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Literature DB >> 18173221

Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA.

Ann L Oberg¹, Douglas W Mahoney, Jeanette E Eckel-Passow, Christopher J Malone, Russell D Wolfinger, Elizabeth G Hill, Leslie T Cooper, Oyere K Onuma, Craig Spiro, Terry M Therneau, H Robert Bergen.

Abstract

Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation, and useful visualization tools are demonstrated via a case study of complex biological samples assessed using the iTRAQ relative labeling protocol.

Mesh：

Substances：
Complex Mixtures
Isotopes

Year: 2008 PMID： 18173221 PMCID： PMC2528956 DOI： 10.1021/pr700734f

Source DB: PubMed Journal: J Proteome Res ISSN： 1535-3893 Impact factor: 4.466

21 in total

1. Assessing gene significance from cDNA microarray expression data via mixed models.

Authors: R D Wolfinger; G Gibson; E D Wolfinger; L Bennett; H Hamadeh; P Bushel; C Afshari; R S Paules
Journal: J Comput Biol Date: 2001 Impact factor: 1.479

2. Analysis of variance for gene expression microarray data.

Authors: M K Kerr; M Martin; G A Churchill
Journal: J Comput Biol Date: 2000 Impact factor: 1.479

3. Accurate quantitation of protein expression and site-specific phosphorylation.

Authors: Y Oda; K Huang; F R Cross; D Cowburn; B T Chait
Journal: Proc Natl Acad Sci U S A Date: 1999-06-08 Impact factor: 11.205

4. Exploration, normalization, and summaries of high density oligonucleotide array probe level data.

Authors: Rafael A Irizarry; Bridget Hobbs; Francois Collin; Yasmin D Beazer-Barclay; Kristen J Antonellis; Uwe Scherf; Terence P Speed
Journal: Biostatistics Date: 2003-04 Impact factor: 5.899

5. Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays.

Authors: Nitin Jain; Jayant Thatte; Thomas Braciale; Klaus Ley; Michael O'Connell; Jae K Lee
Journal: Bioinformatics Date: 2003-10-12 Impact factor: 6.937

6. Faster cyclic loess: normalizing RNA arrays via linear models.

Authors: Karla V Ballman; Diane E Grill; Ann L Oberg; Terry M Therneau
Journal: Bioinformatics Date: 2004-05-27 Impact factor: 6.937

7. A model for random sampling and estimation of relative protein abundance in shotgun proteomics.

Authors: Hongbin Liu; Rovshan G Sadygov; John R Yates
Journal: Anal Chem Date: 2004-07-15 Impact factor: 6.986

8. A statistical model for iTRAQ data analysis.

Authors: Elizabeth G Hill; John H Schwacke; Susana Comte-Walters; Elizabeth H Slate; Ann L Oberg; Jeanette E Eckel-Passow; Terry M Therneau; Kevin L Schey
Journal: J Proteome Res Date: 2008-06-26 Impact factor: 4.466

9. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.

Authors: S P Gygi; B Rist; S A Gerber; F Turecek; M H Gelb; R Aebersold
Journal: Nat Biotechnol Date: 1999-10 Impact factor: 54.908

10. The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster.

Authors: W Jin; R M Riley; R D Wolfinger; K P White; G Passador-Gurgel; G Gibson
Journal: Nat Genet Date: 2001-12 Impact factor: 38.330

82 in total

1. Isobaric labeling and data normalization without requiring protein quantitation.

Authors: Phillip D Kim; Bhavinkumar B Patel; Anthony T Yeung
Journal: J Biomol Tech Date: 2012-04

Review 2. Overcoming key technological challenges in using mass spectrometry for mapping cell surfaces in tissues.

Authors: Noelle M Griffin; Jan E Schnitzer
Journal: Mol Cell Proteomics Date: 2010-06-14 Impact factor: 5.911

3. Nonparametric Bayesian evaluation of differential protein quantification.

Authors: Oliver Serang; A Ertugrul Cansizoglu; Lukas Käll; Hanno Steen; Judith A Steen
Journal: J Proteome Res Date: 2013-09-11 Impact factor: 4.466

4. Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides.

Authors: Douglas W Mahoney; Terry M Therneau; Carrie J Heppelmann; Leeann Higgins; Linda M Benson; Roman M Zenka; Pratik Jagtap; Gary L Nelsestuen; H Robert Bergen; Ann L Oberg
Journal: J Proteome Res Date: 2011-08-02 Impact factor: 4.466

5. AKAP signaling in reinstated cocaine seeking revealed by iTRAQ proteomic analysis.

Authors: Kathryn J Reissner; Joachim D Uys; John H Schwacke; Susanna Comte-Walters; Jennifer L Rutherford-Bethard; Thomas E Dunn; Joe B Blumer; Kevin L Schey; Peter W Kalivas
Journal: J Neurosci Date: 2011-04-13 Impact factor: 6.167

6. An insight into high-resolution mass-spectrometry data.

Authors: J E Eckel-Passow; A L Oberg; T M Therneau; H R Bergen
Journal: Biostatistics Date: 2009-03-26 Impact factor: 5.899

7. iTRAQ-Based Global Proteomic Analysis of Salmonella enterica Serovar Typhimurium in Response to Desiccation, Low Water Activity, and Thermal Treatment.

Authors: Alice Maserati; Antonio Lourenco; Francisco Diez-Gonzalez; Ryan C Fink
Journal: Appl Environ Microbiol Date: 2018-08-31 Impact factor: 4.792

8. Quantification of protein expression changes in the aging left ventricle of Rattus norvegicus.

Authors: Jennifer E Grant; Amy D Bradshaw; John H Schwacke; Catalin F Baicu; Michael R Zile; Kevin L Schey
Journal: J Proteome Res Date: 2009-09 Impact factor: 4.466

Review 9. Quality assessment for clinical proteomics.

Authors: David L Tabb
Journal: Clin Biochem Date: 2012-12-12 Impact factor: 3.281

10. An Efficient Approach to Evaluate Reporter Ion Behavior from MALDI-MS/MS Data for Quantification Studies Using Isobaric Tags.

Authors: Stephanie M Cologna; Christopher A Crutchfield; Brian C Searle; Paul S Blank; Cynthia L Toth; Alexa M Ely; Jaqueline A Picache; Peter S Backlund; Christopher A Wassif; Forbes D Porter; Alfred L Yergey
Journal: J Proteome Res Date: 2015-09-03 Impact factor: 4.466