Biological assessment of robust noise models in microarray data analysis
Motivation: Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying obser...
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| Published in | Bioinformatics Vol. 27; no. 6; pp. 807 - 814 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Oxford University Press
15.03.2011
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1367-4811 1460-2059 |
| DOI | 10.1093/bioinformatics/btr018 |
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| Abstract | Motivation: Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus important and of general interest.
Results: We propose to this end assessing different noise models on a large number of microarray experiments. The goodness of fit of noise models is quantified by a hierarchical Bayesian analysis of variance model, which predicts normalized expression values as a mixture of a Gaussian density and t-distributions with adjustable degrees of freedom. Inference of differentially expressed genes is taken into consideration at a second mixing level. For attaining far reaching validity, our investigations cover a wide range of analysis platforms and experimental settings. As the most striking result, we find irrespective of the chosen preprocessing and normalization method in all experiments that a heavy-tailed noise model is a better fit than a simple Gaussian. Further investigations revealed that an appropriate choice of noise model has a considerable influence on biological interpretations drawn at the level of inferred genes and gene ontology terms. We conclude from our investigation that neglecting the over dispersed noise in microarray data can mislead scientific discovery and suggest that the convenience of Gaussian-based modelling should be replaced by non-parametric approaches or other methods that account for heavy-tailed noise.
Contact: peter.sykacek@boku.ac.at
Availability: http://bioinf.boku.ac.at/alexp/robmca.html. |
|---|---|
| AbstractList | Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus important and of general interest.
We propose to this end assessing different noise models on a large number of microarray experiments. The goodness of fit of noise models is quantified by a hierarchical Bayesian analysis of variance model, which predicts normalized expression values as a mixture of a Gaussian density and t-distributions with adjustable degrees of freedom. Inference of differentially expressed genes is taken into consideration at a second mixing level. For attaining far reaching validity, our investigations cover a wide range of analysis platforms and experimental settings. As the most striking result, we find irrespective of the chosen preprocessing and normalization method in all experiments that a heavy-tailed noise model is a better fit than a simple Gaussian. Further investigations revealed that an appropriate choice of noise model has a considerable influence on biological interpretations drawn at the level of inferred genes and gene ontology terms. We conclude from our investigation that neglecting the over dispersed noise in microarray data can mislead scientific discovery and suggest that the convenience of Gaussian-based modelling should be replaced by non-parametric approaches or other methods that account for heavy-tailed noise. Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus important and of general interest.MOTIVATIONAlthough several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus important and of general interest.We propose to this end assessing different noise models on a large number of microarray experiments. The goodness of fit of noise models is quantified by a hierarchical Bayesian analysis of variance model, which predicts normalized expression values as a mixture of a Gaussian density and t-distributions with adjustable degrees of freedom. Inference of differentially expressed genes is taken into consideration at a second mixing level. For attaining far reaching validity, our investigations cover a wide range of analysis platforms and experimental settings. As the most striking result, we find irrespective of the chosen preprocessing