
                         system.file('cdf/KO/ko16.CDF', package = "faahKO"),
                         system.file('cdf/KO/ko18.CDF', package = "faahKO"))
input_spectraPaths

Two targeted features are defined and stored in a table with as columns:

cpdID (numeric)
cpdName (character)
rtMin (sec)
rtMax (sec)
rt (sec, optional / NA)
mzMin (m/z)
mzMax (m/z)
mz (m/z, optional / NA)

# targetFeatTable
input_targetFeatTable     <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c("cpdID", "cpdName", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax"))), stringsAsFactors=F)
input_targetFeatTable[1,] <- c("ID-1", "Cpd 1", 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222)
input_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962)
input_targetFeatTable[,c(3:8)] <- sapply(input_targetFeatTable[,c(3:8)], as.numeric)

input_targetFeatTable     <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c("cpdID", "cpdName", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax"))), stringsAsFactors=F)
input_targetFeatTable[1,] <- c("ID-1", "Cpd 1", 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222)
input_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962)
input_targetFeatTable[,c(3:8)] <- sapply(input_targetFeatTable[,c(3:8)], as.numeric)
rownames(input_targetFeatTable) <- NULL
pander::pandoc.table(input_targetFeatTable, digits = 9)

Additional compound and spectra metadata can be provided but isn't employed during the fit:

# spectra Metadata
input_spectraMetadata  <- data.frame(matrix(c("sample type 1", "sample type 2", "sample type 1"), 3, 1, dimnames=list(c(), c("sampleType"))), stringsAsFactors=F)

input_spectraMetadata  <- data.frame(matrix(c("sample type 1", "sample type 2", "sample type 1"), 3, 1, dimnames=list(c(), c("sampleType"))), stringsAsFactors=F)
pander::pandoc.table(input_spectraMetadata)

Initialise and Run Parallel Annotation

A peakPantheRAnnotation object is first initialised with the path to the files to process (spectraPaths), compounds to integrate (targetFeatTable) and additional information and parameters such as spectraMetadata, uROI, FIR and if they should be used (useUROI=TRUE, useFIR=TRUE):

library(peakPantheR)
init_annotation <- peakPantheRAnnotation(spectraPaths = input_spectraPaths,
                                         targetFeatTable = input_targetFeatTable,
                                         spectraMetadata = input_spectraMetadata)

The resulting peakPantheRAnnotation object is not annotated, does not contain and use uROI and FIR

init_annotation

peakPantheR_parallelAnnotation() will execute the annotation across files in parallel (if ncores >0) and return the successful annotations (result$annotation) and failures (result$failures):

# annotate files serially
annotation_result <- peakPantheR_parallelAnnotation(init_annotation, ncores=0, verbose=TRUE)

# successful fit
nbSamples(annotation_result$annotation)
data_annotation   <- annotation_result$annotation
data_annotation

# list failed fit
annotation_result$failures

Process Parallel Annotation Results

Based on the fit results, updated ROI (uROI) and fallback integration region (FIR) can be determined using annotationParamsDiagnostic():

uROI are established as the min/max (rt and m/z) of the found peaks (+/- 5% in RT)
FIR are established as the median of found rtMin, rtMax, mzMin, mzMax

updated_annotation  <- annotationParamsDiagnostic(data_annotation, verbose=TRUE)

# uROI now exist
updated_annotation

outputAnnotationDiagnostic() will save to disk annotationParameters_summary.csv containing the original ROI and newly determined uROI and FIR for manual validation. Additionnaly a diagnostic plot for each compound is saved for reference:

# create a colourScale based on the sampleType
uniq_sType <- sort(unique(spectraMetadata(updated_annotation)$sampleType),na.last=TRUE)
col_sType  <- unname( setNames(c('blue', 'red'),c(uniq_sType))[spectraMetadata(updated_annotation)$sampleType] )

# output fit diagnostic to disk
outputAnnotationDiagnostic(updated_annotation, saveFolder='/output_folder/', savePlots=TRUE, sampleColour=col_sType, verbose=TRUE)

