Installation

if(!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("ptairMS")

Introduction

The ptairMS package provides a workflow to process PTR-TOF-MS raw data in the open Hierarchical Data Format 5 (HDF5; .h5 extension), and generate the peak table as an ExpressionSet object for subsequent data analysis with the many methods and packages available in R. Applications include the analysis of exhaled breath, cell culture headspace or ambient air. The package offers several features to check the raw data and tune the few processing parameters. It also enables to include new samples in a study without re-processing all the previous data, providing a convenient management for cohort studies (e.g. duration of the inclusion process, longitudinal studies, etc.).

Proton Transfer Reaction - Mass Spectrometry (PTR-MS) has emerged with excellent sensitivity and specificity for VOC analysis in a wide range of applications, including environment, food quality, and biology (Blake, Monks, and Ellis 2009). In the area of health and care, PTR-MS opens up unique opportunities for real-time analysis at the patient’s bedside.

Volatolomics

The characterization of volatile organic compounds (VOCs) emitted by living organisms is of major interest in medicine, food sciences, and ecology. As an example, thousands of VOCs have been identified in the exhaled breath, resulting from normal metabolism or pathological processes (Lacy Costello et al. 2014). The main advantage of breath analysis in medicine is that the sampling is non-invasive (Devillier et al. 2017). Methods based on mass spectrometry (MS) are the reference technologies for VOC analysis because of their sensitivity and large dynamic range.

The ptairMS processing workflow

The workflow consists of five steps:

createPtrSet: A ptrSet object is generated by taking as input the name of the directory containing the raw files (in HDF5 format), possibly grouped into subfolders according to classes of samples
detectPeak: peak detection and quantification are performed within each file and the ptrSet object is updated with the sample metadata, the peak list for each sample, and several quality metrics
alignSamples: The peak lists are aligned between samples and an ExpressionSet object is returned, containing the table of peak intensities, the sample metadata, and the feature metadata (which can be accessed with the exprs, pData and fData methods from the Biobase package, respectively)
imputing: Missing values in the table of intensities may be replaced by the integrated signal in the expected raw data region
annotateVOC: Features may be annotated, based on the Human Breathomics Database (Kuo et al. 2020)

Datasets (ptairData package)

Two real PTR-TOF-MS raw data sets are available in the ptairData package.

exhaledAir: Exhaled air from two healthy individual: three acquisitions per individual (with two expirations per acquisition), on distinct days [6 files]
mycobacteria: cell culture headspace: two replicates from two species and one control (culture medium) [6 files]

Note: To limit the size of the data and speed up the analysis, the raw data are truncated in the mass dimension within the range [20.4, 21.6] ∪ [50, 150] for individuals, and [20.4, 21.6] ∪ [56.4, 90.6] for mycobacteria.

Hands on

library(ptairMS)
library(ptairData)

Graphical Interface

The whole workflow of ptairMS can be run interactively through a graphical user interface, which provides visualizations (expiration phases, peaks in the raw data, peak table, individual VOCs), quality controls (calibration, resolution, peak shape and evolution of reagent ions depending on time), and exploratory data analysis.

ptairMS::RunShinnyApp()

Alternatively, the workflow can be run on the command line. We describe hereafter the main steps and parameters.

`createPtrSet`: Checking raw data and setting parameters

The analysis starts from a directory containing the raw data. For this example, we use the exhaledAir from the ptairData package:

dirRaw <- system.file("extdata/exhaledAir", package = "ptairData")

Before processing the data, there are two important steps:

calibrate the mass axis
determine the time periods of interest within the acquisitions (i.e., expirations or headspace analysis duration in our examples)

To perform these steps and check the quality of the raw data, we first use the createPtrSet function which, for each file:

reads the raw data
performs calibration on the calibrationPeriod sum spectra, with the mzCalibRef reference masses (see the Calibrating the mass axis section)
determines the expiration time limits (or headspace duration for bacteria example) on the trace of mzBreathTracer mass or on the total sum trace if this parameter is NULL, with the fracMaxTIC parameter (see the Determine the time limits for expirations or headspace duration section)
builds a default sampleMetadata table with the file names, date and possibly the subdirectories

This function creates a unique ptrSet object for the whole study, which contains all necessary information about the files: calibration parameters, time limits, peak lists (at this step they are empty), and primary ion quantification. The corresponding raw files are available in the ptairData companion package.

Importantly, the ptrSet may be updated if new files are added or deleted from the directory, with the function updatePtrSet (see the Updating the ptrSet section).

