Literature DB >> 17081811

Statistics for proteomics: experimental design and 2-DE differential analysis.

Jean-François Chich1, Olivier David, Fanny Villers, Brigitte Schaeffer, Didier Lutomski, Sylvie Huet.   

Abstract

Proteomics relies on the separation of complex protein mixtures using bidimensional electrophoresis. This approach is largely used to detect the expression variations of proteins prepared from two or more samples. Recently, attention was drawn on the reliability of the results published in literature. Among the critical points identified were experimental design, differential analysis and the problem of missing data, all problems where statistics can be of help. Using examples and terms understandable by biologists, we describe how a collaboration between biologists and statisticians can improve reliability of results and confidence in conclusions.

Mesh:

Year:  2006        PMID: 17081811     DOI: 10.1016/j.jchromb.2006.09.033

Source DB:  PubMed          Journal:  J Chromatogr B Analyt Technol Biomed Life Sci        ISSN: 1570-0232            Impact factor:   3.205


  21 in total

1.  Protein profile of cotyledon, tegument, and embryonic axis of mature acorns from a non-orthodox plant species: Quercus ilex.

Authors:  Besma Sghaier-Hammami; Inmaculada Redondo-López; José Valero-Galvàn; Jesús V Jorrín-Novo
Journal:  Planta       Date:  2015-09-30       Impact factor: 4.116

2.  Role of bacterial peptidase F inferred by statistical analysis and further experimental validation.

Authors:  Liliana Lopez Kleine; Véronique Monnet; Christine Pechoux; Alain Trubuil
Journal:  HFSP J       Date:  2008-01-07

Review 3.  Image analysis tools and emerging algorithms for expression proteomics.

Authors:  Andrew W Dowsey; Jane A English; Frederique Lisacek; Jeffrey S Morris; Guang-Zhong Yang; Michael J Dunn
Journal:  Proteomics       Date:  2010-12       Impact factor: 3.984

4.  Normalization and statistical analysis of quantitative proteomics data generated by metabolic labeling.

Authors:  Lily Ting; Mark J Cowley; Seah Lay Hoon; Michael Guilhaus; Mark J Raftery; Ricardo Cavicchioli
Journal:  Mol Cell Proteomics       Date:  2009-07-14       Impact factor: 5.911

5.  Proteomics analysis of date palm leaves affected at three characteristic stages of brittle leaf disease.

Authors:  Besma Sghaier-Hammami; Mohammed Najib Saidi; María Angeles Castillejo; Jesús V Jorrín-Novo; Ahmed Namsi; Noureddine Drira; Radhia Gargouri-Bouzid
Journal:  Planta       Date:  2012-07-29       Impact factor: 4.116

6.  Effects of lead and mercury on the blood proteome of children.

Authors:  Robert E Birdsall; Michael P Kiley; Zaneer M Segu; Christopher D Palmer; Milan Madera; Brooks B Gump; James A MacKenzie; Patrick J Parsons; Yehia Mechref; Milos V Novotny; Kestutis G Bendinskas
Journal:  J Proteome Res       Date:  2010-09-03       Impact factor: 4.466

7.  High-capacity peptide-centric platform to decode the proteomic response to brain injury.

Authors:  Diego F Cortes; Miranda K Landis; Andrew K Ottens
Journal:  Electrophoresis       Date:  2012-11-26       Impact factor: 3.535

8.  Inferring predominant pathways in cellular models of breast cancer using limited sample proteomic profiling.

Authors:  Yogesh M Kulkarni; Vivian Suarez; David J Klinke
Journal:  BMC Cancer       Date:  2010-06-15       Impact factor: 4.430

9.  Proteomic profiling of liver from Atlantic salmon (Salmo salar) fed genetically modified soy compared to the near-isogenic non-GM line.

Authors:  Nini H Sissener; Samuel A M Martin; Phillip Cash; Ernst M Hevrøy; Monica Sanden; Gro-Ingunn Hemre
Journal:  Mar Biotechnol (NY)       Date:  2009-07-17       Impact factor: 3.619

10.  Integrative analysis of the heat shock response in Aspergillus fumigatus.

Authors:  Daniela Albrecht; Reinhard Guthke; Axel A Brakhage; Olaf Kniemeyer
Journal:  BMC Genomics       Date:  2010-01-15       Impact factor: 3.969

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