Non-parametric multivariate analysis of variance in the proteomic response of potato to drought stress

Analytica Chimica Acta

Volumn 719, Issue , 2012, Pages 1-7

  Zerzucha, Piotr ^a   Boguszewska, Dominika ^b   Zagdańska, Barbara ^c   Walczak, Beata ^a  

^a UNIVERSITY OF SILESIA   (Poland)

^b PLANT BREEDING AND ACCLIMATIZATION INSTITUTE NATIONAL RESEARCH INSTITUTE   (Poland)

^c WARSAW UNIVERSITY OF LIFE SCIENCES   (Poland)

Author keywords

Biomarker identification; Electrophoresis; Non parametric MANOV; Robust analysis of variance

Indexed keywords

ANALYSIS OF DATA; BIOMARKER IDENTIFICATION; DROUGHT STRESS; FAST TOOL; GEL IMAGES; INDIVIDUAL PROTEINS; MOLECULAR MECHANISM; MULTIVARIATE ANALYSIS OF VARIANCES; NON-PARAMETRIC; PROTEOMIC RESPONSE; REAL DATA SETS; ROBUST ANALYSIS; SPOT DETECTION; STATISTICAL SIGNIFICANCE; VARIANCE ANALYSIS;

ELECTROPHORESIS; PIXELS;

DROUGHT;

ANALYTIC METHOD; ARTICLE; BOOTSTRAPPING; CONFIDENCE INTERVAL; CONTROLLED STUDY; DROUGHT STRESS; IMAGE PROCESSING; ISOELECTRIC FOCUSING; MATHEMATICAL ANALYSIS; METABOLOMICS; MOLECULAR DYNAMICS; MULTIVARIATE ANALYSIS OF VARIANCE; NON PARAMETRIC MULTIVARIATE ANALYSIS OF VARIANCE; NONHUMAN; NUCLEOTIDE SEQUENCE; PLANT BREEDING; POTATO; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROBABILITY; PROTEOMICS; SEQUENCE HOMOLOGY; SPECTROPHOTOMETRY;

DROUGHTS; MULTIVARIATE ANALYSIS; PLANT PROTEINS; PROTEOMICS; SOLANUM TUBEROSUM;

SOLANUM TUBEROSUM;

EID: 84857039804     PISSN: 00032670     EISSN: 18734324     Source Type: Journal    
DOI: 10.1016/j.aca.2011.12.044     Document Type: Article

References (35)

