README.md

In ychen-uoft/msFeatureCmp: Mass Spectrometry Feature Comparator

msFeatureCmp

Description

The goal of msFeatureCmp is to provide a simple and fast way to evaluate the performance of different mass spectrometry feature finding algorithms, relative to one another. This package provides a way to compare features (peptide signals) found by different algorithms, perform some basic statistical analysis on the results, and visualize the different sets of features and their locations on the raw data. Existing MS tools (like OpenMS and MaxQuant) do not provide methods that allow for cross-tool comparisons–likely because they are competitors–so this package aims to make the process more accessible.

msFeatureCmp 0.1.0 was developed with R 4.1.1 in addition to Python 3.7.9 and the Python package pyOpenMS 2.7.0 on Windows 10 19043.1348.

Installation

Before you can use this package, you need to ensure that you have a version of Python from 3.5 to 3.7 installed, as well as the Python package pyOpenMS (at least version 2.4.0). Python installers can be downloaded from the Python downloads page here, and pyOpenMS can be installed from PyPI by running pip install pyopenms in a Python-enabled shell (e.g. bash, cmd, etc.).

You can install the development version of msFeatureCmp from GitHub with:

require("devtools")
devtools::install_github("ychen-uoft/msFeatureCmp", build_vignettes = TRUE)
library("msFeatureCmp")

To run the Shiny app:

runMsFeatureCmp()

If R has trouble finding the correct Python environment (and you have checked that all the Python prerequisites outlined above have been installed properly), then you can set it by hand with Sys.setenv(RETICULATE_PYTHON = "<path to the correct Python binary>") before installing the package.

Overview

ls("package:msFeatureCmp")

msFeatureCmp contains six public functions for enabling feature comparisons, analysis, and visualization. The primary function is compareFeatures, which takes two feature sets and compares the features between them. This allows users to check things like which features in the first set also occur in the second, and how many features are in common. The function getFeatureByIdx is a simple interface to a featureXML file, and allows users to select a feature in the file (by its index) to get its retention time, mass-to-charge, and signal intensity values.

The function plotRawData loads a single raw mass spectrometry data file and uses it to draw a 2D scatter plot. The function plotSingleFeatureSet loads a single feature set and uses it to draw a 2D scatter plot with colouring to simulate three dimensions. The function plotTwoFeatureSets loads two feature sets and plots both at the same time (with different colours for differentiation). The function plotFeatureSetOnRawData loads a single raw mass spectrometry data file and a single feature set and plots both at the same time (with different colours for differentiation).

Warning: depending on the size of the data, some of these functions may take a few minutes to execute.

For more details, refer to the package vignette.

browseVignettes("msFeatureCmp")

An overview of the package is illustrated below.

The package tree structure is provided below.

msFeatureCmp
├── DESCRIPTION
├── LICENSE
├── NAMESPACE
├── R
│   ├── comparator.R
│   ├── plotting.R
│   └── utils.R
├── README.Rmd
├── README.md
├── inst
│   ├── CITATION
│   └── extdata
│       ├── 20190122_HeLa_QC_Slot1-47_1_3228_800-810.mzML
│       ├── featureSetA.featureXML
│       ├── featureSetB.featureXML
│       └── overview.png
├── man
│   ├── compareFeatures.Rd
│   ├── convertFeaturesToSortedMatrix.Rd
│   ├── findFirstFeature.Rd
│   ├── generateFeatureDataFrame.Rd
│   ├── generateMSDataFrame.Rd
│   ├── getFeatureByIdx.Rd
│   ├── loadFeatureFile.Rd
│   ├── loadMSFile.Rd
│   ├── plotFeatureSetOnRawData.Rd
│   ├── plotRawData.Rd
│   ├── plotSingleFeatureSet.Rd
│   ├── plotTwoFeatureSets.Rd
│   ├── similarFeatures.Rd
│   ├── sortMatrixByColumn.Rd
│   └── withinThreshold.Rd
├── msfeaturecmp.Rproj
├── tests
│   ├── testthat
│   │   ├── test-public.R
│   │   └── test-utils.R
│   └── testthat.R
└── vignettes
    └── Introduction.Rmd

Contributions

The author of this package is Yijia Chen. All of the functions in this package rely on the R package reticulate (which imports the Python package pyOpenMS) in order to access the OpenMS mass spectrometry APIs (e.g. to load the raw MS data into memory), but no other R packages provide additional functionality (i.e. all algorithms were designed and implemented by hand).

The example mzML file (raw MS data, in “inst/extdata”) was downloaded from EMBL-EBI’s PRIDE archive as a Bruker (MaxQuant) d file, converted to the open-source OpenMS mzML format using the Python package diapysef, and cut to an arbitrary 10-second slice (in this case, 800s-810s) from the entire 2-hour run using OpenMS’s FileFilter tool. The example featureXML file (containing found features, in “inst/extdata”) “featureSetA” was obtained by running my own feature finding algorithm (developed for my BCB330 project with the Röst Lab at UofT) on the data, and the second example featureXML file “featureSetB” was obtained by converting and filtering a tsv file, containing results, that was provided with the raw data (which was obtained by running one of MaxQuant’s feature finding algorithms on it).

For the visualization functions, the R package ggplot2 was used to draw scatter plots and overlays.

References

Chen, Y., Xu, L., & Röst, H. (2020). Improving feature finding in LC-MS/MS runs by using additional ion mobility data. [Unpublished BCB330Y1 final report]. Department of Cell & Systems Biology, University of Toronto, Toronto, Canada.

Meier, F., Brunner, A.-D., Frank, M., Ha, A., Bludau, I., Voytik, E., Kaspar-Schoenefeld, S., Lubeck, M., Raether, O., Bache, N., Aebersold, R., Collins, B., Röst, H., & Mann, M. (2020). diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition. Nat Methods, 17, 1229-1236. https://doi.org/10.1038/s41592-020-00998-0

Prianichnikov, N., Koch, H., Koch, S., Lubeck, M., Heilig, R., Brehmer, S., Fischer, R., & Cox, J. (2020). MaxQuant software for ion mobility enhanced shotgun proteomics. Mol Cell Proteomics, 19, 1058-1069. https://doi.org/10.1074/mcp.TIR119.001720

Röst, H., Schmitt, U., Aebersold, R., & Malmström, M. (2014). pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library. Proteomics, 14(1), 74-77. https://doi.org/10.1002/pmic.201300246

Röst, H., Sachsenberg, T., Aiche, S., Bielow, C., Weisser, H., Aicheler, F., Andreotti, S., Ehrlich, H.-C., Gutenbrunner, P., Kenar, E., Liang, X., Nahnsen, S., Nilse, L., Pfeuffer, J., Rosenberger, G., Rurik, M., Schmitt, U., Veit, J., Walzer, M., … Kohlbacher, O. (2016). OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods, 13, 741-748. https://doi.org/10.1038/nmeth.3959

Ushey, K., Allaire, J., & Tang, Y. (2021). reticulate: interface to ‘Python’. R package version 1.22. https://CRAN.R-project.org/package=reticulate

Wickham, H. (2016). ggplot2: elegant graphics for data analysis. R package version 3.3.5. https://ggplot2.tidyverse.org

Acknowledgements

This package was developed as part of an assessment for 2021 BCB410H1: Applied Bioinformatics, University of Toronto, Toronto, CANADA.

ychen-uoft/msFeatureCmp documentation built on Dec. 23, 2021, 7:17 p.m.