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Optimization of statistical methods impact on quantitative proteomics data

ABSTRACT: As tools for quantitative label-free mass spectrometry (MS) rapidly develop a consensus about the best practices is not apparent. In the work described here we compared five popular statistical methods for detecting differential protein expression from quantitative MS data using both controlled experiments with known quantitative differences for specific proteins used as standards, as well as ‘real’ experiments where differences in protein abundance are not known a priori. Our results suggest that data-driven reproducibility-optimization can consistently produce reliable differential expression rankings for label-free proteome tools and are straightforward in their application.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Candida Albicans (yeast)

TISSUE(S): Cell Culture

SUBMITTER: Anni Vehmas

LAB HEAD: Laura L. Elo

PROVIDER: PXD002099 | Pride | 2020-01-13

REPOSITORIES: Pride

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Optimization of Statistical Methods Impact on Quantitative Proteomics Data.

Pursiheimo Anna A Vehmas Anni P AP Afzal Saira S Suomi Tomi T Chand Thaman T Strauss Leena L Poutanen Matti M Rokka Anne A Corthals Garry L GL Elo Laura L LL

Journal of proteome research 20150908 10

As tools for quantitative label-free mass spectrometry (MS) rapidly develop, a consensus about the best practices is not apparent. In the work described here we compared popular statistical methods for detecting differential protein expression from quantitative MS data using both controlled experiments with known quantitative differences for specific proteins used as standards as well as "real" experiments where differences in protein abundance are not known a priori. Our results suggest that da ...[more]

PMID: 26321463

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