To create a ptrSet object form the raw data directory:

exhaledPtrset <- createPtrSet(dir=dirRaw,
                     setName="exhaledPtrset",
                     mzCalibRef = c(21.022, 60.0525),fracMaxTIC = 0.7,
                     saveDir = NULL )

## ind1-1.h5 check

## ind1-2.h5 check

## ind1-3.h5 check

## ind2-1.h5 check

## ind2-2.h5 check

## ind2-3.h5 check

exhaledPtrset

## ptrSet object : exhaledPtrset 
## directory: /github/workspace/pkglib/ptairData/extdata/exhaledAir 
##     6 files contained in the directory 
##     6 files checked 
##     0 files processed by detectPeak

To get the list of the file names in your directory (at the last time the ptrSet was created or updated), use getFileNames:

getFileNames(exhaledPtrset)

## [1] "ind1-1.h5" "ind1-2.h5" "ind1-3.h5" "ind2-1.h5" "ind2-2.h5" "ind2-3.h5"

To check the quality and view useful information about your files, you can use the plot method on the ptrSet, that provides four plots:

calibError: the calibration error boxplot in ppm
$\frac{m}{\Delta m}$: an estimation of the resolution for the mzCalibRef peaks
the average normalized shape of the mzCalibRef peaks
The primary ion isotope intensity in cps in function of acquisition date

plot(exhaledPtrset)

We will now quickly explain each step of this function, and show how to choose and check the quality of the mzCalibRef, mzBreathTracer and fracMaxTIC parameters.

Calibrating the mass axis

To convert Time Of Flight (TOF) axis to mass-to-charge ratio (mz), the following formula is used (Müller et al. 2013):

$mz = \Big (\frac{TOF-b}{a} \Big)^2$

To estimate the parameters (a,b), reference peaks with known masses (and without overlapping peaks) are needed. For optimal results, the calibration peaks have to be well distributed over the entire m/z axis. The masses of the calibration peaks are set with the mzCalibRef parameter, in an numeric vector. For exhaled air, we propose by default 6 peaks (two of them are suggested by the IONICON manufacturer):

calib_table<-read.csv(system.file("extdata", "reference_tables/calib_table.tsv", package = "ptairMS"),sep="\t")
knitr::kable(calib_table)

Formula	mz	Compound
H3 (18O)+	21.02204	Isotope of primary ion (H3O+)
N2H+	29.01342	Diazote
C3H5+	41.03858	Allyl cation
C3H7O+	60.05250	Isotope of Acetone
C6H6I+	203.94300	Iodobenzene (1,3-diiodobenzene fragment)
C6H5I2+	330.84950	1,3-diiodobenzene (instrument internal standard)

Other reference masses (at least two) can be provided by the user.

Since there may be a drift of mass calibration of the instrument during the acquisition, e.g. due to low change of temperature, periodic calibration is performed every calibrationPeriods seconds (default 60): correction of the drifts is then performed by linear interpolation, by using the first calibration as the reference.

If a mass contains one or several outlier values on the summary plot (i.e. an error > 20 ppm) for specific files, you can check those files with the function plotCalib that plots the Average total ion spectrum around all the mzCalibRef (with the exact masses as red vertical lines), after calibration drift correction.

plotCalib(exhaledPtrset,fileNames=getFileNames(exhaledPtrset)[1])

You can then choose to keep or delete these masses from mzCalibRef. To change the calibration masses, use the calibration function on the ptrSet object.

In our dataset example, since we have truncated the mass axis, only two masses from those listed above are present. To calibrate with three masses, we therefore add the peak 75.04406 ([C3H6O2+H]+, Hydroxyacetone).

exhaledPtrset <- calibration(exhaledPtrset, mzCalibRef =  c(21.022, 60.0525,75.04406))
plot(exhaledPtrset,type="calibError")

Determine the time limits of expirations or headspace duration

To see and change the expirations or headspace duration, use the changeTimeLimits function that opens the Shiny application for interactive visualization and selection, or use the timeLimits function.

The limits are determined on the total trace if mzBreathTracer is NULL, or on the trace around mzBreathTracer mass. They correspond to the part of the trace where the intensity is higher than fracMaxTIC * max(TIC), after baseline removal if baseline is set to TRUE.

Note: To analyze the entire spectrum (e.g. in ambient air studies), set fracMaxTIC = 0.

exhaledPtrset <- changeTimeLimits(exhaledPtrset)

Example of expiration detection at fracMaxTIC = 0.5 on a single file:

samplePath <-getFileNames(exhaledPtrset,fullNames = TRUE)[1]
sampleRaw <- readRaw(samplePath, calib = FALSE)
expirationLimit <- timeLimits(sampleRaw,fracMaxTIC =  0.5,plotDel = TRUE, mzBreathTracer = 60.05)

expirationLimit <- timeLimits(sampleRaw,fracMaxTIC =  0.9,plotDel = TRUE,mzBreathTracer = NULL)

Note: You can also see the expiration limits on all or several files from the ptrSet with the plotTIC function. By default, when fileNames is NULL, all TICs files are plotted. A pdf file may be generated by setting the pdfFile argument to the absolute file path ending with the .pdf extension. Finally, you can remove the baseline by setting baselineRm = TRUE, and add the time limits to the plot by setting showLimits = TRUE. Coloring the TICs according to a column from the sampleMetadata is also possible by indicating the column name as the colorBy parameter.

plotTIC(object = exhaledPtrset,baselineRm = TRUE,type = "ggplot")

