Pattern recognition methods in microarray based oncology study

Abstract

With the development of microarray technology, more and more microarray-based oncology studies have been carried out. Huge amounts of data and the complexity of cancer mechanisms make data analysis methods a much more important part of these studies. In this article, we will mainly focus on the pattern recognition methods used in oncology studies. According to the availability of sample information, the unsupervised methods and supervised methods are reviewed separately. Finally, some possible future directions are proposed.

Keywords

pattern recognition methods / microarray / oncology

References

[1] 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. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 1999, 286(5439): 531–537

[2] Alon U, Barkai N, Notterman D A, Gish K, Ybarra S, Mack D, Levine A J. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences of the United States of America, 1999, 96(12): 6745–6750

[3] van’t Veer L J, Dai H, van de Vijver M J, He Y D, Hart A A, Mao M, Peterse H L, van der Kooy K, Marton M J, Witteveen A T, Schreiber G J, Kerkhoven R M, Roberts C, Linsley P S, Bernards R, Friend S H. Gene expression profiling predicts clinical outcome of breast cancer. Nature, 2002, 415(6871): 530–536

[4] 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 I, Yang L, Marti G E, Moore T, Hudson J Jr, 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, Wilson W, Grever M R, Byrd J C, Botstein D, Brown P O, Staudt L M. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 2000, 403(6769): 503–511

[5] Beer D G, Kardia S L, Huang C C, Giordano T J, Levin A M, Misek D E, Lin L, Chen G, Gharib T G, Thomas D G, Lizyness M L, Kuick R, Hayasaka S, Taylor J M, Iannettoni M D, Orringer M B, Hanash S. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nature Medicine, 2002, 8(8): 816–824

[6] Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A, Sampas N, Dougherty E, Wang E, Marincola F, Gooden C, Lueders J, Glatfelter A, Pollock P, Carpten J, Gillanders E, Leja D, Dietrich K, Beaudry C, Berens M, Alberts D, Sondak V. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature, 2000, 406(6795): 536–540

[7] Dyrskjøt L, Thykjaer T, Kruhøffer M, Jensen J L, Marcussen N, Hamilton-Dutoit S, Wolf H, Orntoft T F. Identifying distinct classes of bladder carcinoma using microarrays. Nature Genetics, 2003, 33(1): 90–96

[8] Kapp A V, Jeffrey S S, Langerød A, B-rresen-Dale A L, Han W, Noh D Y, Bukholm I R, Nicolau M, Brown P O, Tibshirani R. Discovery and validation of breast cancer subtypes. BMC Genomics, 2006, 7: 231

[9] 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. Systematic variation in gene expression patterns in human cancer cell lines. Nature Genetics, 2000, 24(3): 227–235

[10] Huang Y, Prasad M, Lemon W J, Hampel H, Wright F A, Kornacker K, LiVolsi V, Frankel W, Kloos R T, Eng C, Pellegata N S, de la Chapelle A. Gene expression in papillary thyroid carcinoma reveals highly consistent profiles. Proceedings of the National Academy of Sciences of the United States of America, 2001, 98(26): 15044–15049

[11] Hastie T, Tibshirani R, Botstein D, Brown P. Supervised harvesting of expression trees. Genome Biology, 2001, 2(1): research0003.1–research0003.12

[12] Huang E, Cheng S H, Dressman H, Pittman J, Tsou M H, Horng C F, Bild A, Iversen E S, Liao M, Chen C M, West M, Nevins J R, Huang A T. Gene expression predictors of breast cancer outcomes. Lancet, 2003, 361(9369): 1590–1596

[13] Nilsson J, Fioretos T, Höglund M, Fontes M. Approximate geodesic distances reveal biologically relevant structures in microarray data. Bioinformatics, 2004, 20(6): 874–880

[14] Boratyn G M, Datta S, Datta S. Incorporation of biological knowledge into distance for clustering genes. Bioinformation, 2007, 1(10): 396–405

[15] Bagirov A M, Ferguson B, Ivkovic S, Saunders G, Yearwood J. New algorithms for multi-class cancer diagnosis using tumor gene expression signatures. Bioinformatics, 2003, 19(14): 1800–1807

[16] Gao Y, Church G. Improving molecular cancer class discovery through sparse non-negative matrix factorization. Bioinformatics, 2005, 21(21): 3970–3975