and normalization method in all experiments that a heavy-tailed noise model is a better fit than a simple Gaussian. Further investigations revealed that an appropriate choice of noise model has a considerable influence on biological interpretations drawn at the level of inferred genes and gene ontology terms. We conclude from our investigation that neglecting the over dispersed noise in microarray data can mislead scientific discovery and suggest that the convenience of Gaussian-based modelling should be replaced by non-parametric approaches or other methods that account for heavy-tailed noise.RESULTSWe propose to this end assessing different noise models on a large number of microarray experiments. The goodness of fit of noise models is quantified by a hierarchical Bayesian analysis of variance model, which predicts normalized expression values as a mixture of a Gaussian density and t-distributions with adjustable degrees of freedom. Inference of differentially expressed genes is taken into consideration at a second mixing level. For attaining far reaching validity, our investigations cover a wide range of analysis platforms and experimental settings. As the most striking result, we find irrespective of the chosen preprocessing and normalization method in all experiments that a heavy-tailed noise model is a better fit than a simple Gaussian. Further investigations revealed that an appropriate choice of noise model has a considerable influence on biological interpretations drawn at the level of inferred genes and gene ontology terms. We conclude from our investigation that neglecting the over dispersed noise in microarray data can mislead scientific discovery and suggest that the convenience of Gaussian-based modelling should be replaced by non-parametric approaches or other methods that account for heavy-tailed noise. Motivation: Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus important and of general interest. Results: We propose to this end assessing different noise models on a large number of microarray experiments. The goodness of fit of noise models is quantified by a hierarchical Bayesian analysis of variance model, which predicts normalized expression values as a mixture of a Gaussian density and t-distributions with adjustable degrees of freedom. Inference of differentially expressed genes is taken into consideration at a second mixing level. For attaining far reaching validity, our investigations cover a wide range of analysis platforms and experimental settings. As the most striking result, we find irrespective of the chosen preprocessing and normalization method in all experiments that a heavy-tailed noise model is a better fit than a simple Gaussian. Further investigations revealed that an appropriate choice of noise model has a considerable influence on biological interpretations drawn at the level of inferred genes and gene ontology terms. We conclude from our investigation that neglecting the over dispersed noise in microarray data can mislead scientific discovery and suggest that the convenience of Gaussian-based modelling should be replaced by non-parametric approaches or other methods that account for heavy-tailed noise. Contact: peter.sykacek@boku.ac.at Availability: http://bioinf.boku.ac.at/alexp/robmca.html. Motivation: Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus important and of general interest.Results: We propose to this end assessing different noise models on a large number of microarray experiments. The goodness of fit of noise models is quantified by a hierarchical Bayesian analysis of variance model, which predicts normalized expression values as a mixture of a Gaussian density and t-distributions with adjustable degrees of freedom. Inference of differentially expressed genes is taken into consideration at a second mixing level. For attaining far reaching validity, our investigations cover a wide range of analysis platforms and experimental settings. As the most striking result, we find irrespective of the chosen preprocessing and normalization method in all experiments that a heavy-tailed noise model is a better fit than a simple Gaussian. Further investigations