Back to the basics: Maximizing the information obtained by quantitative two dimensional gel electrophoresis analyses by an appropriate experimental design and statistical analyses
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Wasinger, 2005, Progress with gene-product mapping of mollicutes: Mycoplasma genitalium, Electrophoresis, 16, 1090, 10.1002/elps.11501601185
Wilkins, 2006, Guidelines for the next 10years of proteomics, Proteomics, 6, 4, 10.1002/pmic.200500856
Views, 2010, A gene-centric human proteome project, Mol Cell Proteomics, 9, 427, 10.1074/mcp.H900001-MCP200
Jorrín-Novo, 2009, Plant proteomics update (2007–2008): second-generation proteomic techniques, an appropriate experimental design, and data analysis to fulfill MIAPE standards, increase plant proteome coverage and expand biological knowledge, J Proteomics, 72, 285, 10.1016/j.jprot.2009.01.026
Lopez, 2007, Two-dimensional electrophoresis in proteome expression analysis, J Chromatogr B, 849, 190, 10.1016/j.jchromb.2006.11.049
Klose, 1995, Two dimensional electrophoresis of proteins: an updated protocol and implications for a functional analysis of the genome, Electrophoresis, 16, 1034, 10.1002/elps.11501601175
Mead, 2009, Recent developments in public proteomic MS repositories and pipelines, Proteomics, 9, 861, 10.1002/pmic.200800553
Han, 2008, Mass spectrometry for proteomics, Curr Opin Chem Biol, 12, 483, 10.1016/j.cbpa.2008.07.024
Mueller, 2008, An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data, J Proteome Res, 7, 51, 10.1021/pr700758r
Chich, 2007, Statistics for proteomics: experimental design and 2-DE differential analysis, J Chromatogr B, 849, 261, 10.1016/j.jchromb.2006.09.033
Taylor, 2007, The minimum information about a proteomics experiment (MIAPE), Nat Biotechnol, 25, 887, 10.1038/nbt1329
Karp, 2007, Design and analysis issues in quantitative proteomics studies, Pract Proteomics, 7, 43
Berth, 2007, The state of the art in the analysis of two dimensional gel electrophoresis images, Appl Microbiol Biotechnol, 76, 1223, 10.1007/s00253-007-1128-0
Stessl, 2009, Influence of image-analysis software on quantitation of two-dimensional gel electrophoresis data, Electrophoresis, 30, 325, 10.1002/elps.200800213
Sigal, 2006, Variability and memory of protein levels in human cells, Nature, 444, 643, 10.1038/nature05316
Noo, 2006, Current status and prospects of clinical proteomics studies on detection of colorectal cancer: Hopes and fears, World J Gastroenterol, 12, 6594, 10.3748/wjg.v12.i41.6594
Smit, 2008, Statistical data processing in clinical proteomics, J Chromatogr B, 866, 77, 10.1016/j.jchromb.2007.10.042
Rodríguez-Piñeiro AM, Rodríguez-Berrocal FJ, Páez de la Cadena, M. Improvements in the search for potential biomarkers by proteomics: Application of principal component and discriminant analyses for two-dimensional maps evaluation. J Chromatogr B 2007;849:251–60.
