Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Statistical Methods for Gene Expression Data

  • Kim, Choongrak (Department of Statistics, Pusan National University)
  • Published : 2004.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

Since the introduction of DNA microarray, a revolutionary high through-put biological technology, a lot of papers have been published to deal with the analyses of the gene expression data from the microarray. In this paper we review most papers relevant to the cDNA microarray data, classify them in statistical methods' point of view, and present some statistical methods deserving consideration and future study.

Keywords

References

  1. Nature v.403 Different types of diffuse large b-cell lymphoma identified by gene expression profiling Alizadeh,A.A.;Eisen,M.B.;Davis,R.E.;Ma,C.;Lossos,I.S.;Rosenwald,A.;Boldrick,J.C.;Sabet,H.;Tran,T.;Yu,X.;Powell,J.L.;Yang,L.;Marti,G.E.;Moore,T.;Hudson,Jr.J.;Lu,L.;Lewis,D.B.;Tibshirani,R.;Sherlock,G.;Chan,W.C.;Greiner,T.C.;Weisenburger,D.D.;Armitage,J.O.;Warnke,R.;Levy,R.;Levy,R.;Wilson,W.;Grever,M.R.;Byrd,J.C.;Brown,P.O.;Bostein,D.;Staudt,L.M. https://doi.org/10.1038/35000501
  2. Proceedings of the National Academy of Science v.97 Singular value decomposition for genome-wide expression data prcessing and modeling Alter,O.;Brown,P.O.;Bostein,D. https://doi.org/10.1073/pnas.97.18.10101
  3. Nature Genetics v.21 Gene expression informatics - it's all in your mine Basset,D.E.;Eisen,M.B.;Boguski,M.S. https://doi.org/10.1038/4478
  4. Journal of Computational Biology v.7 Tissue classification with gene expression profiles Ben-Dor,A.;Bruhn,L.K.;Friedman,N.;Nachman,L.;Schummer,M.;Yakini,Z. https://doi.org/10.1089/106652700750050943
  5. Bioinformatics v.20 Adjustment of systematic microarray data biases Beito,M.;Parker,J.;u,Q.;쪄,J.;Xiang,D.;Perou,C.M.;Marron,J.S. https://doi.org/10.1093/bioinformatics/btg385
  6. Journal of the Royal Statistical Society.Ser.B. v.57 Controlling the false discovery rate: A practical and powerful approach to multiple testing Benjamini,Y.;Hochberg,Y.
  7. Tha Annals of Statistics v.29 The control of the false discovery rate in multiple testing under dependency Benjamini,Y.;Yekutieli,D. https://doi.org/10.1214/aos/1013699998
  8. Bioinformatics v.18 Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering Bozinov,D.;Rahnenfuhrer https://doi.org/10.1093/bioinformatics/18.5.747
  9. Federation of European Biochemical Societies Letters v.480 Gene expression data analysis Brazma,A.;Vilo,J. https://doi.org/10.1016/S0014-5793(00)01772-5
  10. Machine Learning v.24 Bagging predictors Breiman,L.
  11. Classification and Regression Trees Breiman,L.;Friedman,J.H.;Olshen,R.;Stone,C.J.
  12. Proceedings of the National Academy of Science v.97 Knowledge-based analysis of microarray gene expression data by using support vector machines Brown,M.P.S;Grundy,W.N.;Lin,D.;Cristianini,N.;Sugnet,C.W.;Furey,T.S.;Ares,Jr.M.;Haussler,D. https://doi.org/10.1073/pnas.97.1.262
  13. Journal of Biomedical Optics v.2 Ratio-based decisions and the quantitative analysis of cDNA microarray images Chen,Y.;Dougherty,E.R.;Bittner,M.L. https://doi.org/10.1117/12.281504
  14. Bioinformatics v.18 no.9 Ratio statistics of gene expression levels and applicatins to microarray data analysis Chen,Y.;Kamat,V.;Dougherty,E.R.;Bittner,M.L.;Meltzer,P.S.;Trent,J.M. https://doi.org/10.1093/bioinformatics/18.9.1207
  15. Science v.282 The transcriptional program of sporulation in budding yeast Chu,S.;DeRisi,J.;Eisen,M.;Mulholland,J.;Boststein,D.;Brown,P.O.;Herskowitz,I. https://doi.org/10.1126/science.282.5389.699
  16. Journal of Computational Biology v.9 Identifying and quantifying sources of variation in microarray data using high-density cDNA membrance arrays Coombes,K.R.;Highmith,W.E.;Krogmann,T.A.;Baggegly,K.A.;Stivers,D.N.;Abruzzo,L.V. https://doi.org/10.1089/106652702760277372
