Generic framework for high-dimensional fixed-effects anova

Briefings in Bioinformatics

Volumn 13, Issue 5, 2012, Pages 524-535

  Smilde, Age K ^a   Timmerman, Marieke E ^b   Hendriks, Margriet M W B ^c   Jansen, Jeroen J ^a   Hoefsloot, Huub C J ^d  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b UNIVERSITY OF GRONINGEN   (Netherlands)

^c UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^d Chemical Engineering   (Netherlands)

Author keywords

ASCA; Designed experiments; High dimensional data; PRC; SMART

Indexed keywords

ALGORITHM; ANALYSIS OF VARIANCE; ARTICLE; FACTUAL DATABASE; GENOMICS; HUMAN; MATHEMATICAL PHENOMENA; METABOLOMICS; METHODOLOGY;

ALGORITHMS; ANALYSIS OF VARIANCE; DATABASES, FACTUAL; GENOMICS; HUMANS; MATHEMATICAL CONCEPTS; METABOLOMICS; RESEARCH DESIGN;

EID: 84865741404     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbr071     Document Type: Article

References (24)

1. Keun HC, Ebbels TM, Bollard ME, et al. Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles. Chem Res. Toxicol 2004;17(5): 579-87.
2. Jansen JJ, Hoefsloot HCJ, van der Greef J, etal. Asca: analysis of multivariate data obtained from an experimental design. JChemometrics 2005;19:469-81.
3. Harrington PD, Vieira NE, Espinoza J, et al. Analysis of variance-principal component analysis: a soft tool for proteomic discovery. Anal Chim Acta 2005;544(1-2): 118-27.
4. Kerr MK, Martin M, Churchill GA. Analysis of variance for gene expression microarray data. J Comp. Biol 2000;7: 819-37.
5. Kerr MK, Churchill GA. Statistical design and the analysis of gene expression microarray data. Genet Res. 2001;77: 123-8.
6. Jansen JJ, Bro R, Hoefsloot HCJ, et al. Parafasca: asca combined with parafac for the analysis of metabolic fingerprinting data. JChemometrics 2008;22(1-2):114-21.
7. Smilde AK, Jansen JJ, Hoefsloot HCJ, et al. Anovasimultaneous component analysis (asca): a new tool for analyzing designed metabolomics data. Bioinformatics 2005; 21(13):3043-8.
8. Van den Brink P, Ter Braak C. Principal response curves: analysis of time-dependent multivariate responses of biological community to stress. EnvironToxicol Chem 1999; 18(2):138-48.
9. Harrington P, Vieira N, Chen P, et al. Proteomic analysis of amniotic fluids using analysis of variance-principal component analysis and fuzzy rule-building expert systems applied to matrix-assisted laser desorption/ionization mass spectrometry. Chemometrics Intell Lab Syst 2006;82: 283-93.
10. Nueda MJ, Conesa A, Westerhuis JA, et al. Discovering gene expression patterns in time course microarray experiments by anova-sca. Bioinformatics 2007;23:1792-800.
11. de Haan J, Wehrens R, Bauerschmidt S, et al. Interpretation of anova models for microarray data using PCA. Bioinformatics 2007;23:184-90.
12. Pinto R, Bosc V, Nocairi H, et al. Using anova-pca for discriminant analysis: Application to the study of mid-infrared spectra of carraghenan gels as a function of concentration and temperature. Anal Chim Acta 2008;629:47-55.
13. Chen P, Lu Y, Harrington P. Biomarker profiling and reproducibility study of maldi-ms measurements of escherichia coli by analysis of variance-principal component analysis. Anal Chem 2008;80:1474-81.
14. Wang J, Reijmers T, Chen L, et al. Systems toxicology study of doxorubicin on rats using ultra performance liquid chromatography coupled with mass spectrometry based metabolomics. Metabolomics 2009;5:407-18.
15. Luciano G, Naes T. Interpreting sensory data by combining principal component analysis and analysis of variance. Food Quality Preference 2009;20(3):167-75.
16. Mazerolles G, Boccard J, Hanafi M, et al. Analysis of experimental design with multivariate response: a contribution using multiblock techniques. Chemometrics Intell Lab Syst 2011;106:65-72.
17. Bouveresse D, Pinto R, Schmidtke L, et al. Identification of significant factors by an extension of anova-pca based on multi-block analysis. Chemometrics Intell Lab Syst 2011;106: 173-82.
18. Timmerman ME, Ter Braak CJF. Bootstrap confidence intervals for principal response curves. Comp Stat Data Anal 2008;52:1837-49.
19. Thissen U, Wopereis S, van den Berg S, etal. Improving the analysis of designed studies by combining statistical modelling with study design information. BMC Bioinformatics 2009;10:52.
20. Derks E. Panca: panel concordance analysis. Food Qual Preference 2010;21:324-9.
21. Stanimirova I, Michalik K, Drzazga Z, et al. Interpretation of analysis of variance models using principal component analysis to assess the effect of a maternal anticancer treatment on the mineralization of rat bones. Anal Chim Acta 2011; 689:1-7.
22. Searle SR. LinearModels.. New York: John Wiley & Sons, 1971.
23. van Velzen E.J.J. Nutrikinetics. PhD Thesis, University of Amsterdam, 2010.
24. Yoshizuka N, Yoshizuka-Chadani Y, Krishnan V, et al. Human immunodeficiency virus type 1 vpr-dependent cell cycle arrest through a mitogen-activated protein kinase signal transduction pathway. JVirol 2005;79:11366-81.