flipR 1.0.6

FlipR 1.0.6

FlipR is a package to help in optimal collision energy selection for molecules based on MS2 data that was gathered with increasing collision energies. It therefor fits a parameterized log-normal distribution to the relative intensity of one particular fragment ion (incl. adduct) over the range of collision energies. We use the nls.multstart package to select a model that minimizes the AIC value within a given range of parameters that are used in a grid search, starting in an initially user-determined range. The log-normal distribution is able to adapt quite well to different fragments and their distribution profiles. Some cases though seemingly show different fragmentation behaviour that is usually visible through a less steep decline (flatter curve) of the scan relative intensity over the range of fragmentation energies.

It has been tested on Thermo QExactive HF and Waters QTof instruments.

Installation

Install devtools:

For Ubuntu, please install the following libraries:

  sudo apt-get install build-essential libcurl4-gnutls-dev libxml2-dev libssl-dev

Your will require R>=3.5.0 for flipr. Start administrator's R session as follows, to install the packages into the site-wide (all users) library:

  sudo -i R

Install devtools and wait until installation has finished:

  install.packages("devtools")

Load the devtools library:

  library(devtools)

Run

  install_github("lifs-tools/flipr")

This will install the latest, potentially unstable development version of the package with all required dependencies into your local R installation.

If you want to use a proper release version, referenced by a Git tag (here: v1.0.6) install the package as follows:

  install_github("lifs-tools/flipr", ref="v1.0.6")

To load the package, start an R session and type

  library(flipr)

Type the following to see the package vignette / tutorial:

  vignette('introduction', package = 'flipr')

Building the Docker image

The flipr package can also be packaged as a Docker container. To build it for your local use, use the following command under Linux:

./build-docker.sh -d=docker/flipr/ -v=MYTAG 

MYTAG would usually be the package version, e.g. 1.0.6. This command will build the image and will tag it under the version provided with -v.

If you want to change the default registry prefix used for tagging, edit the config.sh file. Also, the tagging base (the first part after the registry) can
be defined in that file.

Running the Docker image

To run flipr using the docker image from your current directory (under Linux), run it as follows (assuming you have not changed config.sh):

docker run -v "$PWD":/home/data/ do1-aps-feris.isas.de:5000/lifs/flipr:1.0.6 --projectDir=/home/data --trainModel=TRUE --dataPlots=TRUE

This will mount the contents of the current directory (PWD) into the /home/data folder within the container. Then, Docker will run the image tagged
do1-aps-feris.isas.de:5000/lifs/flipr:1.0.6 on your local machine. The commands following are passed to the flipr.R script, which is located in the exec folder of the package.
To see, which options are available, add --help to the command. It is advisable to add a custom config.R file to define the boundaries for the optimization. You can use it as follows:

docker run -v "$PWD":/home/data/ do1-aps-feris.isas.de:5000/lifs/flipr:1.0.6 --projectDir=/home/data --trainModel=TRUE --dataPlots=TRUE --config=config.R

This assumes, that config.R is available within the projectDir directory. All file and directory arguments apply to the file system within the container!

Example files are available from the inst/testdata folder of this project.

1. Create a new folder flipr-test
2. Copy the files inst/testdata/qex-HF-config.R and inst/testdata/12-HETE-d8-_M-H_1-qex_fip.tsv into flipr-test
3. Change to the flipr-test folder
4. Run the flipr Docker container: docker run --rm -v "$PWD":/home/data/ do1-aps-feris.isas.de:5000/lifs/flipr:1.0.6 --projectDir=/home/data --trainModel=TRUE --dataPlots=TRUE --config=/home/data/qex-HF-config.R
5. Examine the execution of the container and open the results in flipr-test

You will see multiple diagnostic plots of the extracted signals, as well as plots generated to display the model fit and various metrics for the fit quality. Each plot will contain display information for each of the fragments defined for one precursor molecule and adduct combination.