  17. Bioinformatics v.19 Boosting for tumor classification with gene expression data Dettling,M.;Buhlmann,P. https://doi.org/10.1093/bioinformatics/btf867
  18. Bioinformatics v.19 Unsupervised feature selection via two-way ordering in gene expression analysis Ding,C.H.Q. https://doi.org/10.1093/bioinformatics/btg149
  19. Journal of Computation Biology v.9 Inference from clustering with application to gene-expression microarrays Dougherty,E.R.;Barrera,J.;Brun,M.;Kim,S.;Cesar,R.M.;Chen,Y.;Bittner,M.;Trent,J.M. https://doi.org/10.1089/10665270252833217
  20. Nature Genetics Supplement v.21 Expression profiling using cDNA microarrays Duggan,D.J.;Bittner,M.;Chen,Y.;Meltzer,P.;Trent,J.M. https://doi.org/10.1038/4434
  21. Journal of the American Statistical Association v.97 Comparison of methods for the classification of tumors using gene expression data Dodoit,S.;Fridlyand,J.;Speed,T. https://doi.org/10.1198/016214502753479248
  22. Bioinformatics v.19 Bagging to improve the accuracy of a clustering procedure Dudoit,S.;Fridlyand,J. https://doi.org/10.1093/bioinformatics/btg038
  23. Bioinformatics v.19 Non-linear normalization and background correction in one-channel cDNA microarray studies Edwards,D. https://doi.org/10.1093/bioinformatics/btg083
  24. Journal of the Americans Statistical Association v.96 Empirical Bayes analysis of a microarray experiment Efron,B.;Tibsirani,R.;Storey,J.D.;Tusher,V. https://doi.org/10.1198/016214501753382129
  25. Proceedings of the National Academy of Science v.95 Cluster analysis and display of genome-wide expression patterns Eisen,M.;Spellman,P.T.;Brown,P.O.;Botstein,D. https://doi.org/10.1073/pnas.95.25.14863
  26. Annals of Eugenics v.7 The use of multiple measurements in taxonomic problems Fisher,R.A. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
  27. Technical Report, School of Aviation Medicine Discriminatory analysis, nonparametric discrimination: consistency properties Fix,E.;Hodges,J.
  28. Journal of Computer and System Sciences v.55 A decision -theoretic generalization of on-line learning and an application to boosting Freund,Y.;Schapire,R.E. https://doi.org/10.1006/jcss.1997.1504
  29. Journal of the Royal Statistical Society,Ser.B. v.64 Operating characteristics and extensions of the false discovery rate problem Genovese,C.;Wasserman,L. https://doi.org/10.1111/1467-9868.00347
  30. Science v.286 Molecular classification of cancer:class discovery and class prediction by gene expression monitoring Golub,T.R.;Slonim,D.K.;Tamayo,P.;Huard,C.;Gaasenbeek,M.;Mesirov,J.P.;Coller,H.;Loh,M.L.;Downing,J.R.;Caligiuri,M.A.;Bloomfield,C.D.;Lander,E.S. https://doi.org/10.1126/science.286.5439.531
  31. Journal of Computational Biology v.8 Unfolding of microarray data Goryachev,A.B.;Macgregor,P.F.;Edwards,A.M. https://doi.org/10.1089/106652701752236232
  32. Genome Biology v.2 Supervised harvesting of expression trees Hastie,T.;Tibshirani,R.;Botstein,D.;Brown,P.O. https://doi.org/10.1186/1471-2105-2-1
  33. Proceedings of the National Academy of Science v.97 Fundamental patterns underlying gene expression profiles: Simplicity from complexity Holster,N.S.;Mitra,M.;Maritan,A.;Cieplak,M.;Banavar,J.R. https://doi.org/10.1073/pnas.150242097
  34. Proceedings of the National Academy of Science v.98 Dynamic modeling of gene expression data Holster,N.S.;Martian,A.;Cieplak,M.;Fedroff,N.V.;Banavar,J.R. https://doi.org/10.1073/pnas.98.4.1693
  35. Journal of the American Statistical Association v.97 Bayesian models for gene expression with DNA microarray data Ibrahim,J.G.;Chen,M-H.;Gray,R.J. https://doi.org/10.1198/016214502753479257
  36. Journal of Computational Biology v.7 Analysis of variance for microarray data Kerr,M.K.;Martin,M.;Churchill,G.A. https://doi.org/10.1089/10665270050514954
  37. Proceedings of the National Academy of Science v.98 Bootstrapping cluster analysis: Assessing the reliability of conclusions microarray experiments Kerr,M.K.;Churchill,G.A. https://doi.org/10.1073/pnas.161273698