Managing sample metadata

The createPtrSet function automatically generates a default sampleMetadata data.frame. It contains the file names as row names, a column named ‘subfolder’ when the files are organized into subfolders in the parent directory, and a column date with the date and hour of the acquisition. To get this data frame, use the getSampleMetadata method. Remember that the row names of the sampleMetadata must always correspond to all the file names from the directory.

getSampleMetadata(exhaledPtrset)

##           subfolder                date
## ind1-1.h5      ind1 21/02/2019 11:52:49
## ind1-2.h5      ind1 27/02/2019 11:41:24
## ind1-3.h5      ind1 28/02/2019 11:28:29
## ind2-1.h5      ind2 07/02/2019 10:29:33
## ind2-2.h5      ind2 18/02/2019 11:06:52
## ind2-3.h5      ind2 22/02/2019 09:52:20

You can at any moment obtain this default sample metadata with the function resetSampleMetadata(exhaledPtrset).

To modify the sample metadata, there exists two different ways:

setSampleMetadata method: modify the data frame in your R session and set it back into the ptrSet object.

sampleMD <- getSampleMetadata(exhaledPtrset)
colnames(sampleMD)[1] <- "individual"  

exhaledPtrset <- setSampleMetadata(exhaledPtrset,sampleMD)
getSampleMetadata(exhaledPtrset)

##           individual                date
## ind1-1.h5       ind1 21/02/2019 11:52:49
## ind1-2.h5       ind1 27/02/2019 11:41:24
## ind1-3.h5       ind1 28/02/2019 11:28:29
## ind2-1.h5       ind2 07/02/2019 10:29:33
## ind2-2.h5       ind2 18/02/2019 11:06:52
## ind2-3.h5       ind2 22/02/2019 09:52:20

exportSampleMetada and importSampleMetadata methods: exportSampleMetada saves the data.frame in the .tsv format in the directory of your choice. The parameter saveFile must always have the .tsv extension. You can open and modify it on any spreadsheet editor, but row names must never be modified: if one of the files happens to be missing from the row names during the subsequent import, an error will be thrown. Once the .tsv file has been modified, it can be imported back into the ptrSet object, with the function importSampleMetadata.

exportSampleMetada(exhaledPtrset, saveFile = file.path(DirBacteria,"sampleMetadata.tsv"))
exhaledPtrset <- importSampleMetadata(exhaledPtrset, file = file.path(DirBacteria,"sampleMetadata.tsv"))

Saving

When calling the createPtrSet function, you may use the saveDir argument to save the ptrSet object in the directory of your choice, with setName parameter as name (the .RData extension will automatically be added at the end of the file name). Subsequent import of the saved ptrSet object relies on the classical load function.

Plot raw data

There exist two functions for plotting the raw data:

plotRaw: Displays for a file the image of the matrix of intensities, the Extracted Ion Chromatogram (EIC), the Extracted Ion Spectrum (EIS), for the selected m/z and time ranges, and all the putative annotations available in the vocDB within this mz range (no peak detection has yet been done as this step)

plotRaw(exhaledPtrset, mzRange = 59 , fileNames = getFileNames(exhaledPtrset)[1],showVocDB = TRUE)

##    ion_mass ion_formula                           name_iupac
## 40 59.08552  [C4H10+H]+               butane|2-methylpropane
## 39 59.06037 [C2H6N2+H]+                      dimethyldiazene
## 38 59.04914  [C3H6O+H]+ prop-2-en-1-ol|propanal|propan-2-one
## 37 58.94261     [Ni+H]+                               nickel

plotFeatures: for all selected files, the average spectrum for all time periods is plotted, with the background superimposed as a dashed line. As with the plotTIC function, you can choose the type of display (plotly or ggplot), and the possibility to color spectra individually by indicating a column name of the sample Metadata

plotFeatures(exhaledPtrset,mz=59.049,type="ggplot",colorBy = "individual")

`updatePtrSet`: Updating the ptrSet

If you delete or add files to the directory after the ptrSet object has been created, the updatePtrSet function must be run.

exhaledPtrset <- updatePtrSet(exhaledPtrset)

## nothing to update

`detectPeak`: Peak detection and quantification

Now that we have checked that the calibration is efficient, and that the expiration or headspace time limits are correct, the peaks can be detected and quantified in each file with the detectPeak function, which works on the ptrSet object (and the corresponding raw files).

For each file in the directory, this function:

performs the calibration of the mass axis every calibrationPeriod second
detects peaks on the total ion spectrum with an untargeted peak picking algorithm
estimates the temporal evolution of each detected peak with a 2-dimensional model
quantifies each peak in both exhaled air and background, in cps, ncps (normalized by the primary ion, H3(O18)+ * 488) and ppb [(Hansel et al. 1995)] units, and stores the corresponding information in the peakList slot of the ptrSet object as a list of ExpressionSet objects (see below)
performs two unilateral t-test comparing the mean intensities between background and expiration/headspace phases, and stores the p-value in the peakListAligned slot

The peakList is then written in the ptrSet object as a list of ExpressionSet, each containing all the peaks detected in one file (e.g., sample; see below for the details of the ExpressionSet content).

exhaledPtrset <- detectPeak(exhaledPtrset)

## ind1-1.h5 : 154 peaks detected 
## ind1-2.h5 : 164 peaks detected 
## ind1-3.h5 : 140 peaks detected 
## ind2-1.h5 : 162 peaks detected 
## ind2-2.h5 : 177 peaks detected 
## ind2-3.h5 : 161 peaks detected

The peak detection may be restricted to specific nominal masses with the mzNominal argument: for example exhaledPtrset <- detectPeak(exhaledPtrset , mzNominal = c(5,60)).