[17] Sese J, Kurokawa Y, Monden M, Kato K, Morishita S. Constrained clusters of gene expression profiles with pathological features. Bioinformatics, 2004, 20(17): 3137–3145

[18] Dotan-Cohen D, Melkman A A, Kasif S. Hierarchical tree snipping: clustering guided by prior knowledge. Bioinformatics, 2007, 23(24): 3335–3342

[19] Belacel N, Cuperlović-Culf M, Laflamme M, Ouellette R. Fuzzy J-means and VNS methods for clustering genes from microarray data. Bioinformatics, 2004, 20(11): 1690–1701

[20] Getz G, Levine E, Domany E. Coupled two-way clustering analysis of gene microarray data. Proceedings of the National Academy of Sciences of the United States of America, 2000, 97(22): 12079–12084

[21] Getz G, Gal H, Kela I, Notterman D A, Domany E. Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data. Bioinformatics, 2003, 19(9): 1079–1089

[22] Kluger Y, Basri R, Chang J T, Gerstein M. Spectral biclustering of microarray data: coclustering genes and conditions. Genome Research, 2003, 13(4): 703–716

[23] Wang J, Delabie J, Aasheim H, Smeland E, Myklebost O. Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study. BMC Bioinformatics, 2002, 3: 36

[24] Hanczar B, Courtine M, Benis A, Hennegar C, Clément K, Zucker J D. Improving classification of microarray data using prototype-based feature selection. ACM SIGKDD Explorations Newsletter, 2003, 5(2): 23–30

[25] Crescenzi M, Giuliani A. The main biological determinants of tumor line taxonomy elucidated by a principal component analysis of microarray data. FEBS Letters, 2001, 507(1): 114–118

[26] Hsu A L, Tang S L, Halgamuge S K. An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data. Bioinformatics, 2003, 19(16): 2131–2140

[27] Li W, Fan M, Xiong M. SamCluster: an integrated scheme for automatic discovery of sample classes using gene expression profile. Bioinformatics, 2003, 19(7): 811–817

[28] Dudoit S, Fridlyand J, Speed T. Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Technical Report 576. Berkeley, CA: Department of Statistics, University of California, 2000

[29] Smolkin M, Ghosh D. Cluster stability scores for microarray data in cancer studies. BMC Bioinformatics, 2003, 4: 36

[30] Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning, 2003, 52(1–2): 91–118

[31] Bhattacharjee A, Richards W G, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M, Loda M, Weber G, Mark E J, Lander E S, Wong W, Johnson B E, Golub T R, Sugarbaker D J, Meyerson M. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proceedings of the National Academy of Sciences of the United States of America, 2001, 98(24): 13790–13795

[32] Dudoit S, Fridlyand J. Bagging to improve the accuracy of a clustering procedure. Bioinformatics, 2003, 19(9): 1090–1099

[33] Swift S, Tucker A, Vinciotti V, Martin N, Orengo C, Liu X, Kellam P. Consensus clustering and functional interpretation of gene-expression data. Genome Biology, 2004, 5(11): R94

[34] Martoglio A M, Miskin J W, Smith S K, MacKay D J. A decomposition model to track gene expression signatures: preview on observer-independent classification of ovarian cancer. Bioinformatics, 2002, 18(12): 1617–1624

[35] West M, Blanchette C, Dressman H, Huang E, Ishida S, Spang R, Zuzan H, Olson J A Jr, Marks J R, Nevins J R. Predicting the clinical status of human breast cancer by using gene expression profiles. Proceedings of the National Academy of Sciences of the United States of America, 2001, 98(20): 11462–11467

[36] Liebermeister W. Linear modes of gene expression determined by independent component analysis. Bioinformatics, 2002, 18(1): 51–60

[37] Pomeroy S L, Tamayo P, Gaasenbeek M, Sturla L M, Angelo M, McLaughlin M E, Kim J Y, Goumnerova L C, Black P M, Lau C, Allen J C, Zagzag D, Olson J M, Curran T, Wetmore C, Biegel J A, Poggio T, Mukherjee S, Rifkin R, Califano A, Stolovitzky G, Louis D N, Mesirov J P, Lander E S, Golub T R. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature, 2002, 415(6870): 436–442

