wrProteo

Data analysis of proteomics experiments by mass spectrometry is supported by this collection of functions mostly dedicated to the analysis of (bottom-up) quantitative (XIC) data. Fasta-formatted proteomes (eg from UniProt Consortium doi:10.1093/nar/gky1049) can be read with automatic parsing and multiple annotation types (like species origin, abbreviated gene names, etc) extracted. Initial results from multiple software for protein (and peptide) quantitation can be imported (to a common format): MaxQuant (Tyanova et al 2016 doi:10.1038/nprot.2016.136), Dia-NN (Demichev et al 2020 doi:10.1038/s41592-019-0638-x), Fragpipe (da Veiga et al 2020 doi:10.1038/s41592-020-0912-y), ionbot (Degroeve et al 2021 doi:10.1101/2021.07.02.450686), MassChroq (Valot et al 2011 doi:10.1002/pmic.201100120), OpenMS (Strauss et al 2021 doi:10.1038/nmeth.3959), ProteomeDiscoverer (Orsburn 2021 doi:10.3390/proteomes9010015), Proline (Bouyssie et al 2020 doi:10.1093/bioinformatics/btaa118), AlphaPept (preprint Strauss et al doi:10.1101/2021.07.23.453379) and Wombat-P (Bouyssie et al 2023 doi:10.1021/acs.jproteome.3c00636. Meta-data provided by initial analysis software and/or in sdrf format can be integrated to the analysis. Quantitative proteomics measurements frequently contain multiple NA values, due to physical absence of given peptides in some samples, limitations in sensitivity or other reasons. Help is provided to inspect the data graphically to investigate the nature of NA-values via their respective replicate measurements and to help/confirm the choice of NA-replacement algorithms. Meta-data in sdrf-format (Perez-Riverol et al 2020 doi:10.1021/acs.jproteome.0c00376) or similar tabular formats can be imported and included. Missing values can be inspected and imputed based on the concept of NA-neighbours or other methods. Dedicated filtering and statistical testing using the framework of package 'limma' (doi:10.18129/B9.bioc.limma) can be run, enhanced by multiple rounds of NA-replacements to provide robustness towards rare stochastic events. Multi-species samples, as frequently used in benchmark-tests (eg Navarro et al 2016 doi:10.1038/nbt.3685, Ramus et al 2016 doi:10.1016/j.jprot.2015.11.011), can be run with special options considering such sub-groups during normalization and testing. Subsequently, ROC curves (Hand and Till 2001 doi:10.1023/A:1010920819831) can be constructed to compare multiple analysis approaches. As detailed example the data-set from Ramus et al 2016 (doi:10.1016/j.jprot.2015.11.011) quantified by MaxQuant, ProteomeDiscoverer, and Proline is provided with a detailed analysis of heterologous spike-in proteins.

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Examples that run

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Exported functions with examples

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Installing wrProteo adds 6 packages (2 with compiled code, 1.7 MB of source tarballs). Heaviest: wrMisc 858 KB, knitr 479 KB, xfun 224 KB. 1 more not on CRAN is not measured.

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First published

2020-01-08

Total releases

27 / 6 yrs

License

GPL-3 OSI

Minimum R

≥ 3.5.0

Download size

4.6 MB

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