The peak detection step may take a few minutes if there are many files and a large m/z range (1 to 2 minutes for files for files with an average acquisition time of 3 minutes) . Parallel computing is available by setting parallelize = TRUE and by giving the number of available cores of your computer in nbCores. To find the number of CPU cores available in your computer, use parallel::detectCores().

To see the resulting peak lists, use the getPeakList method. It returns a list of ExpressionSet, where:

assay Data: the matrix of peak intensities, with m/z peak centre as row names, and the quantification in cps at each time point
feature Data: the data frame with m/z peak centre as row names, and the following columns:
- quanti_unit: the mean of the quantifications for all expiration/headspace phases
- background_unit: the mean of the quantifications for all background phases
- diffAbs_unit: the mean of the quantifications for all expiration/headspace phases after baseline subtraction
- pValLess/ pValGreater: the p-value of the unilateral t-test, which tests that quantification (in cps) of expiration points are less/greater than the intensity of the background.
- lower/upper: integration boundaries
- parameter peak: estimated peak parameters (width et left and rigth and height)

peakList<-getPeakList(exhaledPtrset)
peakList1<-peakList$`ind1-1.h5`
X<-Biobase::exprs(peakList1)
Y<-Biobase::fData(peakList1)
mz<-Y[,"Mz"]
plot(X[which.min(abs(mz-59.0498)),],ylab="cps",xlab="time",main=paste("Temporal evolution of acetone "))

head(Y)

##               Mz  lowerMz  upperMz overlap parameterPeak.delta1
## 21.022  21.02199 21.01237 21.03169       0          0.002627749
## 51.0442 51.04420 51.02412 51.06458       0          0.005044793
## 53.0031 53.00311 52.97578 53.03075       1          0.006625388
## 53.0388 53.03880 53.01560 53.06232       1          0.004657318
## 54.0076 54.00762 53.97978 54.03579       1          0.006750953
## 54.036  54.03596 54.00811 54.06414       1          0.006754495
##         parameterPeak.delta2 parameterPeak.height quanti_cps background_cps
## 21.022           0.002033698            816.75081  3974.1939      4640.8017
## 51.0442          0.004725133            442.09372  5558.3477       122.7511
## 53.0031          0.006625388            101.42118   696.9602      1414.9976
## 53.0388          0.006629476             88.69103   757.6235       708.0097
## 54.0076          0.006750953             20.54174   197.6101       247.6568
## 54.036           0.006754495             17.77483   124.2098       260.0253
##         diffAbs_cps     pValLess  pValGreater  quanti_ncps background_ncps
## 21.022    602.36010 2.729988e-13 1.000000e+00 1.895138e-03    0.0022130174
## 51.0442  5386.01378 1.000000e+00 4.256174e-23 2.650559e-03    0.0000585352
## 53.0031   818.19828 2.856432e-11 1.000000e+00 3.323531e-04    0.0006747572
## 53.0388   223.18254 1.000000e+00 1.206099e-01 3.612811e-04    0.0003376222
## 54.0076    15.58893 7.769693e-04 1.000000e+00 9.423254e-05    0.0001180979
## 54.036    147.33833 1.271412e-09 1.000000e+00 5.923082e-05    0.0001239959
##         diffAbs_ncps  quanti_ppb background_ppb diffAbs_ppb
## 21.022  2.872420e-04 45.30682241    52.57848477 6.824506409
## 51.0442 2.568380e-03  2.81161476     0.06170721 2.707559808
## 53.0031 3.901669e-04  0.33808927     0.68214957 0.394441388
## 53.0388 1.064271e-04  0.36751649     0.34132110 0.107593030
## 54.0076 7.433754e-06  0.09393274     0.11699268 0.007364187
## 54.036  7.025991e-05  0.05904238     0.12283554 0.069602391

Updating the `ptrSet` peak lists with `detectPeak`

As described previously, an important feature of the ptairMS package is the possibility to update the ptrSet object linked to a directory, as new data files are added and included in this directory. In such a case, we have seen that the updatePtrSet function must be used to reset the sample metadata or append it. Then, the detectPeak function must be used on the updated ptrSet to compute the additional peak lists.

exhaledPtrset<-updatePtrSet(exhaledPtrset)
exhaledPtrset<-setSampleMetadata(exhaledPtrset,resetSampleMetadata(exhaledPtrset))
exhaledPtrset<-detectPeak(exhaledPtrset)

`alignSamples`: Aligning features between samples

The alignment between samples (i.e. the matching of variables between the peak lists within the ptrSet object) is performed by using a kernel gaussian density (Delabrière et al. 2017).