revealed that an appropriate choice of noise model has a considerable influence on biological interpretations drawn at the level of inferred genes and gene ontology terms. We conclude from our investigation that neglecting the over dispersed noise in microarray data can mislead scientific discovery and suggest that the convenience of Gaussian-based modelling should be replaced by non-parametric approaches or other methods that account for heavy-tailed noise.Contact: peter.sykacekoku.ac.atAvailability: http://bioinf.boku.ac.at/alexp/robmca.html. Motivation: Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus important and of general interest. Results: We propose to this end assessing different noise models on a large number of microarray experiments. The goodness of fit of noise models is quantified by a hierarchical Bayesian analysis of variance model, which predicts normalized expression values as a mixture of a Gaussian density and t-distributions with adjustable degrees of freedom. Inference of differentially expressed genes is taken into consideration at a second mixing level. For attaining far reaching validity, our investigations cover a wide range of analysis platforms and experimental settings. As the most striking result, we find irrespective of the chosen preprocessing and normalization method in all experiments that a heavy-tailed noise model is a better fit than a simple Gaussian. Further investigations revealed that an appropriate choice of noise model has a considerable influence on biological interpretations drawn at the level of inferred genes and gene ontology terms. We conclude from our investigation that neglecting the over dispersed noise in microarray data can mislead scientific discovery and suggest that the convenience of Gaussian-based modelling should be replaced by non-parametric approaches or other methods that account for heavy-tailed noise. Contact: peter.sykacek@boku.ac.at Availability: http://bioinf.boku.ac.at/alexp/robmca.html. |
| Author | Posekany, A. Sykacek, P. Felsenstein, K. |
| AuthorAffiliation | 1 Chair of Bioinformatics, Department of Biotechnology, University of Natural Resources and Life Sciences, Gregor Mendel Straße 33, 1180, Vienna and 2 Department of Statistics, Vienna University of Technology, Karlsplatz 13, 1040 Vienna, Austria |
| AuthorAffiliation_xml | – name: 1 Chair of Bioinformatics, Department of Biotechnology, University of Natural Resources and Life Sciences, Gregor Mendel Straße 33, 1180, Vienna and 2 Department of Statistics, Vienna University of Technology, Karlsplatz 13, 1040 Vienna, Austria |
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| Cites_doi | 10.1186/1471-2105-9-S1-S9 10.1093/biomet/82.4.711 10.1152/physiolgenomics.00030.2006 10.1162/neco.1992.4.3.415 10.1186/1471-2105-6-186 10.1124/jpet.103.053256 10.1093/bioinformatics/bti583 10.1095/biolreprod.104.033696 10.1111/1541-0420.00064 10.1016/j.chemolab.2009.04.011 10.1016/j.neurobiolaging.2007.01.014 10.1186/cc1820 10.1093/hmg/ddi457 10.1093/bioinformatics/btg455 10.1093/bioinformatics/btf879 10.1002/9780470316870 10.1093/nar/30.1.207 10.1007/978-1-4757-4145-2 10.1093/biostatistics/kxp003 10.1093/bioinformatics/18.suppl_1.S96 10.1007/BF02562676 10.1098/rstb.2007.2129 10.1093/bioinformatics/17.6.509 10.1158/1078-0432.CCR-05-0683 10.1371/journal.pone.0001504 10.1093/nar/30.4.e15 10.1038/75556 10.2202/1544-6115.1314 10.1198/016214503000224 10.1073/pnas.091062498 10.1111/j.1541-0420.2005.00397.x 10.1093/bioinformatics/19.2.185 10.1093/bioinformatics/btm280 10.1081/BIP-200067778 10.1186/gb-2003-4-9-r60 10.1198/016214502753479257 10.1186/gb-2005-6-2-r16 10.1093/bioinformatics/btg311 10.1016/j.nut.2003.10.002 10.1007/0-387-29362-0_23 10.1093/bioinformatics/btl361 10.1186/1745-6150-1-27 10.1093/bioinformatics/bth419 10.1186/1471-2164-8-319 10.1093/biostatistics/4.2.249 10.1073/pnas.0308512100 10.1186/1471-2105-7-448 10.1111/j.1399-3054.2007.01002.x 10.1126/science.1153795 10.1093/bioinformatics/18.11.1454 10.1038/oby.2007.116 10.2202/1544-6115.1590 |
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| Keywords | Data analysis Data Microarray Noise Biological model |
