# Parallel Annotation

#### 2020-10-11

#### Package

peakPantheR 1.23.0

Package: peakPantheR
Authors: Arnaud Wolfer, Goncalo Correia

# 1 Introduction

The `peakPantheR` package is designed for the detection, integration and reporting of pre-defined features in MS files (e.g. compounds, fragments, adducts, …).

The Parallel Annotation is set to detect and integrate multiple compounds in multiple files in parallel and store results in a single object. It can be employed to integrate a large number of expected features across a dataset.

Using the faahKO raw MS dataset as an example, this vignette will:

- Detail the Parallel Annotation concept
- Apply the Parallel Annotation to a subset of pre-defined features in the faahKO dataset

## 1.1 Abbreviations

- ROI: Regions Of Interest
  - reference RT / m/z windows in which to search for a feature
- uROI: updated Regions Of Interest
  - modifed ROI adapted to the current dataset which override the reference ROI
- FIR: Fallback Integration Regions
  - RT / m/z window to integrate if no peak is found
- TIC: Total Ion Chromatogram
  - the intensities summed across all masses for each scan
- EIC: Extracted Ion Chromatogram
  - the intensities summed over a mass range, for each scan

# 2 Parallel Annotation Concept

Parallel compound integration is set to process multiple compounds in multiple files in parallel, and store results in a single object.

To achieve this, `peakPantheR` will:

1. load a list of expected RT / m/z ROI and a list of files to process
2. initialise an output object with expected ROI and file paths
3. first pass (without peak filling) on a subset of representative samples (e.g QC samples):
   - for each file, detect features in each ROI and keep highest intensity
   - determine peak statistics for each feature
   - store results + EIC for each ROI
4. 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)
5. initialise a new output object, with updated regions of interest (uROI) and fallback integration regions (FIR), with all samples
6. second pass (with peak filling) on all samples:
   - for each file, detect features in each uROI and keep highest intensity
   - determine peak statistics for each feature
   - integrate FIR when no peaks are found
   - store results + EIC for each uROI
7. summary statistics:
   - plot EICs, apex and peakwidth evolution
   - compare first and second pass
8. return the resulting object and/or table (row: file, col: compound)

> Diagram of the workflow and functions used for parallel annotation.

# 3 Parallel Annotation Example

We can target 2 pre-defined features in 6 raw MS spectra file from the faahKO package using `peakPantheR_parallelAnnotation()`. For more details on the installation and input data employed, please consult the Getting Started with peakPantheR vignette.

## 3.1 Input Data

First the paths to 3 MS file from the faahKO are located and used as input spectras. In this example these 3 samples are considered as representative of the whole run (e.g. Quality Control samples):

    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
    #> [1] "F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/KO/ko15.CDF"
    #> [2] "F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/KO/ko16.CDF"
    #> [3] "F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/KO/ko18.CDF"

Two targeted features (e.g. compounds, fragments, adducts, …) are defined and stored in a table with as columns:

- `cpdID` (character)
- `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=FALSE)
    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)

cpdID | cpdName | rtMin | rt | rtMax | mzMin | mz | mzMax
--- | --- | --- | --- | --- | --- | --- | ---
ID-1 | Cpd 1 | 3310 | 3344.888 | 3390 | 522.194778 | 522.2 | 522.205222
ID-2 | Cpd 2 | 3280 | 3385.577 | 3440 | 496.195038 | 496.2 | 496.204962

Additional compound and spectra metadata can be provided but isn’t employed during the fitting procedure:

    # 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=FALSE)

sampleType
sample type 1
sample type 2
sample type 1

## 3.2 Initialise and Run Parallel Annotation

A `peakPantheRAnnotation` object is first initialised with the path to the files to process (`spectraPaths`), features 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
    #> An object of class peakPantheRAnnotation
    #>  2 compounds in 3 samples.
    #>   updated ROI do not exist (uROI)
    #>   does not use updated ROI (uROI)
    #>   does not use fallback integration regions (FIR)
    #>   is not annotated

