knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Authors: Arnaud Wolfer, Goncalo Correia
## Silently loading all packages library(BiocStyle) library(peakPantheR) library(faahKO) library(pander)
peakPantheR package is designed for the detection, integration and
reporting of pre-defined features in MS files (e.g. compounds, fragments,
The graphical user interface implements all of
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
r Biocpkg("faahKO") raw MS dataset as an example, this vignette
This vignette employ the
.RData files generated from
r Biocpkg("faahKO") in the vignette
Getting Started with peakPantheR.
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.
The first input format is using a
.RData file containing a
annotationObject. This object can be annotated
or not, for example loading a previously run annotation (see the Export
section for more details).
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.
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
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.
Parallelisation enables the selection of the number of CPU cores to
employ for parallel file access and processing.
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
uROIcan for example be manually defined to "tighten" or correct the
ROIand avoid erroneous integration. Another use of
uROIis 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.
FIRshouldn'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.
With all features integrated in all samples, Diagnostic provide tools to
assess the quality of the peak integration and refine integration boundaries by
FIR adapted to the specific chromatographic run being
Annotation statistics summarises the success in integrating each targeted
ratio of peaks found (%),
ratio of peaks filled (%) and the
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
FIR might assist in tuning the
integration of said feature.
Update uROI/FIR automatically sets
FIR for each feature based on
the RT / m/z boundaries of the peaks successfully integrated.
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.
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).
If the features integration are satisfactory, View results regroups all the integration results
Overall results displays a fitting property for all targeted features (as
columns) and all spectra (as rows).
Results per targeted feature displays all fitting properties (as columns)
for all samples (as rows) for a selected targeted feature.
Results per sample displays all fitting properties (as columns) for all
targeted features (as rows) for a selected sample.
The Export tab manages the saving of input parameters, annotation results and automated reporting.
peakPantheRAnnotation in it's current state can be saved as a
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.
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.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.