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| References | Holmes (2023012511554980200_B22) 2006; 1 Gao (2023012511554980200_B16) 2005; 6 Troyanskaya (2023012511554980200_B49) 2002; 18 Bolstad (2023012511554980200_B9) 2003; 19 MacKay (2023012511554980200_B35) 1992; 4 Whitley (2023012511554980200_B53) 2002; 6 Tadesse (2023012511554980200_B47) 2003; 59 Yang (2023012511554980200_B54) 2002; 30 Blalock (2023012511554980200_B8) 2004; 101 Choe (2023012511554980200_B11) 2005; 6 Plummer (2023012511554980200_B39) 2006; 6 Liu (2023012511554980200_B33) 2005; 21 Van Hoewyk (2023012511554980200_B52) 2008; 132 Upton (2023012511554980200_B51) 2010; 9 de Haan (2023012511554980200_B12) 2009; 98 Huber (2023012511554980200_B24) 2002; 18 Li (2023012511554980200_B32) 2008; 16 Edgar (2023012511554980200_B15) 2002; 30 Novak (2023012511554980200_B38) 2006; 1 Ishwaran (2023012511554980200_B27) 2003; 98 Lee (2023012511554980200_B30) 2005; 15 Middleton (2023012511554980200_B37) 2004; 20 Affara (2023012511554980200_B1) 2007; 362 Jin (2023012511554980200_B29) 2003; 307 Somel (2023012511554980200_B44) 2008; 3 Zhao (2023012511554980200_B56) 2008; 9 Tusher (2023012511554980200_B50) 2001; 98 Irizarry (2023012511554980200_B26) 2003; 31 Small (2023012511554980200_B42) 2005; 72 Someya (2023012511554980200_B45) 2008; 29 Sykacek (2023012511554980200_B46) 2007; 23 Jeffreys (2023012511554980200_B28) 1961 Giles (2023012511554980200_B17) 2003; 19 Gottardo (2023012511554980200_B19) 2006; 62 Baldi (2023012511554980200_B5) 2001; 17 Cameron (2023012511554980200_B10) 2005; 11 Liu (2023012511554980200_B34) 2006; 22 Talantov (2023012511554980200_B48) 2005; 11 Dinneny (2023012511554980200_B14) 2008; 320 Bae (2023012511554980200_B4) 2004; 20 Shahbaba (2023012511554980200_B41) 2006; 7 Dennis (2023012511554980200_B13) 2003; 4 Huang (2023012511554980200_B23) 2002; 58 Ibrahim (2023012511554980200_B25) 2002; 97 Bernardo (2023012511554980200_B7) 1994 Lewin (2023012511554980200_B31) 2007; 6 Yao (2023012511554980200_B55) 2007; 8 Zimmerman (2023012511554980200_B58) 2006; 27 Green (2023012511554980200_B20) 1995; 82 Zhao (2023012511554980200_B57) 2003; 19 Robert (2023012511554980200_B40) 2004 Ashburner (2023012511554980200_B3) 2000; 25 Hardin (2023012511554980200_B21) 2009; 10 MacLennan (2023012511554980200_B36) 2006; 15 Smyth (2023012511554980200_B43) 2005 Al-Shahrour (2023012511554980200_B2) 2004; 20 Gilks (2023012511554980200_B18) 1996 Berger (2023012511554980200_B6) 1994; 3 16368706 - Hum Mol Genet. 2006 Feb 1;15(3):405-15 12801864 - Bioinformatics. 2003 Jun 12;19(9):1046-54 12169536 - Bioinformatics. 2002;18 Suppl 1:S96-104 12493072 - Crit Care. 2002 Dec;6(6):509-13 18171320 - Stat Appl Genet Mol Biol. 2007;6:Article36 18231591 - PLoS One. 2008;3(1):e1504 17038174 - BMC Bioinformatics. 2006;7:448 21044041 - Stat Appl Genet Mol Biol. 2010;9:Article37 15693945 - Genome Biol. 2005;6(2):R16 19276243 - Biostatistics. 2009 Jul;10(3):446-50 12808002 - J Pharmacol Exp Ther. 2003 Oct;307(1):93-109 18436742 - Science. 2008 May 16;320(5878):942-5 16820429 - Bioinformatics. 2006 Sep 1;22(17):2107-13 11395427 - Bioinformatics. 2001 Jun;17(6):509-19 14990455 - Bioinformatics. 2004 Mar 1;20(4):578-80 16042764 - BMC Bioinformatics. 2005;6:186 18315862 - BMC Bioinformatics. 2008;9 Suppl 1:S9 17540682 - Bioinformatics. 2007 Aug 1;23(15):1936-44 18251864 - Physiol Plant. 2008 Feb;132(2):236-53 12925520 - Biostatistics. 2003 Apr;4(2):249-64 11752295 - Nucleic Acids Res. 2002 Jan 1;30(1):207-10 12795414 - Recent Prog Horm Res. 2003;58:55-73 11842121 - Nucleic Acids Res. 2002 Feb 15;30(4):e15 14630654 - Bioinformatics. 2003 Nov 22;19(17):2254-62 18239588 - Obesity (Silver Spring). 2008 Apr;16(4):811-8 15256404 - Bioinformatics. 2004 Dec 12;20(18):3423-30 16078385 - J Biopharm Stat. 2005;15(5):783-97 16959036 - Biol Direct. 2006 Sep 07;1:27 16020470 - Bioinformatics. 2005 Sep 15;21(18):3637-44 17363114 - Neurobiol Aging. 2008 Jul;29(7):1080-92 16243793 - Clin Cancer Res. 2005 Oct 15;11(20):7234-42 15496517 - Biol Reprod. 2005 Feb;72(2):492-501 17850668 - BMC Genomics. 2007;8:319 16954408 - Physiol Genomics. 2006 Nov 27;27(3):337-50 14769913 - Proc Natl Acad Sci U S A. 2004 Feb 17;101(7):2173-8 10802651 - Nat Genet. 2000 May;25(1):25-9 16542223 - Biometrics. 2006 Mar;62(1):10-8 11309499 - Proc Natl Acad Sci U S A. 2001 Apr 24;98(9):5116-21 14698009 - Nutrition. 2004 Jan;20(1):14-25 14601755 - Biometrics. 2003 Sep;59(3):542-54 12734009 - Genome Biol. 2003;4(5):P3 17569639 - Philos Trans R Soc Lond B Biol Sci. 2007 Aug 29;362(1484):1469-87 12538238 - Bioinformatics. 2003 Jan 22;19(2):185-93 12424116 - Bioinformatics. 2002 Nov;18(11):1454-61 16205622 - Mol Vis. 2005;11:775-91 |