tmp_csv      <- data.frame(matrix(nrow=2,ncol=21,dimnames=list(c(), c('cpdID', 'cpdName', 'X', 'ROI_rt', 'ROI_mz','ROI_rtMin', 'ROI_rtMax', 'ROI_mzMin', 'ROI_mzMax', 'X', 'uROI_rtMin', 'uROI_rtMax', 'uROI_mzMin', 'uROI_mzMax', 'uROI_rt', 'uROI_mz', 'X', 'FIR_rtMin', 'FIR_rtMax', 'FIR_mzMin', 'FIR_mzMax'))), stringsAsFactors=FALSE)
tmp_csv[1,]  <- c('ID-1', 'Cpd 1', '|', 3344.888, 522.2, 3310., 3390., 522.194778, 522.205222, '|', 3305.75893, 3411.436284, 522.194778, 522.205222, 3344.888, 522.2, '|', 3326.10635, 3407.272648, 522.194778, 522.205222)
tmp_csv[2,]  <- c('ID-2', 'Cpd 2', '|', 3385.577, 496.2, 3280., 3440., 496.195038, 496.204962, '|', 3337.376665, 3462.449033, 496.195038, 496.204962, 3385.577, 496.2, '|', 3365.023857, 3453.404957, 496.195038, 496.204962)
tmp_csv[,-c(1,2,3,10,17)]  <- sapply(tmp_csv[,-c(1,2,3,10,17)], as.numeric)
colnames(tmp_csv) <- c('cpdID', 'cpdName', 'X', 'ROI_rt', 'ROI_mz','ROI_rtMin', 'ROI_rtMax', 'ROI_mzMin', 'ROI_mzMax', 'X', 'uROI_rtMin', 'uROI_rtMax', 'uROI_mzMin', 'uROI_mzMax', 'uROI_rt', 'uROI_mz', 'X', 'FIR_rtMin', 'FIR_rtMax', 'FIR_mzMin', 'FIR_mzMax')
pander::pandoc.table(tmp_csv, digits=9)

knitr::include_graphics("../man/figures/parallel_annotation_diagnostic_cpd1.png")

Diagnostic plot for compound 1: The top panel is an overlay of the extracted EIC across all samples with the fitted curve as dotted line. The panel under the EIC represent each found peak RT peakwidth (rtMin, rtMax and apex marked as dot), ordered with the first sample at the top. The bottom 3 panels represent found RT (peakwidth), m/z (peakwidth) and peak area by run order, with the corresponding histograms to the right

ROI exported to .csv can be updated based on the diagnostic plots; uROI (updated ROI potentially used for all samples) and FIR (fallback integration regions for when no peak is found) can also be tweaked to better fit the peaks.

New Initialisation with Updated Parameters to be Applied to All Study Samples

Following this manual validation of the fit on reference samples, the modified parameters in the .csv file can be reloaded and applied to all study samples.

Load new fit parameters

peakPantheR_loadAnnotationParamsCSV() will load the new .csv parameters (as generated by outputAnnotationDiagnostic()) and initialise a peakPantheRAnnotation object without spectraPaths, spectraMetadata or cpdMetadata which will need to be added before annotation. useUROI and useFIR are set to FALSE and will need to be set accordingly. uROIExist is established depending on the uROI columns present in the .csv and will only be set to TRUE if no NA are present.

update_csv_path <- '/path_to_new_csv/'

# load csv
new_annotation <- peakPantheR_loadAnnotationParamsCSV(update_csv_path)
#> uROIExist set to TRUE
#> New peakPantheRAnnotation object initialised for 2 compounds

new_annotation
#> An object of class peakPantheRAnnotation
#>  2 compounds in 0 samples. 
#>   updated ROI exist (uROI)
#>   does not use updated ROI (uROI)
#>   does not use fallback integration regions (FIR)
#>   is not annotated

Add new samples to process

Now that the fit parameters were set on the QC samples, the same processing can be applied to all study samples. resetAnnotation() will reinitialise all the results and modify the samples or compounds targeted if required:

## new files
new_spectraPaths  <- c(system.file('cdf/KO/ko15.CDF', package = "faahKO"),
                       system.file('cdf/WT/wt15.CDF', package = "faahKO"),
                       system.file('cdf/KO/ko16.CDF', package = "faahKO"),
                       system.file('cdf/WT/wt16.CDF', package = "faahKO"),
                       system.file('cdf/KO/ko18.CDF', package = "faahKO"),
                       system.file('cdf/WT/wt18.CDF', package = "faahKO"))

new_spectraPaths

## new spectra metadata
new_spectraMetadata  <- data.frame(matrix(c("KO", "WT", "KO", "WT", "KO", "WT"), 6, 1, dimnames=list(c(), c("Group"))), stringsAsFactors=F)

new_spectraMetadata  <- data.frame(matrix(c("KO", "WT", "KO", "WT", "KO", "WT"), 6, 1, dimnames=list(c(), c("Group"))), stringsAsFactors=F)
pander::pandoc.table(new_spectraMetadata)

new_annotation <- resetAnnotation(updated_annotation, spectraPaths=new_spectraPaths, spectraMetadata=new_spectraMetadata, useUROI=TRUE, useFIR=TRUE, verbose=FALSE)

## add new samples to the annotation loaded from csv, useUROI, useFIR

new_annotation <- resetAnnotation(new_annotation, spectraPaths=new_spectraPaths, spectraMetadata=new_spectraMetadata, useUROI=TRUE, useFIR=TRUE)
#> peakPantheRAnnotation object being reset:
#>   Previous "ROI", "cpdID" and "cpdName" value kept
#>   Previous "uROI" value kept
#>   Previous "FIR" value kept
#>   Previous "cpdMetadata" value kept
#>   New "spectraPaths" value set
#>   New "spectraMetadata" value set
#>   Previous "uROIExist" value kept
#>   New "useUROI" value set
#>   New "useFIR" value set

new_annotation

Run Final Parallel Annotation

Run the final annotation:

# annotate files serially
new_annotation_result <- peakPantheR_parallelAnnotation(new_annotation, ncores=0, verbose=FALSE)

# successful fit
nbSamples(new_annotation_result$annotation)

final_annotation      <- new_annotation_result$annotation
final_annotation

# list failed fit
new_annotation_result$failures

Output final results

The final fits can be saved to disk with outputAnnotationDiagnostic():

# create a colourScale based on the sampleType
uniq_group <- sort(unique(spectraMetadata(final_annotation)$Group),na.last=TRUE)
col_group  <- unname( setNames(c('blue', 'red'),c(uniq_sType))[spectraMetadata(final_annotation)$Group] )

# output fit diagnostic to disk
outputAnnotationDiagnostic(final_annotation, saveFolder='/final_output_folder/', savePlots=TRUE, sampleColour=col_group, verbose=TRUE)

For each processed sample, a peakTables contains all the fit information for all compounds targeted. annotationTable( , column) will group the values across all samples and compounds for any peakTables column:

# peakTables for the first sample
peakTables(final_annotation)[[1]]

pander::pandoc.table(peakTables(final_annotation)[[1]])

# Extract the found peak area for all compounds and all samples
annotationTable(final_annotation, column='peakArea')

pander::pandoc.table(annotationTable(final_annotation, column='peakArea'))

Finally all annotation results can be saved to disk as .csv with outputAnnotationResult(). These .csv will contain the compound metadata, spectra metadata and a file for each column of peakTables (with samples as rows and compounds as columns):

# save
outputAnnotationResult(final_annotation, saveFolder='/final_output_folder/', annotationName='ProjectName', verbose=TRUE)
#> Compound metadata saved at /final_output_folder/ProjectName_cpdMetadata.csv
#> Spectra metadata saved at /final_output_folder/ProjectName_spectraMetadata.csv
#> Peak measurement "found" saved at /final_output_folder/ProjectName_found.csv
#> Peak measurement "rtMin" saved at /final_output_folder/ProjectName_rtMin.csv
#> Peak measurement "rt" saved at /final_output_folder/ProjectName_rt.csv
#> Peak measurement "rtMax" saved at /final_output_folder/ProjectName_rtMax.csv
#> Peak measurement "mzMin" saved at /final_output_folder/ProjectName_mzMin.csv
#> Peak measurement "mz" saved at /final_output_folder/ProjectName_mz.csv
#> Peak measurement "mzMax" saved at /final_output_folder/ProjectName_mzMax.csv
#> Peak measurement "peakArea" saved at /final_output_folder/ProjectName_peakArea.csv
#> Peak measurement "maxIntMeasured" saved at /final_output_folder/ProjectName_maxIntMeasured.csv
#> Peak measurement "maxIntPredicted" saved at /final_output_folder/ProjectName_maxIntPredicted.csv
#> Peak measurement "is_filled" saved at /final_output_folder/ProjectName_is_filled.csv
#> Peak measurement "ppm_error" saved at /final_output_folder/ProjectName_ppm_error.csv
#> Peak measurement "rt_dev_sec" saved at /final_output_folder/ProjectName_rt_dev_sec.csv
#> Peak measurement "tailingFactor" saved at /final_output_folder/ProjectName_tailingFactor.csv
#> Peak measurement "asymmetryFactor" saved at /final_output_folder/ProjectName_asymmetryFactor.csv
#> Summary saved at /final_output_folder/ProjectName_summary.csv

peakPantheR
Peak Picking and Annotation of High Resolution Experiments

Parallel Annotation"
In peakPantheR: Peak Picking and Annotation of High Resolution Experiments

Parallel Annotation Concept

Parallel Annotation Example

Input Data

Initialise and Run Parallel Annotation

Process Parallel Annotation Results

New Initialisation with Updated Parameters to be Applied to All Study Samples

Load new fit parameters

Add new samples to process

Run Final Parallel Annotation

Output final results

See Also

Try the peakPantheR package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

peakPantheR Peak Picking and Annotation of High Resolution Experiments

Parallel Annotation" In peakPantheR: Peak Picking and Annotation of High Resolution Experiments

Parallel Annotation Concept

Parallel Annotation Example

Input Data

Initialise and Run Parallel Annotation

Process Parallel Annotation Results

New Initialisation with Updated Parameters to be Applied to All Study Samples

Load new fit parameters

Add new samples to process

Run Final Parallel Annotation

Output final results

See Also

Try the peakPantheR package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

peakPantheR
Peak Picking and Annotation of High Resolution Experiments

Parallel Annotation"
In peakPantheR: Peak Picking and Annotation of High Resolution Experiments