The alignSamples returns an ExpressionSet object, with the table of peak intensities which has just been built, the sample meta data (borrowed from the input ptrSet) and the variable meta data which contains peak intensities in the background.

It is possible to apply two filters:

On the background: only variables with a significant higher intensity in the expiration phases compared to the background phases (at the pValGreaterThres p-value threshold) for fracExp % of the samples are kept
On sample reproducibility: only variables which are detected in more than fracGroup percent of the samples (or percent of at least one group) are kept.

If you do not want to apply those filters, set fracGroup to 0 and pValGreaterThres to 1.

exhaledEset <- alignSamples(exhaledPtrset, group="individual", fracGroup = 1, fracExp=1/6)

## 113 peaks aligned

The three tables from the ExpressionSet can be accessed with the classical exprs, pData, and fData accessors:

knitr::kable(head(Biobase::exprs(exhaledEset)))

	ind1-1.h5	ind1-2.h5	ind1-3.h5	ind2-1.h5	ind2-2.h5	ind2-3.h5
51.0442	2.8116148	2.7230144	2.3933849	8.2962850	6.9375697	10.9301556
52.0473	NA	NA	NA	0.1268996	0.1101952	0.1610980
53.0029	0.3380893	0.4097092	0.3345380	0.3943865	0.6936014	0.4844526
53.0138	NA	NA	NA	0.1195139	0.1170446	0.1568373
53.0388	0.3675165	0.3534616	0.3584981	0.6067432	0.7399354	0.7080680
54.0085	0.0939327	0.0945617	NA	0.1446772	0.2594131	0.1919683

knitr::kable(head(Biobase::pData(exhaledEset)))

	individual	date
ind1-1.h5	ind1	21/02/2019 11:52:49
ind1-2.h5	ind1	27/02/2019 11:41:24
ind1-3.h5	ind1	28/02/2019 11:28:29
ind2-1.h5	ind2	07/02/2019 10:29:33
ind2-2.h5	ind2	18/02/2019 11:06:52
ind2-3.h5	ind2	22/02/2019 09:52:20

knitr::kable(head(Biobase::fData(exhaledEset)))

	ion_mass	Background - ind1-1.h5	Background - ind1-2.h5	Background - ind1-3.h5	Background - ind2-1.h5	Background - ind2-2.h5	Background - ind2-3.h5
51.0442	51.0442	0.0617072	0.0372129	0.0373825	0.0699982	0.0228573	0.1021184
52.0473	52.0473	NA	NA	NA	0.0095295	0.0055960	0.0102491
53.0029	53.0029	0.6821496	0.4545806	0.2022318	0.9540640	0.5317314	0.6168134
53.0138	53.0138	NA	NA	NA	0.1546882	-0.0016491	0.1394140
53.0388	53.0388	0.3413211	0.3598626	0.2159814	0.1840640	0.3216666	0.3652356
54.0085	54.0085	0.1169927	0.0806165	NA	0.2357813	0.0953313	0.1249523

Note: The view method from the ropls package may be used to print these tables:

`imputing`: Imputation of missing values

To impute missing values, ptairMS returns back to the raw data, and performs the quantification with the same method as detectPeak but this time without any limit of detection.

exhaledEset <- ptairMS::impute(exhaledEset,  exhaledPtrset)

## ind1-1.h5 done

## ind1-2.h5 done

## ind1-3.h5 done

## ind2-1.h5 done

## ind2-2.h5 done

`annotateVOC`: Annotation

ptairMS provides putative annotations by matching the measured ion masses to the Human Breathomics Database (Kuo et al. 2020). Applied to an ExpressionSet, it appends the feature metadata (fData) with new columns containing chemical information (formulas, IUPAC name, InChI, etc.), as well as the isotopes for nuclides C13, O17 and O18.

annotateVOC(59.049)

##        vocDB_ion_mass vocDB_ion_formula                       vocDB_name_iupac
## 59.049       59.04914        [C3H6O+H]+ prop-2-en-1-ol, propan-2-one, propanal

exhaledEset<-annotateVOC(exhaledEset)

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## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
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## NOTE: You are sure that is the mass of an electrone?
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## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?
## NOTE: You are sure that is the mass of an electrone?

knitr::kable(head(Biobase::fData(exhaledEset)))

	ion_mass	Background - ind1-1.h5	Background - ind1-2.h5	Background - ind1-3.h5	Background - ind2-1.h5	Background - ind2-2.h5	Background - ind2-3.h5	vocDB_ion_mass	vocDB_ion_formula	vocDB_name_iupac	isotope
51.0442	51.0442	0.0617072	0.0372129	0.0373825	0.0699982	0.0228573	0.1021184				53.0388/52.0473
52.0473	52.0473	NA	NA	NA	0.0095295	0.0055960	0.0102491				NA
53.0029	53.0029	0.6821496	0.4545806	0.2022318	0.9540640	0.5317314	0.6168134				54.0085
53.0138	53.0138	NA	NA	NA	0.1546882	-0.0016491	0.1394140				NA
53.0388	53.0388	0.3413211	0.3598626	0.2159814	0.1840640	0.3216666	0.3652356	53.03857	[C4H4+H]+	but-1-en-3-yne	NA
54.0085	54.0085	0.1169927	0.0806165	NA	0.2357813	0.0953313	0.1249523				NA