Grove, 2008, Combination of statistical approaches for analysis of 2-DE data gives complementary results, J Proteome Res, 7, 5119, 10.1021/pr800424c
Hunt, 2005, Optimal replication and the importance of experimental design for gel-based quantitative proteomics, J Proteome Res, 4, 809, 10.1021/pr049758y
Horgan, 2007, Sample size and replication in 2d gel electrophoresis studies, J Proteome Res, 6, 2884, 10.1021/pr070114a
Rowell, 2005, Modeling biological variability in 2-DE gel proteomic carcinogenesis experiments, J Proteome Res, 4, 1619, 10.1021/pr0501261
Karp, 2005, Impact of replicate types on proteomic expression analysis, J Proteome Res, 4, 1867, 10.1021/pr050084g
Lenth, 2001, Some practical guidelines for effective sample size determination, Am Stat, 55, 187, 10.1198/000313001317098149
Karp, 2007, Experimental and statistical considerations to avoid false conclusions in proteomic studies using differential in-gel electrophoresis, Mol Cell Proteomics, 6, 1354, 10.1074/mcp.M600274-MCP200
Eravci, 2009, Strategies for a reliable bioestadistical analysis of differentially expressed spots from two-dimensional electrophoresis gels, J Proteome Res, 8, 2601, 10.1021/pr800532f
Fuxius, 2008, Technical strategies to reduce the amount of “false significant” results in quantitative proteomics, Proteomics, 8, 1780, 10.1002/pmic.200701074
Kendziorski, 2005, On the utility of pooling biological samples in microarray experiments, Proc Natl Acad Sci U S A, 102, 4252, 10.1073/pnas.0500607102
Oberg, 2009, Statistical design of quantitative mass spectrometry-based proteomic experiments, J Proteome Res, 8, 2144, 10.1021/pr8010099
Karp, 2005, Maximising sensitivity for detecting changes in protein expression: experimental design using minimal CyDyes, Proteomics, 5, 3105, 10.1002/pmic.200500083
Molloy, 2003, Overcoming technical variation and biological variation in quantitative proteomics, Proteomics, 3, 1912, 10.1002/pmic.200300534
Jorge, 2005, The holm oak leaf proteome: analytical and biological variability in the protein expression level assessed by 2-DE and protein identification tandem mass spectrometry de novo sequencing and sequence similarity searching, Proteomics, 5, 222, 10.1002/pmic.200400893
Maldonado, 2008, Evaluation of three different precipitation protocols of protein extraction for Arabidopsis thaliana leaf proteome analysis by two-dimensional electrophoresis, J Proteomics, 71, 461, 10.1016/j.jprot.2008.06.012
Eravci, 2007, Improved comparative proteome analysis based on two-dimensional gel electrophoresis, Proteomics, 7, 513, 10.1002/pmic.200600648
Corthals, 2000, The dynamic range of protein expression: a challenge for proteomic research, Electrophoresis, 21, 1104, 10.1002/(SICI)1522-2683(20000401)21:6<1104::AID-ELPS1104>3.0.CO;2-C
Stainberg, 2009, Protein gel staining methods: an introduction and overview, Meth Enzymol, 463, 541, 10.1016/S0076-6879(09)63031-7
Patton, 2000, A thousand points of light: the application of fluorescence detection technologies to two-dimensional gel electrophoresis and proteomics, Electrophoresis, 6, 1123, 10.1002/(SICI)1522-2683(20000401)21:6<1123::AID-ELPS1123>3.0.CO;2-E
Castillejo, 2009, Differential expression proteomics to investigate responses and resistance to Orobanche crenata in Medicago truncatula, BMC Genomics, 10, 294, 10.1186/1471-2164-10-294
Wheelock, 2006, Effects of post-electrophoretic analysis on variance in gel-based proteomics, Expert Rev Proteomics, 3, 129, 10.1586/14789450.3.1.129
Möller, 2009, Robust features for 2-DE gel image registration, Electrophoresis, 30, 4137, 10.1002/elps.200900293
Kang, 2009, Comparison of three commercially available dige analysis software packages: minimal user intervention in gel-based proteomics, J Proteome Res, 8, 1077, 10.1021/pr800588f
Valledor L, Jorrin-Novo JV, Rodríguez JL, Lenz C. Combined Proteomic and Transcriptomic analysis identifies differentially expressed pathways and stress responses associated to Pinus radiata needle maturation. J Proteome Res 2010;9:3954–79.