| References_xml | – volume: 9 start-page: S9 issue: Suppl. 1 year: 2008 ident: 2023012511554980200_B56 article-title: Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-S1-S9 – volume: 82 start-page: 711 year: 1995 ident: 2023012511554980200_B20 article-title: Reversible jump Markov Chain Monte Carlo computation and Bayesian model determination publication-title: Biometrika doi: 10.1093/biomet/82.4.711 – volume: 27 start-page: 337 year: 2006 ident: 2023012511554980200_B58 article-title: Multiple mechanisms limit the duration of wakefulness in Drosophila brain publication-title: Physiol. Genomics doi: 10.1152/physiolgenomics.00030.2006 – volume: 6 start-page: 7 year: 2006 ident: 2023012511554980200_B39 article-title: CODA: convergence diagnosis and output analysis for MCMC publication-title: R. News – volume: 4 start-page: 415 year: 1992 ident: 2023012511554980200_B35 article-title: Bayesian interpolation publication-title: Neural Comput. doi: 10.1162/neco.1992.4.3.415 – volume: 6 start-page: 186 year: 2005 ident: 2023012511554980200_B16 article-title: Nonparametric tests for differential gene expression and interaction effects in multi-factorial microarray experiments publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-6-186 – volume: 307 start-page: 93 year: 2003 ident: 2023012511554980200_B29 article-title: Modeling of corticosteroid pharmacogenomics in rat liver using gene microarrays publication-title: J. Pharmalcol. Exp. Ther. doi: 10.1124/jpet.103.053256 – volume: 21 start-page: 3637 year: 2005 ident: 2023012511554980200_B33 article-title: A tractable probabilistic model for affymetrix probe-level analysis across multiple chips publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti583 – volume: 72 start-page: 492 year: 2005 ident: 2023012511554980200_B42 article-title: Profiling gene expression during the differentiation and development of the murine embryonic gonad publication-title: Biol. Reprod. doi: 10.1095/biolreprod.104.033696 – volume: 59 start-page: 542 year: 2003 ident: 2023012511554980200_B47 article-title: Identification of differentially expressed genes in high-density oligonucleotide arrays accounting for the quantification limits of the technology publication-title: Biometrics doi: 10.1111/1541-0420.00064 – volume: 98 start-page: 38 year: 2009 ident: 2023012511554980200_B12 article-title: Robust anova for microarray data publication-title: Chemometr. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2009.04.011 – volume: 29 start-page: 1080 year: 2008 ident: 2023012511554980200_B45 article-title: The role of mtdna mutations in the pathogenesis of age-related hearing loss in mice carrying a mutator dna polymerase gamma publication-title: Neurobiol. Aging doi: 10.1016/j.neurobiolaging.2007.01.014 – volume: 6 start-page: 509 year: 2002 ident: 2023012511554980200_B53 article-title: Statistics review 6: nonparametric methods publication-title: Crit. Care doi: 10.1186/cc1820 – volume: 15 start-page: 405 year: 2006 ident: 2023012511554980200_B36 article-title: Targeted disruption of glycerol kinase gene in mice: expression analysis in liver shows alterations in network partners related to glycerol kinase activity publication-title: Hum. Mol. Genet. doi: 10.1093/hmg/ddi457 – volume: 20 start-page: 578 year: 2004 ident: 2023012511554980200_B2 article-title: Fatigo: a web tool for finding significant association of gene ontology terms with groups of genes publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg455 – volume: 19 start-page: 1046 year: 2003 ident: 2023012511554980200_B57 article-title: Modified nonparametric approaches to detecting differentially expressed genes in replicated microarray