`writeEset`: Export data and metadata to 3 tabular files

Finally, the ExpressionSet can be exported to 3 tabulated files ‘dataMatrix.tsv’, sampleMetadata.tsv’, and ‘variableMetadata.tsv’:

writeEset(exhaledEset, dirC = file.path(getwd(), "processed_dataset"))

Statistical analysis

The ExpressionSet object is now ready for subsequent data analysis (e.g. data mining, classification or feature selection) with the many R packages.

As an example, we describe here how to perform Exploratory Data Analysis with the ropls package (Thevenot 2015).

As a preliminary step, log transformation is often used in Mass Spectrometry to stabilize the variance:

Biobase::exprs(exhaledEset) <- log2(Biobase::exprs(exhaledEset))

Then, the data and metadata may be printed and plotted with the view method:

ropls::view(Biobase::exprs(exhaledEset),printL=FALSE)

To avoid redundancy, the isotopes may be discarded:

isotopes<-Biobase::fData(exhaledEset)[,"isotope"]
isotopes<-isotopes[!is.na(isotopes)]
exhaledEset <- exhaledEset[!(Biobase::fData(exhaledEset)[, "ion_mass"] %in% isotopes), ]

Principal Component Analysis may then be performed (due to the limited number of samples, cross-validation is decreased to 5 instead of 7):

exhaledPca<-ropls::opls(exhaledEset,crossvalI=5,info.txtC="none",fig.pdfC="none")
ropls::plot(exhaledPca, parAsColFcVn=Biobase::pData(exhaledEset)[, "individual"],typeVc="x-score")

Subsequent supervised analysis, e.g. (Orthogonal) Partial Least Squares, or feature selection may be performed with the ropls and biosigner packages, respectively.

To get the most important variable that contribute to the dimension that discriminate the two individual (here the first dimension), we look at the loadings:

load1<-ropls::getLoadingMN(exhaledPca)[,1]
barplot(sort(abs(load1),decreasing = TRUE))

knitr::kable(Biobase::fData(exhaledEset)[names(sort(abs(load1),decreasing = TRUE)[1:10]),c("vocDB_ion_formula","vocDB_name_iupac")])

	vocDB_ion_formula	vocDB_name_iupac
110.9705
53.0388	[C4H4+H]+	but-1-en-3-yne
67.0542	[C5H6+H]+	(E)-pent-3-en-1-yne, 2-methylbut-1-en-3-yne, cyclopenta-1,3-diene, pent-3-en-1-yne
135.1139	[C10H14+H]+	1,2,3,4-tetramethylbenzene, 1,2,3,5-tetramethylbenzene, 1,2,4,5-tetramethylbenzene, 1,3-diethylbenzene, 1,4-diethylbenzene, 1-ethyl-2,3-dimethylbenzene, 1-ethyl-2,4-dimethylbenzene, 1-ethyl-3,5-dimethylbenzene, 1-methyl-2-propan-2-ylbenzene, 1-methyl-2-propylbenzene, 1-methyl-3-propan-2-ylbenzene, 1-methyl-4-propan-2-ylbenzene, 1-methyl-4-propylbenzene, 2-ethyl-1,4-dimethylbenzene, 2-methyl-5-prop-1-en-2-ylcyclohexa-1,3-diene, 4-ethyl-1,2-dimethylbenzene, butylbenzene
69.0704	[C5H8+H]+	(3E)-penta-1,3-diene, (3Z)-penta-1,3-diene, 2-methylbuta-1,3-diene, 3-methylbuta-1,2-diene, pent-2-yne, penta-1,2-diene, penta-1,4-diene
129.127	[C8H16O+H]+	2-ethylhexanal, 3-methylheptan-2-one, 6-methylheptan-2-one, 6-methylheptan-3-one, octan-2-one, octan-3-one, octanal
109.0652	[C7H8O+H]+	2-prop-2-enylfuran, 3-methylphenol, 4-methylphenol, phenylmethanol
52.0473
63.0443
143.0969

We then could plot the raw data around this masses to check the robustness of these potential marker:

plotFeatures(exhaledPtrset,mz = 53.0387,typePlot = "ggplot",colorBy = "individual")

plotFeatures(exhaledPtrset,mz = 67.0539,typePlot = "ggplot",colorBy = "individual")

Mycobacteria dataset

The package can also be used to process data from headspace analysis (e.g., cell culture headspace). The acquisition phase is detected on the total ion trace, in a similar way as for exhaled air. The mycobacteria dataset contains two replicates from two bacterial species and one control (culture medium). The 6 corresponding raw files are available in the companion ptairData package.