`peakPantheR_parallelAnnotation()` will run 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,
                                                        curveModel='skewedGaussian',
                                                        verbose=TRUE)
    #> Processing 2 compounds in 3 samples:
    #>   uROI:  FALSE
    #>   FIR:   FALSE
    #> ----- ko15 -----
    #> Polarity can not be extracted from netCDF files, please set manually the polarity with the 'polarity' method.
    #> Check input, mzMLPath must be a .mzML
    #> Reading data from 2 windows
    #> Data read in: 3.18 secs
    #> Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and ROI$mzMax for ROI #1
    #> Found 2/2 features in 0.11 secs
    #> Peak statistics done in: 0 secs
    #> Feature search done in: 4.4 secs
    #> ----- ko16 -----
    #> Polarity can not be extracted from netCDF files, please set manually the polarity with the 'polarity' method.
    #> Check input, mzMLPath must be a .mzML
    #> Reading data from 2 windows
    #> Data read in: 3.08 secs
    #> Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and ROI$mzMax for ROI #1
    #> Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and ROI$mzMax for ROI #2
    #> Found 2/2 features in 0.02 secs
    #> Peak statistics done in: 0 secs
    #> Feature search done in: 4.18 secs
    #> ----- ko18 -----
    #> Polarity can not be extracted from netCDF files, please set manually the polarity with the 'polarity' method.
    #> Check input, mzMLPath must be a .mzML
    #> Reading data from 2 windows
    #> Data read in: 3.15 secs
    #> Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and ROI$mzMax for ROI #1
    #> Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and ROI$mzMax for ROI #2
    #> Found 2/2 features in 0.02 secs
    #> Peak statistics done in: 0 secs
    #> Feature search done in: 4.26 secs
    #> Annotation object cannot be reordered by sample acquisition date
    #> ----------------
    #> Parallel annotation done in: 15.65 secs
    #>   0 failure(s)

    # successful fit
    nbSamples(annotation_result$annotation)
    #> [1] 3
    data_annotation   <- annotation_result$annotation
    data_annotation
    #> An object of class peakPantheRAnnotation
    #>  2 compounds in 3 samples.
    #>   updated ROI do not exist (uROI)
    #>   does not use updated ROI (uROI)
    #>   does not use fallback integration regions (FIR)
    #>   is annotated

    # list failed fit
    annotation_result$failures
    #> [1] file  error
    #> <0 rows> (or 0-length row.names)

## 3.3 Process Parallel Annotation Results

Based on the fit results, updated ROI (`uROI`) and fallback integration region (`FIR`) can be automatically 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 will be set as mimimum/maximum of found peaks (+/-5% of ROI in retention time)
    #> FIR will be calculated as the median of found "rtMin","rtMax","mzMin","mzMax"

    # uROI now exist
    updated_annotation
    #> An object of class peakPantheRAnnotation
    #>  2 compounds in 3 samples.
    #>   updated ROI exist (uROI)
    #>   does not use updated ROI (uROI)
    #>   does not use fallback integration regions (FIR)
    #>   is annotated

`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 and can be generated in parallel with the argument `ncores`:

    # 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] )

    # create a temporary location to save the diagnotic (otherwise provide the path
    # to the selected location)
    output_folder <- tempdir()

    # output fit diagnostic to disk
    outputAnnotationDiagnostic(updated_annotation, saveFolder=output_folder,
                                savePlots=TRUE, sampleColour=col_sType,
                                verbose=TRUE, ncores=2)

The data saved in `annotationParameters_summary.csv` is as follow:

Table continues below
cpdID | cpdName | X | ROI_rt | ROI_mz | ROI_rtMin | ROI_rtMax | ROI_mzMin
--- | --- | --- | --- | --- | --- | --- | ---
ID-1 | Cpd 1 | | | 3344.888 | 522.2 | 3310 | 3390 | 522.194778
ID-2 | Cpd 2 | | | 3385.577 | 496.2 | 3280 | 3440 | 496.195038

Table continues below
ROI_mzMax | X | uROI_rtMin | uROI_rtMax | uROI_mzMin | uROI_mzMax | uROI_rt
--- | --- | --- | --- | --- | --- | ---
522.205222 | | | 3305.75893 | 3411.43628 | 522.194778 | 522.205222 | 3344.888
496.204962 | | | 3337.37666 | 3462.44903 | 496.195038 | 496.204962 | 3385.577

uROI_mz | X | FIR_rtMin | FIR_rtMax | FIR_mzMin | FIR_mzMax
--- | --- | --- | --- | --- | ---
522.2 | | | 3326.10635 | 3407.27265 | 522.194778 | 522.205222
496.2 | | | 3365.02386 | 3453.40496 | 496.195038 | 496.204962

> 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.

