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Biostatistics. 2018 Jun 18. doi: 10.1093/biostatistics/kxy021. [Epub ahead of print]

PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptides.

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Université de Lyon, Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, 43 bd du 11 novembre 1918, 69622 Villeurbanne Cedex, France.
Université Grenoble Alpes, CNRS, CEA, INSERM, BIG-BGE, 17 avenue des Martyrs, 38054 Grenoble Cedex 9, France.


We propose a new hypothesis test for the differential abundance of proteins in mass-spectrometry based relative quantification. An important feature of this type of high-throughput analyses is that it involves an enzymatic digestion of the sample proteins into peptides prior to identification and quantification. Due to numerous homology sequences, different proteins can lead to peptides with identical amino acid chains, so that their parent protein is ambiguous. These so-called shared peptides make the protein-level statistical analysis a challenge and are often not accounted for. In this article, we use a linear model describing peptide-protein relationships to build a likelihood ratio test of differential abundance for proteins. We show that the likelihood ratio statistic can be computed in linear time with the number of peptides. We also provide the asymptotic null distribution of a regularized version of our statistic. Experiments on both real and simulated datasets show that our procedures outperforms state-of-the-art methods. The procedures are available via the pepa.test function of the DAPAR Bioconductor R package.

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