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  • Title: Optimization of Statistical Methods Impact on Quantitative Proteomics Data.
    Author: Pursiheimo A, Vehmas AP, Afzal S, Suomi T, Chand T, Strauss L, Poutanen M, Rokka A, Corthals GL, Elo LL.
    Journal: J Proteome Res; 2015 Oct 02; 14(10):4118-26. PubMed ID: 26321463.
    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 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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