Skip to main content

Advertisement

Gene Selection for Multiclass Prediction by Weighted Fisher Criterion

Article metrics

Abstract

Gene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. Gene selection, as an important step for improved diagnostics, screens tens of thousands of genes and identifies a small subset that discriminates between disease types. A two-step gene selection method is proposed to identify informative gene subsets for accurate classification of multiclass phenotypes. In the first step, individually discriminatory genes (IDGs) are identified by using one-dimensional weighted Fisher criterion (wFC). In the second step, jointly discriminatory genes (JDGs) are selected by sequential search methods, based on their joint class separability measured by multidimensional weighted Fisher criterion (wFC). The performance of the selected gene subsets for multiclass prediction is evaluated by artificial neural networks (ANNs) and/or support vector machines (SVMs). By applying the proposed IDG/JDG approach to two microarray studies, that is, small round blue cell tumors (SRBCTs) and muscular dystrophies (MDs), we successfully identified a much smaller yet efficient set of JDGs for diagnosing SRBCTs and MDs with high prediction accuracies (96.9% for SRBCTs and 92.3% for MDs, resp.). These experimental results demonstrated that the two-step gene selection method is able to identify a subset of highly discriminative genes for improved multiclass prediction.

[1234567891011121314151617181920212223242526272829303132333435363738394041424344454647]

References

  1. 1.

    Bittner M, Meltzer P, Chen Y, et al.: Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 2000, 406(6795):536-540. 10.1038/35020115

  2. 2.

    Golub TR, Slonim DK, Tamayo P, et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286(5439):531-537. 10.1126/science.286.5439.531

  3. 3.

    Shipp MA, Ross KN, Tamayo P, et al.: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine 2002, 8(1):68-74. 10.1038/nm0102-68

  4. 4.

    Liotta L, Petricoin E: Molecular profiling of human cancer. Nature Reviews Genetics 2000, 1(1):48-56. 10.1038/35049567

  5. 5.

    Jain AK, Duin RPW, Mao J: Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22(1):4-37. 10.1109/34.824819

  6. 6.

    Jain AK, Zongker D: Feature selection: evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(2):153-158. 10.1109/34.574797

  7. 7.

    Raudys SJ, Jain AK: Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Transactions on Pattern Analysis and Machine Intelligence 1991, 13(3):252-264. 10.1109/34.75512

  8. 8.

    Fukunaga K: Introduction to Statistical Pattern Recognition. 2nd edition. Academic Press, Boston, Mass, USA; 1990.

  9. 9.

    Devijver PA, Kittler J: Pattern Recognition: A Statistical Approach. Prentice-Hall, Englewood Cliffs, NJ, USA; 1982.

  10. 10.

    Pudil P, Novovicova J, Kittler J: Floating search methods in feature selection. Pattern Recognition Letters 1994, 15(11):1119-1125. 10.1016/0167-8655(94)90127-9

  11. 11.

    Dudoit S, Fridlyand J, Speed TP: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 2002, 97(457):77-87. 10.1198/016214502753479248

  12. 12.

    Khan J, Wei JS, Ringnér M, et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine 2001, 7(6):673-679. 10.1038/89044

  13. 13.

    Li T, Zhang C, Ogihara M: A comparative study of feature selection and multiclass classfication methods for tissue classification based on gene expression. Bioinformatics 2004, 20(15):2429-2437. 10.1093/bioinformatics/bth267

  14. 14.

    Tibshirani R, Hastie T, Narasimhan B, Chu G: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences of the United States of America 2002, 99(10):6567-6572. 10.1073/pnas.082099299

  15. 15.

    Xiong M, Fang X, Zhao J: Biomarker identification by feature wrappers. Genome Research 2001, 11(11):1878-1887.

  16. 16.

    Loog M: Approximate Pairwise Accuracy Criteria for Multiclass Linear Dimension Reduction: Generalisations of the Fisher Criterion. Delft University Press, Delft, The Netherlands; 1999.

  17. 17.

    Loog M, Duin RPW, Haeb-Umbach R: Multiclass linear dimension reduction by weighted pairwise Fisher criteria. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001, 23(7):762-766. 10.1109/34.935849

  18. 18.

    Koop JC: Generalized inverse of a singular matrix. Nature 1963, 200: 716.

  19. 19.

    Press WM, Flannery BP, Teukolsky SA, Vetterling WT: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, New York, NY, USA; 1986.

  20. 20.

    Narendra PM, Fukunaga K: A branch and bound algorithm for feature subset selection. IEEE Transactions on Computers 1977, 26(9):917-922.

  21. 21.

    Marill T, Green DM: On the effectiveness of receptors in cognition system. IEEE Transactions on Information Theory 1963, 9: 11-17. 10.1109/TIT.1963.1057810

  22. 22.

    Whitney AW: A direct method of nonparametric measurement selection. IEEE Transactions on Computers 1971, 20(9):1100-1103.

  23. 23.

    Stearns SD: On selecting features for pattern classifiers. Proceedings of the 3rd International Conference on Pattern Recognition, Coronado, Calif, USA, November 1976 71-75.

  24. 24.

    Haykin S: Neural Networks: A Comprehensive Foundation. 2nd edition. Prentice-Hall, Upper Saddle River, NJ, USA; 1999.

  25. 25.

    Lee Y, Lee C-K: Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 2003, 19(9):1132-1139. 10.1093/bioinformatics/btg102

  26. 26.