Albrecht, 2010, Missing values in gel-based proteomics, Proteomics, 10, 1202, 10.1002/pmic.200800576
Dowsey, 2008, Automated image alignment for 2D gel electrophoresis in a high-throughput proteomics pipeline, Bioinformatics, 24, 950, 10.1093/bioinformatics/btn059
Luhn, 2003, Using standard positions and image fusion to create proteome maps from collections of two-dimensional gel electrophoresis images, Proteomics, 3, 1117, 10.1002/pmic.200300433
Wheelock, 2005, Software-induced variance in two-dimensional gel electrophoresis image analysis, Electrophoresis, 26, 4508, 10.1002/elps.200500253
Grove, 2006, Challenges related to analysis of protein spot volume from two-dimensional gel electrophoresis revealed by replicate gels, J Proteome Res, 5, 3399, 10.1021/pr0603250
Meunier, 2007, Assessment of hierarchical clustering methodologies for protemic data mining, J Proteome Res, 6, 358, 10.1021/pr060343h
Little RJA, Rubin DB. Statistical Analysis with Missing Data, 2nd Edition. Willey Interscience. New Jersey, USA; 2002. ISBN 0-471-18386-5.
Pedreschi, 2008, Treatment of missing values for multivariate statistical analysis of gel-based proteomics data, Proteomics, 8, 1371, 10.1002/pmic.200700975
Kim KY, Yi GS (2008) Sequential KNN imputation method v. 1.0.1. CRAN R project. http://cran.r-project.org/web/packages/SeqKnn/index.html.
R Development Core Team, 2008
Gentleman, 2004, Bioconductor: open software development of computational biology and bioinformatics, Genome Biol, 5, R80, 10.1186/gb-2004-5-10-r80
Karp, 2005, Maximizing sensitivity for detecting changes in protein expression: experimental design using minimal CyDyes, Proteomics, 5, 3105, 10.1002/pmic.200500083
Meleth, 2005, The case of well conducted experiments to validate statistical protocols for 2D gels: different pre-processing=different list of significant proteins, BMC Biotechnol, 5, 7, 10.1186/1472-6750-5-7
Valledor, 2008, Proteomic analysis of Pinus radiata needles: 2-DE map and protein identification by LC/MS/MS and substitution-tolerant database searching, J Proteome Res, 7, 2616, 10.1021/pr7006285
Curto, 2010, 2-DE based proteomic analysis of Saccharomyces cerevisiae wild and K+ transport-affected mutant (trk1, 2) strains at the growth exponential and stationary phases, J Proteomics, 73, 2316, 10.1016/j.jprot.2010.07.003
Sghaier-Hammami, 2009, Proteomic analysis of date palm (Phoenix dactylifera L.) zygotic embryos development and germination, Proteomics, 9, 2543, 10.1002/pmic.200800523
Zhao, 2009, Quantitative proteomics and biomarker discovery in human cancer, Expert Rev Proteomics, 6, 115, 10.1586/epr.09.8
Kreutz, 2008, Systems biology: experimental design, FEBS J, 276, 923, 10.1111/j.1742-4658.2008.06843.x
Daniel, 2009, Biostatistics: basic concepts and methodology for the health sciences
Dudoit, 2004, Multiple testing. part I. single-step procedures for control of general type I error rates, Stat Appl Genet Mol Biol, 3, 10.2202/1544-6115.1040
Fay, 2009, Wilcoxon–Mann–Whitney or t-test? On assumptions for hypothesis tests and multiple interpretation of decision rules, Stat Surv, 4, 1, 10.1214/09-SS051
Brien, 2006, Multiple randomizations (with discussion), J R Stat Soc B, 68, 571, 10.1111/j.1467-9868.2006.00557.x
Kerr, 2001, Experimental design for gene expression microarrays, Biostatistics, 2, 183, 10.1093/biostatistics/2.2.183
