Integrating identification and quantification uncertainty for differential protein abundance analysis with Triqler

The, M.; Käll, L.

doi:10.1101/2020.09.24.311605

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bioRxiv · 10.1101/2020.09.24.311605

Integrating identification and quantification uncertainty for differential protein abundance analysis with Triqler

The, M.·Käll, L.

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Abstract

Protein quantification for shotgun proteomics is a complicated process where errors can be introduced in each of the steps. Triqler is a Python package that estimates and integrates errors of the different parts of the label-free protein quantification pipeline into a single Bayesian model. Specifically, it weighs the quantitative values by the confidence we have in the correctness of the corresponding PSM. Furthermore, it treats missing values in a way that reflects their uncertainty relative to observed values. Finally, it combines these error estimates in a single differential abundance FDR that not only reflects the errors and uncertainties in quantification but also in identification. In this tutorial, we show how to (1) generate input data for Triqler from quantification packages such as MaxQuant and Quandenser, (2) run Triqler and what the different options are, (3) interpret the results, (4) investigate the posterior distributions of a protein of interest in detail and (5) verify that the hyperparameter estimations are sensible.

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The, M., Käll, L.. 2020-09-25. Integrating identification and quantification uncertainty for differential protein abundance analysis with Triqler. https://doi.org/10.1101/2020.09.24.311605

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