### 3.3.1 Retention time correction

The optional `retentionTimeCorrection()` method provides an interface to adjust the expected ROI rt values and account for chromatographic batch effects. By comparing expected and found rt values for a set of reference compounds, a model of the chromatographic shift for the present batch can be established. This model can be in turned used to correct the expected retention time of all targeted compounds. In order to apply this method, the `peakPantheRAnnotation` must be previously annotated (`isAnnotated=TRUE`). The retention time correction algorithm to use can be selected using the `method` argument (currently `polynomial` and `constant` methods are available). `retentionTimeCorrection()` fits a correction function to model the dependency of the mean `rt_dev_sec` per reference feature with the expected databased retention time. If `useUROI=TRUE`, the expected retention time value is taken from the `UROI_rt` field, otherwise `ROI_rt` is used. If `robust=TRUE`, the RANSAC algorithm is used to automatically detect outliers and exclude them from the fit (this should only be used with a large number of reference features). `retentionTimeCorrection()` returns a list with 2 elements: * a modified `peakPantheRAnnotation` object * a `ggplot2` diagnostic plot (optional, depending on whether `TRUE` or `FALSE` is passed to the `diagnostic` argument). The returned `peakPantheRAnnotation` object contains the same `uROI` and `FIR` `mz` values as the original annotation, but the retention time related parameters (`rt`, `rtMin` and `rtMax`) are replaced by the adjusted values. The `rtMax` and `rtMin` are set as the corrected `rt` value plus or minus half the value passed to the `rtWindowWidth` argument, respectively. `useUROI` is also set to TRUE. To continue with the workflow, simply set a new annotation object with the fit parameters established by `retentionTimeCorrection()` and call `peakPantheR_parallelAnnotation()` for the final annotation.

    #> Warning in applyRTCorrection_checkInputParams(params, method, referenceTable):
    #> `polynomialOrder` is larger than the number of references passed.
    #> `polynomialOrder` will be set equal to number of reference compounds - 1

## 3.4 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.

### 3.4.1 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` by default and will need to be modified according to the analysis to run. `uROIExist` is established depending on the `.csv` uROI column, and will only be set to TRUE if no `NA` are present. It is possible to reset the `FIR` values with the `uROI` windows using `resetFIR()`.

    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

    new_annotation <- resetFIR(new_annotation)
    #> FIR will be reset with uROI values

### 3.4.2 Add new samples to process

Now that the fit parameters were set on 3 representative samples (e.g. QC), the same processing can be applied to all study samples. `resetAnnotation()` will reinitialise all the results and modify the samples or compounds targeted as 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
    #> [1] "F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/KO/ko15.CDF"
    #> [2] "F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/WT/wt15.CDF"
    #> [3] "F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/KO/ko16.CDF"
    #> [4] "F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/WT/wt16.CDF"
    #> [5] "F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/KO/ko18.CDF"
    #> [6] "F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/WT/wt18.CDF"

Below we define the metadata of these new samples:

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

Group
KO
WT
KO
WT
KO
WT

    ## 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
    #> An object of class peakPantheRAnnotation
    #>  2 compounds in 6 samples.
    #>   updated ROI exist (uROI)
    #>   uses updated ROI (uROI)
    #>   uses fallback integration regions (FIR)
    #>   is not annotated

## 3.5 Run Final Parallel Annotation

We can now run the final annotation on all samples with the optimised targeted features:

    # annotate files serially
    new_annotation_result <- peakPantheR_parallelAnnotation(new_annotation,
                                                            ncores=0, verbose=FALSE)
    #> Polarity can not be extracted from netCDF files, please set manually the polarity with the 'polarity' method.
    #> Warning in minpack.lm::nls.lm(par = init, lower = lower, upper = upper, : lmdif: info = -1. Number of iterations has reached `maxiter' == 50.
    #> Polarity can not be extracted from netCDF files, please set manually the polarity with the 'polarity' method.
    #> Polarity can not be extracted from netCDF files, please set manually the polarity with the 'polarity' method.
    #> Fit of ROI #1 is unsuccessful (cannot determine rtMin/rtMax)
    #> Polarity can not be extracted from netCDF files, please set manually the polarity with the 'polarity' method.
    #> Warning in minpack.lm::nls.lm(par = init, lower = lower, upper = upper, : lmdif: info = -1. Number of iterations has reached `maxiter' == 50.
    #> Polarity can not be extracted from netCDF files, please set manually the polarity with the 'polarity' method.
    #> Warning in min(tmpPt$mz): no non-missing arguments to min; returning Inf
    #> Warning in max(tmpPt$mz): no non-missing arguments to max; returning -Inf
    #> Polarity can not be extracted from netCDF files, please set manually the polarity with the 'polarity' method.
    #> Warning in minpack.lm::nls.lm(par = init, lower = lower, upper = upper, : lmdif: info = -1. Number of iterations has reached `maxiter' == 50.

