Benchmarking of Analysis Strategies for Data-independent Acquisition Proteomics Using a Large-scale Dataset Comprising Inter-patient Heterogeneity

Overview

Journal Nat Commun

Specialty Biology

Date 2022 May 13

PMID 35551187

Authors

Klemens Frohlich

Eva Brombacher

Matthias Fahrner

Daniel Vogele

Lucas Kook

Niko Pinter

Peter Bronsert

Sylvia Timme-Bronsert

Alexander Schmidt

Katja Barenfaller

Clemens Kreutz

Oliver Schilling

Affiliations

Soon will be listed here.

Abstract

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.

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