experiments publication-title: Bioinformatics doi: 10.1093/bioinformatics/btf879 – volume-title: Bayesian Theory. year: 1994 ident: 2023012511554980200_B7 doi: 10.1002/9780470316870 – volume: 30 start-page: 207 year: 2002 ident: 2023012511554980200_B15 article-title: Gene expression omnibus: NCBI gene expression and hybridization array data repository publication-title: Nucleic Acid Res. doi: 10.1093/nar/30.1.207 – volume-title: Monte Carlo Statistical Methods. year: 2004 ident: 2023012511554980200_B40 doi: 10.1007/978-1-4757-4145-2 – volume: 10 start-page: 446 year: 2009 ident: 2023012511554980200_B21 article-title: A note on oligonucleotide expression values not being normally distributed publication-title: Biostatistics doi: 10.1093/biostatistics/kxp003 – volume: 18 start-page: S96 issue: Suppl. 1 year: 2002 ident: 2023012511554980200_B24 article-title: Variance stabilization applied to microarray data calibration and to the quantification of differential expression publication-title: Bioinformaics doi: 10.1093/bioinformatics/18.suppl_1.S96 – volume: 3 start-page: 5 year: 1994 ident: 2023012511554980200_B6 article-title: An overview of robust Bayesian analysis publication-title: Test doi: 10.1007/BF02562676 – volume: 362 start-page: 1469 year: 2007 ident: 2023012511554980200_B1 article-title: Understanding endothelial cell apoptosis: what can the transcriptome, glycome and proteome reveal? publication-title: Philos. Trans. R. Soc. B doi: 10.1098/rstb.2007.2129 – volume: 17 start-page: 509 year: 2001 ident: 2023012511554980200_B5 article-title: A bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes publication-title: Bioinformatics doi: 10.1093/bioinformatics/17.6.509 – volume: 11 start-page: 7234 year: 2005 ident: 2023012511554980200_B48 article-title: Novel genes associated with malignant melanoma but not benign melanocytic lesions publication-title: Clin. Cancer Res. doi: 10.1158/1078-0432.CCR-05-0683 – volume-title: Theory of Probability year: 1961 ident: 2023012511554980200_B28 – volume: 3 start-page: e1504 year: 2008 ident: 2023012511554980200_B44 article-title: Human and chimpanzee gene expression differences replicated in mice fed different diets publication-title: PLoS One doi: 10.1371/journal.pone.0001504 – volume: 30 start-page: e15 year: 2002 ident: 2023012511554980200_B54 article-title: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation publication-title: Nucleic Acid Res. doi: 10.1093/nar/30.4.e15 – volume: 25 start-page: 25 year: 2000 ident: 2023012511554980200_B3 article-title: Gene ontology: tool for the unification of biology. the gene ontology consortium publication-title: Nat. Genet. doi: 10.1038/75556 – volume: 6 year: 2007 ident: 2023012511554980200_B31 article-title: Fully Bayesian mixture model for differential gene expression: simulations and model checks publication-title: Stat. Appl. Genet. Mol. Biol. doi: 10.2202/1544-6115.1314 – volume: 98 start-page: 438 year: 2003 ident: 2023012511554980200_B27 article-title: Detecting differentially expressed gene in microarrays using Bayesian model selection publication-title: J. Am. Stat. Assoc. doi: 10.1198/016214503000224 – volume: 98 start-page: 5116 year: 2001 ident: 2023012511554980200_B50 article-title: Significance analysis of microarrays applied to the ionizing radiation response publication-title: Proc. Natl Acad. Sci. doi: 10.1073/pnas.091062498 – volume: 62 start-page: 10 year: 2006 ident: 2023012511554980200_B19 article-title: Bayesian robust inference for differential gene expression in microarrays with multiple samples publication-title: Biometrics doi: 10.1111/j.1541-0420.2005.00397.x – volume: 19 start-page: 185 year: 2003 ident: 2023012511554980200_B9 article-title: A comparison of normalization methods for high density oligonucleotide array data based on bias and variance publication-title: Bioinformatics doi: 10.1093/bioinformatics/19.2.185 – volume: 23 start-page: 1936 year: 2007 ident: 2023012511554980200_B46 article-title: Bayesian modelling of shared gene function publication-title: Bioinformatics doi: 10.1093/bioinformatics/btm280 – volume: 15 start-page: 783 year: 2005 ident: 2023012511554980200_B30 article-title: