Literature DB >> 21591257

The role of statistical power analysis in quantitative proteomics.

Yishai Levin1.   

Abstract

Designing an experiment for quantitative proteomic analysis is not a trivial task. One of the key factors influencing the success of such studies is the number of biological replicates included in the analysis. This, along with the measured variation will determine the statistical power of the analysis. Presented is a simple yet powerful analysis to determine the appropriate sample size required for reliable and reproducible results, based on the total variation (technical and biological). This approach can also be applied retrospectively for the interpretation of results as it takes into account both significance (p value) and quantitative difference (fold change) of the results.
Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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Year:  2011        PMID: 21591257     DOI: 10.1002/pmic.201100033

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  38 in total

1.  Differential phosphorylation of serum proteins reflecting inflammatory changes in schizophrenia patients.

Authors:  Julian A J Jaros; Daniel Martins-de-Souza; Hassan Rahmoune; Emanuel Schwarz; F Markus Leweke; Paul C Guest; Sabine Bahn
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2011-12-15       Impact factor: 5.270

2.  Longitudinal study of differential protein expression in an Alzheimer's mouse model lacking inducible nitric oxide synthase.

Authors:  Michael D Hoos; Brenna M Richardson; Matthew W Foster; Angela Everhart; J Will Thompson; M Arthur Moseley; Carol A Colton
Journal:  J Proteome Res       Date:  2013-09-18       Impact factor: 4.466

3.  DeltaMS: a tool to track isotopologues in GC- and LC-MS data.

Authors:  Tim U H Baumeister; Nico Ueberschaar; Wolfgang Schmidt-Heck; J Frieder Mohr; Michael Deicke; Thomas Wichard; Reinhard Guthke; Georg Pohnert
Journal:  Metabolomics       Date:  2018-02-27       Impact factor: 4.290

4.  Integrated Cellular and Plasma Proteomics of Contrasting B-cell Cancers Reveals Common, Unique and Systemic Signatures.

Authors:  Harvey E Johnston; Matthew J Carter; Kerry L Cox; Melanie Dunscombe; Antigoni Manousopoulou; Paul A Townsend; Spiros D Garbis; Mark S Cragg
Journal:  Mol Cell Proteomics       Date:  2017-01-04       Impact factor: 5.911

5.  Systems proteomics for translational network medicine.

Authors:  D Kent Arrell; Andre Terzic
Journal:  Circ Cardiovasc Genet       Date:  2012-08-01

6.  A robotic protocol for high-throughput processing of samples for selected reaction monitoring assays.

Authors:  Min Zhu; Pingbo Zhang; Minghui Geng-Spyropoulos; Ruin Moaddel; Richard D Semba; Luigi Ferrucci
Journal:  Proteomics       Date:  2016-12-23       Impact factor: 3.984

7.  Quantifying reversible oxidation of protein thiols in photosynthetic organisms.

Authors:  William O Slade; Emily G Werth; Evan W McConnell; Sophie Alvarez; Leslie M Hicks
Journal:  J Am Soc Mass Spectrom       Date:  2015-02-20       Impact factor: 3.109

8.  Quantitative proteomic analysis reveals metabolic alterations, calcium dysregulation, and increased expression of extracellular matrix proteins in laminin α2 chain-deficient muscle.

Authors:  Bruno Menezes de Oliveira; Cintia Y Matsumura; Cibely C Fontes-Oliveira; Kinga I Gawlik; Helena Acosta; Patrik Wernhoff; Madeleine Durbeej
Journal:  Mol Cell Proteomics       Date:  2014-07-03       Impact factor: 5.911

Review 9.  Biomarker discovery in mass spectrometry-based urinary proteomics.

Authors:  Samuel Thomas; Ling Hao; William A Ricke; Lingjun Li
Journal:  Proteomics Clin Appl       Date:  2016-02-11       Impact factor: 3.494

10.  Pressure Cycling Technology Assisted Mass Spectrometric Quantification of Gingival Tissue Reveals Proteome Dynamics during the Initiation and Progression of Inflammatory Periodontal Disease.

Authors:  Kai Bao; Xiaofei Li; Tetsuhiro Kajikawa; Abe Toshiharu; Nathalie Selevsek; Jonas Grossmann; George Hajishengallis; Nagihan Bostanci
Journal:  Proteomics       Date:  2020-01-15       Impact factor: 3.984

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