Strassburger, 2008, Compatible simultaneous lower confidence bounds for the Holm procedure and other Bonferroni-based closed tests, Stat Med, 27, 4914, 10.1002/sim.3338
Shaffer, 1995, Multiple hypothesis testing, Annu Rev Psychol, 46, 561, 10.1146/annurev.ps.46.020195.003021
Benjamini, 1995, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J R Stat Soc B, 57, 289
Storey, 2002, A direct approach to false discovery rates, J R Stat Soc B, 64, 479, 10.1111/1467-9868.00346
Gold, 2009, Error control variability in pathway-based microarray analysis, Bioinformatics, 25, 2216, 10.1093/bioinformatics/btp385
Verhoeven, 2005, Implementing false discovery rate control: increasing your power, Oikos, 108, 643, 10.1111/j.0030-1299.2005.13727.x
Martens, 2001
Nedenskov Jensen, 2008, Multivariate data analysis of two-dimensional gel electrophoresis protein patterns from few samples, J Proteome Res, 7, 1288, 10.1021/pr700800s
Schultz, 2004, Explorative data analysis of two-dimensional electrophoresis gels, Electrophoresis, 25, 502, 10.1002/elps.200305715
Jacobsen, 2007, Multivariate of 2-DE protein patterns—practical approaches, Electrophoresis, 28, 1289, 10.1002/elps.200600414
Valero J, Echevarría-Zomeño S, Ariza D, Valledor L, Jorge I, Navarro-Cerrillo R, Jorrín JV. Proteomics of Holm Oak (Quercus ilex). COST FA0603 "Plant proteomics in Europe" WG2 & MC meeting abstract book ISBN: 978-80-89088-83-6. Edited by: IPGB, SAS.
Hyvärinen, 1997, A fast fixed—point algorithm for independent component analysis, Neural Comput, 9, 1483, 10.1162/neco.1997.9.7.1483
Hyvärinen, 2001
Hyvärinen, 2000, Independent component analysis: algorithms and applications, Neural Netw, 13, 411, 10.1016/S0893-6080(00)00026-5
Safavi, 2008, Independent component analysis of 2-D electrophoresis gels, Electrophoresis, 29, 4017, 10.1002/elps.200800028
Morgenthal, 2005, Correlative GC–TOF–MS–based metabolite profiling and LC–MS–based protein profiling reveal time-related systemic regulation of metabolite-protein networks and improve pattern recognition for multiple biomarker selection, Metabolomics, 1, 109, 10.1007/s11306-005-4430-9
Stone, 2004
Karp, 2005, Application of partial least squares discriminant analysis to two-dimensional difference gel studies in expression proteomics, Proteomics, 5, 81, 10.1002/pmic.200400881
Kohonen, 2006, Self-organizing neural projections, Neural Netw, 19, 723, 10.1016/j.neunet.2006.05.001
Weherns, 2007, Self- and super-organizing maps in r: the Kohonen package, J Stat Softw, 21, 1
Tamayo, 1999, Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation, Proc Natl Acad Sci U S A, 96, 2907, 10.1073/pnas.96.6.2907
Laboratory of Computer and Information Science. University of Helsinki. http://www.cis.hut.fi/research/ (Cited May 25th, 2010).
Germano T. Self Organizing Maps. http://davis.wpi.edu/matt/courses/soms/ (Cited May 25th, 2010).
Seiler, 2010, Consensus cluster: a software tool for unsupervised cluster discovery in numerical data, OMICS J Integr Biol, 14, 109, 10.1089/omi.2009.0083
Eisen, 1998, Cluster analysis and display of genome-wide expression patterns, Proc Natl Acad Sci U S A, 95, 14863, 10.1073/pnas.95.25.14863
de Hoon, 2004, Open source clustering software, Bioinformatics, 20, 1453, 10.1093/bioinformatics/bth078
Saldanha, 2004, Java treeview-extensible visualization of microarray data, Bioinformatics, 20, 3246, 10.1093/bioinformatics/bth349
Morgan, 1995, Non-uniqueness and inversions in cluster analysis, Appl Stat, 44, 117, 10.2307/2986199