Nonparametric methods for microarray data based on exchangeability and borrowed power publication-title: J. Biopharm. Stat. doi: 10.1081/BIP-200067778 – volume: 4 start-page: R60 year: 2003 ident: 2023012511554980200_B13 article-title: DAVID: Database for Annotation, Visualization, and Integrated Discovery publication-title: Genome Biol. doi: 10.1186/gb-2003-4-9-r60 – volume: 58 start-page: 55 year: 2002 ident: 2023012511554980200_B23 article-title: Gene expression profiling for prediction of clinical characteristics of breast cancer publication-title: Hormone Res. – volume: 97 start-page: 88 year: 2002 ident: 2023012511554980200_B25 article-title: Bayesian models for gene expression with dna microarray data publication-title: J. Am. Stat. Assoc. doi: 10.1198/016214502753479257 – volume: 1 start-page: 145 year: 2006 ident: 2023012511554980200_B22 article-title: Bayesian auxiliary variable models for binary and multinomial regression publication-title: Bayesian Anal. – volume: 6 start-page: R16 year: 2005 ident: 2023012511554980200_B11 article-title: Preferred analysis methods for affymetrix genechips revealed by a wholly defined control dataset publication-title: Genome Biol. doi: 10.1186/gb-2005-6-2-r16 – volume: 19 start-page: 2254 year: 2003 ident: 2023012511554980200_B17 article-title: Normality of oligonucleotide microarray data and implications for parametric statistical analyses publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg311 – volume: 20 start-page: 14 year: 2004 ident: 2023012511554980200_B37 article-title: Application of genomic technologies: DNA microarrays and metabolic profiling of obesity in the hypothalamus and in subcutaneous fat publication-title: Nutrition doi: 10.1016/j.nut.2003.10.002 – start-page: 397 volume-title: Bioinformatics and Computational Biology Solutions using R and BioConductor. year: 2005 ident: 2023012511554980200_B43 article-title: Limma: linear models for microarray data doi: 10.1007/0-387-29362-0_23 – volume: 22 start-page: 2107 year: 2006 ident: 2023012511554980200_B34 article-title: Probe-level measurement error improves accuracy in detecting differential gene expression publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl361 – volume: 1 start-page: 27 year: 2006 ident: 2023012511554980200_B38 article-title: Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution publication-title: Biol. Direct doi: 10.1186/1745-6150-1-27 – volume: 20 start-page: 3423 year: 2004 ident: 2023012511554980200_B4 article-title: Gene selection using a two-level hierarchical bayesian model publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth419 – volume: 8 start-page: 319 year: 2007 ident: 2023012511554980200_B55 article-title: A Marfan syndrome gene expression phenotype in cultured skin fibroblasts publication-title: BMC Genomics doi: 10.1186/1471-2164-8-319 – volume-title: Markov Chain Monte Carlo in Practice. year: 1996 ident: 2023012511554980200_B18 – volume: 31 start-page: 249 year: 2003 ident: 2023012511554980200_B26 article-title: Exploration, normalization, and summaries of high density oligonucleotide array probe level data publication-title: Biostatistics doi: 10.1093/biostatistics/4.2.249 – volume: 101 start-page: 2173 year: 2004 ident: 2023012511554980200_B8 article-title: Incipient alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses publication-title: Proc. Natl Acad. Sci. doi: 10.1073/pnas.0308512100 – volume: 7 start-page: 448 year: 2006 ident: 2023012511554980200_B41 article-title: Gene function classification using Bayesian models with hierarchy-based priors publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-7-448 – volume: 132 start-page: 236 year: 2008 ident: 2023012511554980200_B52 article-title: Transcriptome analyses give insights into selenium-stress responses and selenium tolerance mechanisms in arabidopsis publication-title: Physiol. Plant. doi: 10.1111/j.1399-3054.2007.01002.x – volume: 11 start-page: 775 year: 2005 ident: 2023012511554980200_B10 article-title: Gene expression profiles of intact and regenerating zebrafish retina publication-title: Mol. Vis. – volume: 320 start-page: 942 year: 2008 ident: 2023012511554980200_B14 