Tidyproteomics: an open-source R package and data object for quantitative proteomics post analysis and visualization

Creators: Jones, Jeff; MacKrell, Elliot J.; Wang, Ting-Yu; Lomenick, Brett; Roukes, Michael L.; Chou, Tsui-Fen

Abstract

Background. The analysis of mass spectrometry-based quantitative proteomics data can be challenging given the variety of established analysis platforms, the differences in reporting formats, and a general lack of approachable standardized post-processing analyses such as sample group statistics, quantitative variation and even data filtering. We developed tidyproteomics to facilitate basic analysis, improve data interoperability and potentially ease the integration of new processing algorithms, mainly through the use of a simplified data-object. Results: The R package tidyproteomics was developed as both a framework for standardizing quantitative proteomics data and a platform for analysis workflows, containing discrete functions that can be connected end-to-end, thus making it easier to define complex analyses by breaking them into small stepwise units. Additionally, as with any analysis workflow, choices made during analysis can have large impacts on the results and as such, tidyproteomics allows researchers to string each function together in any order, select from a variety of options and in some cases develop and incorporate custom algorithms. Conclusions: Tidyproteomics aims to simplify data exploration from multiple platforms, provide control over individual functions and analysis order, and serve as a tool to assemble complex repeatable processing workflows in a logical flow. Datasets in tidyproteomics are easy to work with, have a structure that allows for biological annotations to be added, and come with a framework for developing additional analysis tools. The consistent data structure and accessible analysis and plotting tools also offers a way for researchers to save time on mundane data manipulation tasks.

Additional Information

© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. The authors would like to thank numerous Caltech graduate students for their feedback and discussions that are an invaluable resource for understanding how to convey concise information about biological systems from complex analyses. A manual covering all the available functions along with explanation of each function and tutorials can be found at https://jefsocal.github.io/tidyproteomics. An R Shiny application is available at http://bioinformatics.pel.caltech.edu/tidyproteomics/. The Proteome Exploration Laboratory was supported by NIH OD010788, NIH OD020013, the Betty and Gordon Moore Foundation through grant GBMF775 and the Beckman Institute at Caltech. The Shiny app is hosted by The Proteome Exploration Laboratory at the Caltech Beckman Institute. This work was supported by the Institute for Collaborative Biotechnologies through cooperative agreement W911NF-19-2-0026 from the U.S. Army Research Office. The content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. In addition, partial support was provided by the Wellcome Leap Delta Tissue Program. Contributions. JJ developed the tidyproteomics code base. EM developed the shiny application. TYW, BL, TFC and MLR provided insight to workflow processes and analysis. All authors read and approved the final manuscript. Availability and requirements. Project name: tidyproteomics. Project homepage: https://github.com/jeffsocal/tidyproteomics. Operating system: platform independent. Programming language: R. Other requirements: none. License: MIT. Any restrictions to use by non-academics: none. Availability of data and materials. The datasets analyzed within the current study are available in the Tidyproteomics code repository, https://github.com/jeffsocal/tidyproteomics and Shiny app https://github.com/ejmackrell/tidyproteomics-interactive. Access to both the protein and peptide data sets are immediately available upon loading the package. Additionally, the data set is available from the Caltech data repository, https://data.caltech.edu/records/aevwq-2ps50, taken from Wang et al. [38]. Contributions. JJ developed the tidyproteomics code base. EM developed the shiny application. TYW, BL, TFC and MLR provided insight to workflow processes and analysis. All authors read and approved the final manuscript. The authors declare that they have no competing interests.

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