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Bayesian Confidence Intervals for Multiplexed Proteomics Integrate Ion-Statistics with Peptide Quantification Concordance

Leonid Peshkin, Lillia Ryazanova, View ORCID ProfileMartin Wühr
doi: https://doi.org/10.1101/210476
Leonid Peshkin
1Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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Lillia Ryazanova
2Department of Molecular Biology & the Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
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Martin Wühr
2Department of Molecular Biology & the Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
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Abstract

Multiplexed proteomics has emerged as a powerful tool to measure protein expression levels across multiple conditions. The relative protein abundances are inferred by comparing the signal generated by isobaric tags, which encode the samples’ origins. Intuitively, the trust associated with a protein measurement depends on the similarity of ratios from different peptides and the signal level of these measurements. Up to this point in the field, peptide-level information has not typically been integrated into confidence, and only the most likely results for relative protein abundances are reported. If confidence is reported, it is based on proteinlevel measurement agreement between replicates. Here we present a mathematically rigorous approach that integrates peptide intensities and peptide-measurement agreement into confidence intervals for protein ratios (BACIQ). The main advantages of BACIQ are: 1) it removes the need to threshold reported peptide signal based on an arbitrary cut-off, thereby reporting more measurements from a given experiment; 2) confidence can be assigned without replicates; 3) for repeated experiments BACIQ provides confidence intervals for the union, not the intersection, of quantified proteins; 4) for repeated experiments, BACIQ confidence intervals are more predictive than confidence intervals based on protein measurement agreement. Therefore, our method drastically increases the value obtained from quantitative proteomics experiments and will help researchers to interpret their data and prioritize resources. To make our approach easily accessible we distribute it via an R/Stan package.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 28, 2017.
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Bayesian Confidence Intervals for Multiplexed Proteomics Integrate Ion-Statistics with Peptide Quantification Concordance
Leonid Peshkin, Lillia Ryazanova, Martin Wühr
bioRxiv 210476; doi: https://doi.org/10.1101/210476
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Bayesian Confidence Intervals for Multiplexed Proteomics Integrate Ion-Statistics with Peptide Quantification Concordance
Leonid Peshkin, Lillia Ryazanova, Martin Wühr
bioRxiv 210476; doi: https://doi.org/10.1101/210476

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