Author: Arnaud Wolfer
Date: 2020-10-11

# peakPantheR Graphical User Interface

#### Arnaud Wolfer

#### 2020-10-11

Package: peakPantheR
Authors: Arnaud Wolfer, Goncalo Correia

# 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 graphical user interface implements all of `peakPantheR`’s functionalities and can be preferred to understand the methodology, select the best parameters on a subset of the samples before running the command line, or to visually explore results.

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

- Detail the step-by-step use of the graphical user interface
- Apply the GUI to a subset of pre-defined features in the faahKO dataset

## 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

## Example Data

This vignette employ the `.csv` or `.RData` files generated from faahKO in the vignette Getting Started with peakPantheR.

## Getting Started

The graphical user interface is started as follow:

    library(peakPantheR)

    peakPantheR_start_GUI(browser = TRUE)
    #  To exit press ESC in the command line

> The graphical interface is divided in 5 main tabs, Import Data, Run annotation, Diagnostic: plot & update, View results and Export results.

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# Graphical User Interface

## Import

The first input format is using a `.RData` file containing a peakPantheRAnnotation named `annotationObject`. This object can be annotated or not, for example loading a previously run annotation (see the Export section for more details).

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The second input format consists of multiple `.csv` files describing the targeted features, spectra to process and corresponding metadata (optional). Spectra can also be directly selected on disk.

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## Run Annotation

With the targeted features and spectra defined, Run annotation handles the fitting parameter selection as well as downstream computation. First the use of updated regions of interest (`uROI`) and fallback integration regions (`FIR`) can be selected if available. If `uROI` haven’t been previously defined, the option will be crossed out. Secondly the curve fitting model to use can be selected from the interface. Finally `Parallelisation` enables the selection of the number of CPU cores to employ for parallel file access and processing.

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## Diagnostic: plot & update

> Note:

> The targeted regions of interest (`ROI`) should represent a good starting point for feature integration, however it might be necessary to refine these boundary box to the specific analytical run considered. This ensures a successful integration over all the spectra irrespective of potential chromatographic equilibration differences or retention time drift.

> Updated regions of interest (`uROI`) can be defined and will supplant `ROI`. `uROI` can for example be manually defined to “tighten” or correct the `ROI` and avoid erroneous integration. Another use of `uROI` is to encompass the integration region in each sample throughout the run without targeting any excess spectral region that would interfere with the correct analysis.

> Fallback integration regions (`FIR`) are defined as spectral regions that will be integrated (i.e. integrating the baseline signal) when no successful chromatographic peak could be detected in a sample. `FIR` shouldn’t reasonably stretch further than the minimum and maximum bound (RT / m/z) of all found peaks across all samples for a given feature: this way no excess signal will be considered.

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With all features integrated in all samples, Diagnostic provide tools to assess the quality of the peak integration and refine integration boundaries by setting `uROI` and `FIR` adapted to the specific chromatographic run being processed.

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`Annotation statistics` summarises the success in integrating each targeted feature. The `ratio of peaks found (%)`, `ratio of peaks filled (%)` and the average `ppm error` and `RT deviation (s)` will highlight a feature that wasn’t reliably integrated over a large number of samples. Visual evaluation (see below) and the adjustment of `uROI` or `FIR` might assist in tuning the integration of said feature.

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`Update uROI/FIR` automatically sets `uROI` and `FIR` for each feature based on the RT / m/z boundaries of the peaks successfully integrated.

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`Diagnostic plot` offer a visualisation of a selected feature across all samples in order of analysis. This visualisation highlights the fitting of the feature in each sample, as well as the change in RT / m/z (of the peak apex) and peak area through time. Samples can be automatically coloured based on a sample metadata column.

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Once `uROI` and `FIR` are successfully set, it is possible to go back to the Run annotation tab and refit all features in all samples (Note: this will overwrite the current results).

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## View results

If the features integration are satisfactory, View results regroups all the integration results

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`Overall results` displays a fitting property for all targeted features (as columns) and all spectra (as rows).

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`Results per targeted feature` displays all fitting properties (as columns) for all samples (as rows) for a selected targeted feature.

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`Results per sample` displays all fitting properties (as columns) for all targeted features (as rows) for a selected sample.

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## Export

The Export tab manages the saving of input parameters, annotation results and automated reporting.

The `peakPantheRAnnotation` in it’s current state can be saved as a `.RData` file which can subsequently be reloaded. The `.csv` files defining the current analysis can also be exported to reproduce the current processing.

All diagnostic plots from the `Diagnostic` tab can be automatically saved to disk for rapid evaluation. This can be executed in parallel if a large number of plots have to be generated.

Finally each fitting property can be saved as a `.csv` file with all samples as rows and all targeted features as columns. Additionally a summary table will present the integration success rate for each targeted feature.

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# Final Note

If a very high number of targeted features and samples are to be processed, `peakPantheR`’s command line functions are more efficient, as they can be integrated in scripts and the reporting automated.

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# See Also

- Getting Started with peakPantheR
- Real Time Annotation
- Parallel Annotation

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