article-title: Cell identity mediates the response of Arabidopsis roots to abiotic stress publication-title: Science doi: 10.1126/science.1153795 – volume: 18 start-page: 1454 year: 2002 ident: 2023012511554980200_B49 article-title: Nonparametric methods for identifying differentially expressed genes in microarray data publication-title: Bioinformatics doi: 10.1093/bioinformatics/18.11.1454 – volume: 16 start-page: 811 year: 2008 ident: 2023012511554980200_B32 article-title: Assessment of diet-induced obese rats as an obesity model by comparative functional genomics publication-title: Obesity doi: 10.1038/oby.2007.116 – volume: 9 year: 2010 ident: 2023012511554980200_B51 article-title: The detection of blur in Affymetrix GeneChips publication-title: Stat. Appl. Genet. Mol. Biol. doi: 10.2202/1544-6115.1590 – reference: 16954408 - Physiol Genomics. 2006 Nov 27;27(3):337-50 – reference: 18315862 - BMC Bioinformatics. 2008;9 Suppl 1:S9 – reference: 18251864 - Physiol Plant. 2008 Feb;132(2):236-53 – reference: 12734009 - Genome Biol. 2003;4(5):P3 – reference: 16020470 - Bioinformatics. 2005 Sep 15;21(18):3637-44 – reference: 16078385 - J Biopharm Stat. 2005;15(5):783-97 – reference: 16959036 - Biol Direct. 2006 Sep 07;1:27 – reference: 18239588 - Obesity (Silver Spring). 2008 Apr;16(4):811-8 – reference: 11395427 - Bioinformatics. 2001 Jun;17(6):509-19 – reference: 14601755 - Biometrics. 2003 Sep;59(3):542-54 – reference: 11752295 - Nucleic Acids Res. 2002 Jan 1;30(1):207-10 – reference: 17540682 - Bioinformatics. 2007 Aug 1;23(15):1936-44 – reference: 12538238 - Bioinformatics. 2003 Jan 22;19(2):185-93 – reference: 12925520 - Biostatistics. 2003 Apr;4(2):249-64 – reference: 14698009 - Nutrition. 2004 Jan;20(1):14-25 – reference: 16042764 - BMC Bioinformatics. 2005;6:186 – reference: 11309499 - Proc Natl Acad Sci U S A. 2001 Apr 24;98(9):5116-21 – reference: 19276243 - Biostatistics. 2009 Jul;10(3):446-50 – reference: 12169536 - Bioinformatics. 2002;18 Suppl 1:S96-104 – reference: 16205622 - Mol Vis. 2005;11:775-91 – reference: 17363114 - Neurobiol Aging. 2008 Jul;29(7):1080-92 – reference: 16243793 - Clin Cancer Res. 2005 Oct 15;11(20):7234-42 – reference: 12493072 - Crit Care. 2002 Dec;6(6):509-13 – reference: 21044041 - Stat Appl Genet Mol Biol. 2010;9:Article37 – reference: 16820429 - Bioinformatics. 2006 Sep 1;22(17):2107-13 – reference: 14630654 - Bioinformatics. 2003 Nov 22;19(17):2254-62 – reference: 14990455 - Bioinformatics. 2004 Mar 1;20(4):578-80 – reference: 18171320 - Stat Appl Genet Mol Biol. 2007;6:Article36 – reference: 17850668 - BMC Genomics. 2007;8:319 – reference: 12795414 - Recent Prog Horm Res. 2003;58:55-73 – reference: 10802651 - Nat Genet. 2000 May;25(1):25-9 – reference: 18436742 - Science. 2008 May 16;320(5878):942-5 – reference: 17569639 - Philos Trans R Soc Lond B Biol Sci. 2007 Aug 29;362(1484):1469-87 – reference: 15496517 - Biol Reprod. 2005 Feb;72(2):492-501 – reference: 15693945 - Genome Biol. 2005;6(2):R16 – reference: 12424116 - Bioinformatics. 2002 Nov;18(11):1454-61 – reference: 11842121 - Nucleic Acids Res. 2002 Feb 15;30(4):e15 – reference: 14769913 - Proc Natl Acad Sci U S A. 2004 Feb 17;101(7):2173-8 – reference: 16542223 - Biometrics. 2006 Mar;62(1):10-8 – reference: 18231591 - PLoS One. 2008;3(1):e1504 – reference: 12801864 - Bioinformatics. 2003 Jun 12;19(9):1046-54 – reference: 15256404 - Bioinformatics. 2004 Dec 12;20(18):3423-30 – reference: 17038174 - BMC Bioinformatics. 2006;7:448 – reference: 12808002 - J Pharmacol Exp Ther. 2003 Oct;307(1):93-109 – reference: 16368706 - Hum Mol Genet. 2006 Feb 1;15(3):405-15 |
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| SubjectTerms | Algorithms Analysis of Variance Bayes Theorem Biological and medical sciences Fundamental and applied biological sciences. Psychology Gene Expression Profiling - methods General aspects Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Statistical Oligonucleotide Array Sequence Analysis - methods Original Papers Reproducibility of Results |
| Title | Biological assessment of robust noise models in microarray data analysis |
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