  38. Self-Organizing Maps Kohonen,T.
  39. Journal of Computational Biology v.9 no.1 Improved background correction for spotted DNA microarrays Kooperberg,C.;Fazzio,T.G.;Delrow,J.J.;Tsukiyama,T. https://doi.org/10.1089/10665270252833190
  40. Statistica Sinica v.12 Plaid models for gene expression data Lazzeroni,L.;Owen,A.
  41. Proceedings of the National Academy of Science v.97 Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations Lee,M.T.;Kuo,F.C.;Whitemore,G.A.;Sklar,J. https://doi.org/10.1073/pnas.97.18.9834
  42. Bioinformatics v.19 Classification of multiple cancer types by multicategory support vector machines using gene expression data Lee,Y.;Lee,C-K. https://doi.org/10.1093/bioinformatics/btg102
  43. Nature Genetics no.SUP.21 High-density synthetic oilgonucleotide arrays Lipshutz,R.J.;Fordor,S.;Gingeras,T.;Lockhart,D.
  44. Nature Biotechnology v.14 Expression monitoring by hybridization to high-density oligonucleotide arrays Lockhart,D.J.;Dong,H.;Byrne,M.C.;Follettie,M.T.;Gallo,M.V.;Chee,M.S.;Mittmann,M.;Wang,C.;Kobayashi,M.;Horton,H.;Horton,H. https://doi.org/10.1038/nbt1296-1675
  45. Statistica Sinica v.12 Replicated microarray data Lonnstadt,I.;Speed,T.
  46. Jourma of Computational Biology v.8 On differential variability of expression ratios : Improving statistical inference about gene expression changes from microarray data Newton,M.A.;Kendziorski,C.M.;Richmond,C.S.;Blattner,F.R.;Tsui,K.W.
  47. Biometrics v.58 DNA microarray experiments: Biological and technological aspects Nguyen,D.V.;Arpay,A.B.;Wang,N.;Carroll,R.J. https://doi.org/10.1111/j.0006-341X.2002.00701.x
  48. Bioinformatics v.18 Deriving quantitative conclusions from microarray expression data Olshen,A.B.;Jain,A.N. https://doi.org/10.1093/bioinformatics/18.7.961
  49. Proceedings of the National Academy of Science v.16 Distinctive gene expression patterns in human mammary epithelial cells and breast cancers Perou,C.M.;Jeffrey,S.S.;van de Rijn,M.;Rees,C.A.;Eisen,M.B.;Ross,D.T.;Pergamenschikov,A.;Williams,C.F.;Zhu,S.X.;Lee,J.C.;Lashkari,D.;Shalon,D.;Brown,P.O.;Botstein,D.
  50. Journal of Computational Biology v.9 A paradigm for class prediction using gene expression profiles Radmacher,M.D.;McShane,L.M.;Simon,R. https://doi.org/10.1089/106652702760138592
  51. Bioinformatics v.19 Identifying differentially expressed genes using false discovery rate controlling procedures reiner,A.;Yekutieli,D.;Benjamini,Y. https://doi.org/10.1093/bioinformatics/btf877