dir <- system.file("extdata/mycobacteria",  package = "ptairData")
mycobacteriaSet <- createPtrSet(dir = dir, setName = "test", 
                         mzCalibRef = c(21.022,59.049))

## Control1.h5 check

## Control2.h5 check

## Specie-a1.h5 check

## Specie-a2.h5 check

## specie-b1.h5 check

## specie-b2.h5 check

mycobacteriaSet <- detectPeak(mycobacteriaSet, smoothPenalty = 0)

## Control1.h5 : 36 peaks detected 
## Control2.h5 : 38 peaks detected 
## Specie-a1.h5 : 38 peaks detected 
## Specie-a2.h5 : 39 peaks detected 
## specie-b1.h5 : 51 peaks detected 
## specie-b2.h5 : 41 peaks detected

plotTIC(mycobacteriaSet,type="ggplot",showLimits = TRUE,file="Specie-a2.h5")

eSet <- alignSamples(mycobacteriaSet)

## 30 peaks aligned

eSet<-impute(eSet,ptrSet = mycobacteriaSet)

## specie-b1.h5 done

## specie-b2.h5 done

X<-Biobase::exprs(eSet)
pca<-ropls::opls(log2(t(X)),predI =2,crossvalI=5,info.txtC = "none",
  fig.pdfC = "none")
ropls::view(log2(X),printL=FALSE)

plot(pca,type="x-score")

Processing a single raw file

To read and access the entire raw data of a single file, use the readRaw function. It opens the data from an absolute file path with the rhdf5 library, and returns a ptrRaw object containing the raw data matrix, the time axis, the m/z axis obtained after calibration if calibTIS = TRUE, and additional information contained in the h5 file (transmission curve, drift temperature, …).

As an example, we get the absolute file path for the first raw file:

dirRaw <- system.file("extdata/exhaledAir", package = "ptairData")
samplePath<-list.files(dirRaw,recursive = TRUE,full.names = TRUE,pattern = ".h5$")[1]

To read the file:

sampleRaw <- readRaw(samplePath, calib = FALSE)
sampleRaw

## /github/workspace/pkglib/ptairData/extdata/exhaledAir/ind1/ind1-1.h5 
##   mz range:  20.4 - 150.7 
##   time range:  0 - 49 
##   Calibration error in ppm: 
##      No calibration performs

At this stage, the same methods as with the ptrSet object can be used: calibration, timeLimits, plotCalib, plotTIC, plotRaw, and detectPeak.

Acknowledgements

This research was conducted by Camille Roquencourt, with the contributions from Paul Zheng, Pierrick Roger-Mele and Etienne A. Thevenot (Data Sciences for Deep Phenotyping and Precision Medicine team at CEA), in collaboration with Stanislas Grassin-Delyle, Helene Salvator, Emmanuel Naline, Philippe Devillier and Louis-Jean Couderc (Exhalomics platform at the Foch Hospital) within the SoftwAiR project funded by the Agence Nationale de la Recherche (ANR-18-CE45-0017 grant).

Session Info

sessionInfo()

## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ptairData_1.14.0 ptairMS_1.15.0   BiocStyle_2.35.0
## 
## loaded via a namespace (and not attached):
##   [1] MultiDataSet_1.35.0         gridExtra_2.3              
##   [3] rlang_1.1.4                 magrittr_2.0.3             
##   [5] matrixStats_1.4.1           compiler_4.4.2             
##   [7] vctrs_0.6.5                 stringr_1.5.1              
##   [9] crayon_1.5.3                pkgconfig_2.0.3            
##  [11] fastmap_1.2.0               XVector_0.47.0             
##  [13] backports_1.5.0             labeling_0.4.3             
##  [15] utf8_1.2.4                  ropls_1.39.0               
##  [17] promises_1.3.0              rmarkdown_2.29             
##  [19] UCSC.utils_1.3.0            purrr_1.0.2                
##  [21] bit_4.5.0                   MultiAssayExperiment_1.33.0
##  [23] xfun_0.49                   zlibbioc_1.52.0            
##  [25] cachem_1.1.0                GenomeInfoDb_1.43.1        
##  [27] jsonlite_1.8.9              later_1.3.2                
##  [29] DelayedArray_0.33.2         rhdf5filters_1.19.0        
##  [31] Rhdf5lib_1.29.0             broom_1.0.7                
##  [33] parallel_4.4.2              cluster_2.1.6              
##  [35] R6_2.5.1                    bslib_0.8.0                
##  [37] stringi_1.8.4               limma_3.63.2               
##  [39] car_3.1-3                   rpart_4.1.23               
##  [41] GenomicRanges_1.59.1        jquerylib_0.1.4            
##  [43] Rcpp_1.0.13-1               SummarizedExperiment_1.37.0
##  [45] iterators_1.0.14            knitr_1.49                 
##  [47] base64enc_0.1-3             IRanges_2.41.1             
##  [49] Matrix_1.7-1                httpuv_1.6.15              
##  [51] splines_4.4.2               nnet_7.3-19                
##  [53] tidyselect_1.2.1            rstudioapi_0.17.1          
##  [55] abind_1.4-8                 yaml_2.3.10                
##  [57] doParallel_1.0.17           codetools_0.2-20           
##  [59] minpack.lm_1.2-4            lattice_0.22-6             
##  [61] tibble_3.2.1                plyr_1.8.9                 
##  [63] Biobase_2.67.0              shiny_1.9.1                
##  [65] withr_3.0.2                 evaluate_1.0.1             
##  [67] signal_1.8-1                foreign_0.8-87             
##  [69] pillar_1.9.0                BiocManager_1.30.25        
##  [71] ggpubr_0.6.0                MatrixGenerics_1.19.0      
##  [73] carData_3.0-5               checkmate_2.3.2            
##  [75] foreach_1.5.2               stats4_4.4.2               
##  [77] generics_0.1.3              S4Vectors_0.45.2           
##  [79] ggplot2_3.5.1               munsell_0.5.1              
##  [81] scales_1.3.0                calibrate_1.7.7            
##  [83] chron_2.3-61                xtable_1.8-4               
##  [85] glue_1.8.0                  shinyscreenshot_0.2.1      
##  [87] Hmisc_5.2-0                 maketools_1.3.1            
##  [89] tools_4.4.2                 sys_3.4.3                  
##  [91] data.table_1.16.2           enviPat_2.6                
##  [93] ggsignif_0.6.4              buildtools_1.0.0           
##  [95] cowplot_1.1.3               rhdf5_2.51.0               
##  [97] grid_4.4.2                  tidyr_1.3.1                
##  [99] colorspace_2.1-1            GenomeInfoDbData_1.2.13    
## [101] htmlTable_2.4.3             Formula_1.2-5              
## [103] cli_3.6.3                   fansi_1.0.6                
## [105] S4Arrays_1.7.1              dplyr_1.1.4                
## [107] gtable_0.3.6                rstatix_0.7.2              
## [109] sass_0.4.9                  digest_0.6.37              
## [111] BiocGenerics_0.53.3         SparseArray_1.7.2          
## [113] htmlwidgets_1.6.4           farver_2.1.2               
## [115] htmltools_0.5.8.1           lifecycle_1.0.4            
## [117] httr_1.4.7                  statmod_1.5.0              
## [119] mime_0.12                   qqman_0.1.9                
## [121] bit64_4.5.2                 MASS_7.3-61

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Blake, Robert S., Paul S. Monks, and Andrew M. Ellis. 2009. “Proton-Transfer Reaction Mass Spectrometry.” Chemical Reviews 109 (3): 861–96. https://doi.org/10.1021/cr800364q.

Delabrière, Alexis, Ulli M Hohenester, Benoit Colsch, Christophe Junot, François Fenaille, and Etienne A Thévenot. 2017. “proFIA: A Data Preprocessing Workflow for Flow Injection Analysis Coupled to High-Resolution Mass Spectrometry.” Edited by Oliver Stegle. Bioinformatics 33 (23): 3767–75. https://doi.org/10.1093/bioinformatics/btx458.

Devillier, Philippe, Helene Salvator, Emmanuel Naline, Louis-Jean Couderc, and Stanislas Grassin-Delyle. 2017. “Metabolomics in the Diagnosis and Pharmacotherapy of Lung Diseases.” Current Pharmaceutical Design 23 (14). https://doi.org/10.2174/1381612823666170130155627.

Hansel, A., A. Jordan, R. Holzinger, P. Prazeller, W. Vogel, and W. Lindinger. 1995. “Proton Transfer Reaction Mass Spectrometry: On-Line Trace Gas Analysis at the Ppb Level.” International Journal of Mass Spectrometry and Ion Processes 149-150 (November): 609–19. https://doi.org/10.1016/0168-1176(95)04294-U.

Kuo, Tien-Chueh, Cheng-En Tan, San-Yuan Wang, Olivia A Lin, Bo-Han Su, Ming-Tsung Hsu, Jessica Lin, et al. 2020. “Human Breathomics Database” 2020: 8.

Lacy Costello, B de, A Amann, H Al-Kateb, C Flynn, W Filipiak, T Khalid, D Osborne, and N M Ratcliffe. 2014. “A Review of the Volatiles from the Healthy Human Body.” Journal of Breath Research 8 (1): 014001. https://doi.org/10.1088/1752-7155/8/1/014001.

Müller, Markus, Tomáš Mikoviny, Werner Jud, Barbara D’Anna, and Armin Wisthaler. 2013. “A New Software Tool for the Analysis of High Resolution PTR-TOF Mass Spectra.” Chemometrics and Intelligent Laboratory Systems 127 (August): 158–65. https://doi.org/10.1016/j.chemolab.2013.06.011.

Thevenot. 2015. “Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses.”

ptairMS: Processing and analysis of PTR-TOF-MS data