Abstract
We present a framework for the analysis of multiplexed mass spectrometry proteomics data that reduces estimation error when combining multiple isobaric batches. Variations in the number and quality of observations have long complicated the analysis of isobaric proteomics data. Here we show that the power to detect statistical associations is substantially improved by utilizing models that directly account for known sources of variation in the number and quality of observations that occur across batches.
In a multibatch benchmarking experiment, our open-source software (msTrawler) increases the power to detect changes, especially in the range of less than twofold changes, while simultaneously increasing quantitative proteome coverage by utilizing more low-signal observations. Further analyses of previously published multiplexed datasets of 4 and 23 batches highlight both increased power and the ability to navigate complex missing data patterns without relying on unverifiable imputations or discarding reliable measurements.
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Code availability
The msTrawler R package, is available at www.github.com/Calico/msTrawler and the supplementary data and code used to generate the analyses in this paper are available at https://console.cloud.google.com/storage/browser/mstrawler_paper. A patent has been filed for the msTrawler data analysis framework and workflows.
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Acknowledgements
We thank all members of the mass spectrometry and computational teams at Calico Life Sciences LLC for assistance and helpful discussions, in particular E. Melamud, B. Bennett, L. Chan, T. Nguyen, P. Seitzer and J. Xu. Also, we thank the IT teams for their help with the support of in-house data analysis software and in particular A. Chekholko. We also thank S. Gygi, D. Schweppe, J. Mintseris, E. Huttlin, M. Wühr and B. Qaqish for helping us to clarify the key messages in the paper. Funding for this work was provided by Calico Life Sciences LLC.
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Experiments were conceived and planned by J.J.O., A.R., A.W. and F.E.M. Experiments were carried out by A.W., A.G. and N.O. The new algorithms were created by J.J.O. The software was developed by J.J.O. and W.L. Data analyses and interpretations were performed by J.J.O., W.L., A.R., A.G., D.G.H., N.O. and F.E.M. The paper was written by J.J.O. with input from all authors.
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All authors were employees of Calico Life Sciences LLC at the time of submission.
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Nature Methods thanks Samuel Payne and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Arunima Singh, in collaboration with the Nature Methods team.
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Supplementary Information
Supplementary Appendix 1 (Supplementary Table 1 and Fig. 1), Appendix 2 (Supplementary Figs. 2 and 3), Appendix 3 (Supplementary Fig. 4 and Tables 2 and 3), Appendix 4 (Supplementary Fig. 5), Appendix 5 (Supplementary Fig. 6), Supplementary Figs. 7–10, Appendix 6 (supplementary methods and Table 4) and references.
Supplementary Dataset 1
msTrawler results from the interbatch experiment.
Supplementary Dataset 2
Worksheet containing results from the msTrawler reanalysis of the Hayflick time course.
Supplementary Dataset 3
msTrawler results from the reanalysis of the pediatric brain tumor data.
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O’Brien, J.J., Raj, A., Gaun, A. et al. A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments. Nat Methods 21, 290–300 (2024). https://doi.org/10.1038/s41592-023-02120-6
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DOI: https://doi.org/10.1038/s41592-023-02120-6