  52. Pattern Recognition and Neural Networks Ripley,B.D.
  53. Nature Genetics v.24 Systematic variation in gene expression patterns in human cancer cell lines Ross,D.T.;Scherf,U.;Eisen,M.B.;Perou,C.M.;Rees,C.;Spellman,P.;Iyer,V.;Jeffrey,S.S.;Van de Rijn,M.;Waltham,M.;Pergamenschikov,A.;Lee,J.C.;Lashkari,D.;Shalon,D.;Myers,T.G.;Weinstein,J.N.;Botstein,D.;Brown,P.O. https://doi.org/10.1038/73432
  54. Journal of Cellular Biochemistry v.80 Analyzing high-density oligonucleotide gene expression array data Schadt,E.E.;Li,C.;Su,C.;Wong,W.H. https://doi.org/10.1002/1097-4644(20010201)80:2<192::AID-JCB50>3.0.CO;2-W
  55. Journal of Cellular Biochemistry v.84 Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data Schadt,E.E.;Li,C.;Ellis,B.;Wong,W.H. https://doi.org/10.1002/jcb.10073
  56. Science v.270 Quantitative monitoring of gene expression patterns with a complementary DNA microarray Schena,M.;Shalon,D.;Davis,R.W.;Brown,P.O. https://doi.org/10.1126/science.270.5235.467
  57. Nature Genetics v.24 Systematic variation in gene expression patterns in human cancer cell lines Scherf,U.;Ross,D.T.;Waltham,M.;Smith,L.H.;Lee,J.K.;Kohn,K.W.;Reinhold,W.C.;Meyers,T.G.;Andrews,D.T.;Scudiero,D.A.;Eisen,M.B.;Sausville,E.A.;Pommier,Y.;Bostein,D.;Brown,P.O.;Weinstein,J.N. https://doi.org/10.1038/73439
  58. Statistical Science v.18 Statistical challenges in functional genomics(with discussion) Sebastiani,P.;Gussoni,E.;Kohane,L.S.;Ramoni,M.F. https://doi.org/10.1214/ss/1056397486
  59. Annals Review of Psychology v.46 Multiple hypothesis testing Shaffer,J.P. https://doi.org/10.1146/annurev.ps.46.020195.003021
  60. Methods and Protocols,To appear Statistical issues in cDNA microarray data analysis,Functional Genomics Smyth,G.K.;Yang,Y.H.;Speed,T.
  61. Molecular Biology of the Cell v.9 Comprehensive identification of cell cycle-regulated genes of the yeast saccaromyces cerevisiae by microarray hybridization Spellman,P.T.;Sherlock,G.;Zhang,M.Q.;Iyer,V.R.;Andres,K.;Eisen,M.B.;Brown,P.O.;Bostein,D.;Futcher,B. https://doi.org/10.1091/mbc.9.12.3273
  62. Journal of the Royal Statistical Society Ser.B. v.64 A Direct approach to false discovery rates Storey,J.D. https://doi.org/10.1111/1467-9868.00346
  63. Proceedings of the National Academy of Science v.96 Interpreting patterns of gene expression with self-organizing mpas: Methods and applications to hematopoietic differentiation Tamayo,P.;Slonim,T.;Mesirov,J.;Zhu,Q.;Kitareewan,S.;Dmitrovsky,E.;Lander,E.S.;Golub,T.R. https://doi.org/10.1073/pnas.96.6.2907
  64. Technical Report,Department of Health Research and Policy Clustering methods for the analysis of dna microarray data Tibshirani,R.;Hastie,T.;Eisen,M.;Ross,D.T.;Botstein,D.;Brown,P.O.
  65. Proceedings of the National Academy of Science v.99 Diagnosis of multiple cancer types by shrunken centroids of gene expression Tibshirani,R.;Hastie,T.;Narasimhan,B.;Chu,G. https://doi.org/10.1073/pnas.082099299
  66. Nucleic Acids Research v.29 Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects Tseng,G.C.;Oh,M-K;Rohlin,L.;Liao,J.C.;Wong,W.H. https://doi.org/10.1093/nar/29.12.2549
  67. Proceedings of the National Academy of Science v.98 Significance analysis of microarrays applied to the ionizing radiation response Tusher,V.G.;Tibshirani,R.;Chu,G. https://doi.org/10.1073/pnas.091062498
  68. Statistical Learning Theory Vapnik,V.N.
  69. Bioinformatics v.20 A generalized likelihood ratio test to identify differentially expressed genes from microarray data Wang,S.;Ethier,S. https://doi.org/10.1093/bioinformatics/btg384
  70. Resampling-based Multiple Testing: Examples and Methods for P-value Adjustment Westfall,P.H.;Young,S.S.
  71. Bioinformatics v.19 New normalization methods for cDNA microarray data Wilson,D.L.;Buckley,M.J.;Helliwell,C.A.;Wilson,I.W. https://doi.org/10.1093/bioinformatics/btg146
  72. Journal of Computational Biology v.8 Assessing gene significance from cDNA microarray expression data via mixed models Wolfinger,R.D.;Gibson,G.;Wolfinger,E.D.;Bennett,L.;Madadeh,H.;Bushel,P.;Afshari,C.;Paules,R.S. https://doi.org/10.1089/106652701753307520
  73. Journal of Computational and Graphical Statistics v.11 Comparison of methods for image analysis on cDNA microarray data Yang,Y.H.;Buckley,M.J.;Dudoit,S.;Speed,T.P. https://doi.org/10.1198/106186002317375640
  74. Proceedings of SPIE Normalization for cDNA microarray data in Microarrays: Optical Technologies and Informatics Yang,Y.H.;Dudoits,S.;Luu,P.;Speed,T.P.