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Optimization of Statistical Methods Impact on Quantitative Proteomics Data




TekijätPursiheimo A, Vehmas AP, Afzal S, Suomi T, Chand T, Strauss L, Poutanen M, Rokka A, Corthals GL, Elo LL,

Julkaisuvuosi2015

JournalJournal of Proteome Research

Vuosikerta14

Numero10

Aloitussivu4118

Lopetussivu4126

Sivujen määrä9

ISSN1535-3893

DOIhttps://doi.org/10.1021/acs.jproteome.5b00183


Tiivistelmä

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 data-driven reproducibility-optimization can consistently produce reliable differential expression rankings for label-free proteome tools and are straightforward in their application.



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