    # successful fit
    nbSamples(new_annotation_result$annotation)
    #> [1] 6

    final_annotation      <- new_annotation_result$annotation
    final_annotation
    #> An object of class peakPantheRAnnotation
    #>  2 compounds in 6 samples.
    #>   updated ROI exist (uROI)
    #>   uses updated ROI (uROI)
    #>   uses fallback integration regions (FIR)
    #>   is annotated

    # list failed fit
    new_annotation_result$failures
    #> [1] file  error
    #> <0 rows> (or 0-length row.names)

### 3.5.1 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] )

    # create a temporary location to save the diagnotic (otherwise provide the path
    # to the selected location)
    final_output_folder <- tempdir()

    # 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]]

Table continues below
found | rtMin | rt | rtMax | mzMin | mz | mzMax | peakArea | peakAreaRaw
--- | --- | --- | --- | --- | --- | --- | --- | ---
TRUE | 3322 | 3337 | 3337 | 522.2 | 522.2 | 522.2 | 4289965 | 4289965
TRUE | 3363 | 3378 | 3378 | 496.2 | 496.2 | 496.2 | 7312556 | 7312556

Table continues below
maxIntMeasured | maxIntPredicted | is_filled | ppm_error | rt_dev_sec
--- | --- | --- | --- | ---
711872 | NA | TRUE | NA | NA
982976 | NA | TRUE | NA | NA

tailingFactor | asymmetryFactor | cpdID | cpdName
--- | --- | --- | ---
NA | NA | ID-1 | Cpd 1
NA | NA | ID-2 | Cpd 2

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

Table continues below
| ID-1
--- | ---
F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/KO/ko15.CDF | 4289965
F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/WT/wt15.CDF | 4355690
F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/KO/ko16.CDF | 46184
F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/WT/wt16.CDF | 28157
F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/KO/ko18.CDF | 141309
F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/WT/wt18.CDF | 101738

| ID-2
--- | ---
F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/KO/ko15.CDF | 7312556
F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/WT/wt15.CDF | 9278578
F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/KO/ko16.CDF | 473801
F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/WT/wt16.CDF | 475297
F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/KO/ko18.CDF | 743452
F:/biocbuild/bbs-3.22-bioc/R/library/faahKO/cdf/WT/wt18.CDF | 1480713

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):

    # create a temporary location to save the diagnotic (otherwise provide the path
    # to the selected location)
    final_output_folder <- tempdir()

    # 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

# 4 See Also

- Getting Started with peakPantheR
- Real Time Annotation
- Graphical user interface use

# 5 Session Information

    #> ─ Session info ───────────────────────────────────────────────────────────────
    #>  setting  value
    #>  version  R version 4.5.0 (2025-04-11 ucrt)
    #>  os       Windows Server 2022 x64 (build 20348)
    #>  system   x86_64, mingw32
    #>  ui       RTerm
    #>  language (EN)
    #>  collate  C
    #>  ctype    English_United States.utf8
    #>  tz       America/New_York
    #>  date     2025-06-04
    #>  pandoc   2.7.3 @ C:/PROGRA~1/Pandoc/ (via rmarkdown)
    #>  quarto   NA
    #>
    #> ─ Packages ───────────────────────────────────────────────────────────────────
    #>  package              * version   date (UTC) lib source
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    #>  affyio                 1.79.0    2025-06-04 [2] Bioconductor 3.22 (R 4.5.0)
    #>  AnnotationFilter       1.33.0    2025-06-04 [2] Bioconductor 3.22 (R 4.5.0)
    #>  Biobase              * 2.69.0    2025-06-04 [2] Bioconductor 3.22 (R 4.5.0)
    #>  BiocBaseUtils          1.11.0    2025-06-04 [2] Bioconductor 3.22 (R 4.5.0)
    #>  BiocGenerics         * 0.55.0    2025-06-04 [2] Bioconductor 3.22 (R 4.5.0)
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