    Ramaswamy S, Tamayo P, Rifkin R, et al.: Multiclass cancer diagnosis using tumor gene expression signatures. Proceedings of the National Academy of Sciences of the United States of America 2001, 98(26):15149-15154. 10.1073/pnas.211566398

  27. 27.

    Bakay M, Chen Y-W, Borup R, Zhao P, Nagaraju K, Hoffman E: Sources of variability and effect of experimental approach on expression profiling data interpretation. BMC Bioinformatics 2002, 3(1):4. 10.1186/1471-2105-3-4

  28. 28.

    Bakay M, Wang Z, Melcon G, et al.: Nuclear envelope dystrophies show a transcriptional fingerprint suggesting disruption of Rb-MyoD pathways in muscle regeneration. Brain 2006, 129(4):996-1013. 10.1093/brain/awl023

  29. 29.

    Affymetrix Technical Note: Statistical algorithms description document. Affymetrix 2002. [http://www.affymetrix.com/support/technical/whitepapers/sadd_whitepaper.pdf]

  30. 30.

    Zhao P, Seo J, Wang Z, Wang Y, Shneiderman B, Hoffman E: In vivo filtering of in vitro expression data reveals MyoD targets. Comptes Rendus - Biologies 2003, 326(10-11):1049-1065. 10.1016/j.crvi.2003.09.035

  31. 31.

    Zhao P, Hoffman E: Embryonic myogenesis pathways in muscle regeneration. Developmental Dynamics 2004, 229(2):380-392. 10.1002/dvdy.10457

  32. 32.

    Winokur S, Chen Y-W, Masny PS, et al.: Expression profiling of FSHD muscle supports a defect in specific stages of myogenic differentiation. Human Molecular Genetics 2003, 12(22):2895-2907. 10.1093/hmg/ddg327

  33. 33.

    Bakay M, Zhao P, Chen J, Hoffman E: A web-accessible complete transcriptome of normal human and DMD muscle. Neuromuscular Disorders 2002, 12(1):S125-S141.

  34. 34.

    Chen Y-W, Zhao P, Borup R, Hoffman E: Expression profiling in the muscular dystrophies: identification of novel aspects of molecular pathophysiology. Journal of Cell Biology 2000, 151(6):1321-1336. 10.1083/jcb.151.6.1321

  35. 35.

    Hoffman E, Brown RH Jr., Kunkel LM: Dystrophin: the protein product of the Duchenne muscular dystrophy locus. Cell 1987, 51(6):919-928. 10.1016/0092-8674(87)90579-4

  36. 36.

    Koening M, Hoffman E, Bertelson CJ, Monaco AP, Feener C, Kunkel LM: Complete cloning of the Duchenne muscular dystrophy (DMD) cDNA and preliminary genomic organization of the DMD gene in normal and affected individuals. Cell 1987, 50(3):509-517. 10.1016/0092-8674(87)90504-6

  37. 37.

    Zhao P, Iezzi S, Carver E, et al.: Slug is a novel downstream target of MyoD. Temporal profiling in muscle regeneration. Journal of Biological Chemistry 2002, 277(33):30091-30101. 10.1074/jbc.M202668200

  38. 38.

    Fernandes RJ, Skiena SS: Microarray synthesis through multiple-use PCR primer design. Bioinformatics 2002, 18(1):S128-S135. 10.1093/bioinformatics/18.suppl_1.S128

  39. 39.

    Jaeger J, Weichenhan D, Ivandic B, Spang R: Early diagnostic marker panel determination for microarray based clinical studies. Statistical Applications in Genetics and Molecular Biology 2005., 4(1, article 9):

  40. 40.

    Li W: How many genes are needed for early detection of breast cancer, based on gene expression patterns in peripheral blood cells? Breast Cancer Research 2005, 7(5):E5. 10.1186/bcr1295

  41. 41.

    Glas AM, Floore A, Delahaye LJMJ, et al.: Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics 2006, 7: 278. 10.1186/1471-2164-7-278

  42. 42.

    Duin RPW: Classifiers in almost empty spaces. Proceedings of the 15th International Conference on Pattern Recognition (ICPR '00), Barcelona, Spain, September 2000 2: 1-7.

  43. 43.

    Raudys SJ: Evolution and generalization of a single neurone—I: single-layer perceptron as seven statistical classifiers. Neural Networks 1998, 11(2):283-296. 10.1016/S0893-6080(97)00135-4

  44. 44.

    Raudys SJ: Evolution and generalization of a single neurone—II: complexity of statistical classifiers and sample size considerations. Neural Networks 1998, 11(2):297-313. 10.1016/S0893-6080(97)00136-6

  45. 45.

    Raudys SJ, Duin RPW: Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix. Pattern Recognition Letters 1998, 19(5-6):385-392. 10.1016/S0167-8655(98)00016-6

  46. 46.

    Vapnik VN: Statistical Learning Theory. John Wiley & Sons, New York, NY, USA; 1998.

  47. 47.

    Oja E: Subspace Methods of Pattern Recognition. John Wiley & Sons, New York, NY, USA; 1984.

Download references

Author information

Correspondence to Jianhua Xuan.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Xuan, J., Wang, Y., Dong, Y. et al. Gene Selection for Multiclass Prediction by Weighted Fisher Criterion. J Bioinform Sys Biology 2007, 64628 (2007) doi:10.1155/2007/64628

Download citation

Keywords

  • Support Vector Machine
  • Muscular Dystrophy
  • Gene Selection
  • Molecular Diagnostics
  • Gene Subset