1. Daszykowski M., Stanimirova I., Bodzon-Kulakowska A., Silberring J., Lubec G., Walczak B. The start-to-end processing of two-dimensional gel electrophoretic images. Journal of Chromatography A 2007, 1158:306-317.
2. Faergestad E.M., Rye M., Walczak B., Gidskehaug L., Wold J.P., Grove H., Jia X., Hollung K., Indahl U.G., Westad F., van den Berg F., Martens H. Pixel-based analysis of multiple images for the identification of changes: A novel approach applied to unravel proteome patters of 2-D electrophoresis gel images. Proteomics 2007, 7:3450-3461.
3. Daszykowski M., Bierczynska-Krzysik A., Silberring J., Walczak B. Avoiding spots detection in analysis of electrophoretic gel images. Chemometrics and Intelligent Laboratory Systems 2010, 104:2-7.
4. Boguszewska D., Grudkowska M., Zagdańska B. Drought-responsive antioxidant enzymes in potato (Solanum tuberosum L.). Potato Research 2010, 53:373-382.
5. Anderson M.J. A new method for non-parametric multivariate analysis of variance. Austral Ecology 2001, 26:32-46.
6. Stahle L., Wold S. Analysis of variance (ANOVA). Chemometrics and Intelligent Laboratory Systems 1989, 6:259-272.
7. Hotelling H. The generalization of Student's ratio. Annals of Mathematical Statistics 1931, 2:360-378.
8. Stahle L., Wold S. Multivariate analysis of variance (MANOVA). Chemometrics and Intelligent Laboratory Systems 1990, 9:127-141.
9. de P., Harrington B., Vieira N.E., Espinoza J., Nien J.K., Romero R., Yergey A.L. Analysis of variance-principal component analysis: a soft tool for proteomic discovery. Analytica Chimica Acta 2005, 544:118-127.
10. de P., Harrington B., Vieira N.E., Chen P., Espinoza J., Nien J.K., Romero R., Yergey A.L. Proteomic analysis of amniotic fluids using analysis of variance-principal component analysis and fuzzy rue-building expert systems applied to matrix-assisted laser desorption/ionization mass spectrometry. Chemometrics and Intelligent Laboratory Systems 2006, 82:283-293.
11. Smilde A.K., Jansen J.J., Hoefsloot H.C.J., Van der Greef J., Timmerman M.E. ANOVA-simultaneous component analysis (ASCA): a new toll for analyzing designed metabolomic data. Bioinformatics 2005, 21:3043-3048.
12. Jansen J.J., Hoefsloot H.C.J., Van der Greef J., Timmerman M.E., Westerhuis J.A., Smilde A.K. ASCA: analysis of multivariate data obtained from an experimental design. Journal of Chemometrics 2005, 19:469-481.
13. Climaco-Pinto R., Barros A.S., Locquet N., Schmidtke L., Rutlege R.N. Improving the detection of significant factors using ANOVA-PCA by selective reduction of residual variability. Analytica Chimica Acta 2009, 653:131-142.
14. Jouan-Rimbaud Bouveresse D., Climaco Pinto R., Schmidtke L.M., Locquet N., Rutledge D.N. Identification of significant factors by an extension of ANOVA-PCA based on multi-block analysis. Chemometrics and Intelligent Laboratory Systems 2011, 106:173-182.
15. Vis D.J., Westerhuis J.A., Smilde A.K., van der Greef J. Statistical validation of megavariate effects in ASCA. BMC Bioinformatics 2007, 8:322-329.
16. de Haan J.R., Wehrens R., Bauerschmidt S., Piek E., van Schaik R.C., Buydens L.M.C. Interpretation of ANOVA models for microarray data using PCA. Bioinformatics 2007, 23:184-190.
17. Bray J.R., Curtis J.T. An ordination of the upland forest communities of southern Wisconsin. Ecological Monographs 1957, 27:325-349.
18. Anderson M.J. Permutation tests for univariate or multivariate analysis of variance and regression. Canadian Journal of Fisheries and Aquatic Sciences 2001, 58:626-639.
19. Anderson M.J., ter Braak C.J.F. Permutation tests for multi-factor analysis of variance. Journal of Statistical Computation and Simulation 2003, 73:85-113.
20. de Haan J.R., Bauerschmidt S., van Schaik R.C., Piek E., Buydens L.M.C., Wehrens R. Robust ANOVA for microarray data. Chemometrics and Intelligent Laboratory Systems 2009, 98:38-44.
21. Wehrens R., Putter H., Buydens L.M.C. The bootstrap: a tutorial. Chemometrics and Intelligent Laboratory Systems 2000, 54:35-52.
22. Timmerman M.E., Kiers H.A.L., Smilde A.K. Estimating confidence intervals for principal component loadings: a comparison between the bootstrap and asymptotic results. British Journal of Mathematical and Statistical Psychology 2007, 60:295-314.
23. Schafleitner R., Gutierrez R., Espino R., Gaudin A., Perez J., Martinez M., Dominguez A., Tincopa L., Alvarado C., Numberto G., Bonierhale M. Field screening for variation of drought tolerance in Solanum tuberosum L., by agronomical, physiological and genetic analysis. Potato Research 2007, 50:71-85.
24. Bradford M.M. A rapid and sensitive method for the quantification of microgram quantities of protein utilizing the principle of protein dye-binding. Analytical Biochemistry 1976, 72:248-254.
25. Kaczmarek K., Walczak B., de Jong S., Vandeginste B.G.M Preprocessing of two-dimensional gel electrophoresis images. Proteomics 2004, 4:2377-2389.
26. Eliers P.H.C. Parametric time warping. Analytical Chemistry 2004, 76:404-411.
27. Kaczmarek K., Walczak B., de Jong S., Vandeginste B.G.M. Baseline reduction in two dimensional gel electrophoresis images. Acta Chromatographica 2005, 15:82-96.
28. Kaczmarek K., Walczak B., de Jong S., Vandeginste B.G.M. Matching of 2D gel electrophoresis images. Journal of Chemical Information and Computer Sciences 2003, 43:978-986.
29. Kaczmarek K., Walczak B., de Jong S., Vandeginste B.G.M. Feature based fuzzy matching of 2D gel electrophoresis images. Journal of Chemical Information and Computer Sciences 2002, 42:1431-1442.
30. Walczak B., Wu W. Fuzzy warping of chromatograms. Chemometrics and Intelligent Laboratory Systems 2005, 77:173-180.
31. Daszykowski M., Wrobel M.S., Bierczynska-Krzysik A., Silberring J., Lubec G., Walczak B. Automatic preprocessing of electrophoretic images. Chemometrics and Intelligent Laboratory Systems 2009, 97:132-140.
32. Daszykowski M., Walczak B. Target selection for alignment of chromatographic signals obtained using monochannel detectors. Journal of Chromatography A 2007, 1176:1-11.
33. Perkins D.N., Pappin D.J., Creasy D.M., Cottrell J.S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20:3551-3567.
34. Sato Y., Yokoya S. Enhanced tolerance to drought stress in transgenic rice plants overexpressing a small heat-shock protein, sHSP17.7. Plant Cell Reports 2008, 27:329-334.
35. Jiang C., Xiu J., Zhang H., Zhang X., Shi J., Li M., Ming F. A cytosolic class I small hest shock protein, RcHSP17.8, of Rosa chinensis confers resistance to a variety of stresses to Escherichia coli, yeast and Arabidopsis thaliana. Plant, Cell Environment 2009, 32:1046-1059.