[38] Gordon G J, Richards W G, Sugarbaker D J, Jaklitsch M T, Bueno R. A prognostic test for adenocarcinoma of the lung from gene expression profiling data. Cancer Epidemiology, Biomarkers & Prevention, 2003, 12(9): 905–910

[39] Gordon G J, Jensen R V, Hsiao L L, Gullans S R, Blumenstock J E, Ramaswamy S, Richards W G, Sugarbaker D J, Bueno R. Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Research, 2002, 62(17): 4963–4967

[40] Dabney A R. Classification of microarrays to nearest centroids. Bioinformatics, 2005, 21(22): 4148–4154

[41] Thomas J G, Olson J M, Tapscott S J, Zhao L P. An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Research, 2001, 11(7): 1227–1236

[42] Troyanskaya O G, Garber M E, Brown P O, Botstein D, Altman R B. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics, 2002, 18(11): 1454–1461

[43] Dettling M, Bühlmann P. Boosting for tumor classification with gene expression data. Bioinformatics, 2003, 19(9): 1061–1069

[44] Broët P, Lewin A, Richardson S, Dalmasso C, Magdelenat H. A mixture model-based strategy for selecting sets of genes in multiclass response microarray experiments. Bioinformatics, 2004, 20(16): 2562–2571

[45] Nutt C L, Mani D R, Betensky R A, Tamayo P, Cairncross J G, Ladd C, Pohl U, Hartmann C, McLaughlin M E, Batchelor T T, Black P M, von Deimling A, Pomeroy S L, Golub T R, Louis D N. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Research, 2003, 63(7): 1602–1607

[46] Gormley M, Dampier W, Ertel A, Karacali B, Tozeren A. Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets. BMC Bioinformatics, 2007, 8: 415

[47] Furey T S, Cristianini N, Duffy N, Bednarski D W, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 2000, 16(10): 906–914

[48] Li J, Wong L. Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns. Bioinformatics, 2002, 18(5): 725–734

[49] Bø T H, Jonassen I. New feature subset selection procedures for classification of expression profiles. Genome Biology, 2002, 3(4): research0017.1–research0017.11

[50] Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Machine Learning, 2002, 46(1–3): 389–422

[51] Zhang X, Lu X, Shi Q, Xu X Q, Leung H C, Harris L N, Iglehart J D, Miron A, Liu J S, Wong W H. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics, 2006, 7: 197

[52] Furlanello C, Serafini M, Merler S, Jurman G. Entropy-based gene ranking without selection bias for the predictive classification of microarray data. BMC Bioinformatics, 2003, 4: 54

[53] Li W, Xiong M. Tclass: tumor classification system based on gene expression profile. Bioinformatics, 2002, 18(2): 325–326

[54] Inza I, Sierra B, Blanco R, Larrañaga P. Gene selection by sequential search wrapper approaches in microarray cancer class prediction. Journal of Intelligent and Fuzzy Systems, 2002, 12(1): 25–33

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

[56] Liu J J, Cutler G, Li W, Pan Z, Peng S, Hoey T, Chen L, Ling X B. Multiclass cancer classification and biomarker discovery using GA-based algorithms. Bioinformatics, 2005, 21(11): 2691–2697

[57] Peng S, Xu Q, Ling X B, Peng X, Du W, Chen L. Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. FEBS Letters, 2003, 555(2): 358–362

[58] Li L, Weinberg C R, Darden T A, Pedersen L G. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics, 2001, 17(12): 1131–1142

[59] Ooi C H, Tan P. Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics, 2003, 19(1): 37–44

[60] Deutsch J M. Evolutionary algorithms for finding optimal gene sets in microarray prediction. Bioinformatics, 2003, 19(1): 45–52

[61] Jirapech-Umpai T, Aitken S. Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes. BMC Bioinformatics, 2005. 6: 148

[62] Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics, 2007, 23(19): 2507–2517

[63] Krishnapuram B, Carin L, Hartemink A J. Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data. Journal of Computational Biology, 2004, 11(2-3): 227–242

[64] Cawley G C, Talbot N L C. Gene selection in cancer classification using sparse logistic regression with Bayesian regularization. Bioinformatics, 2006, 22(19): 2348–2355

[65] Nguyen D V, Rocke D M. Tumor classification by partial least squares using microarray gene expression data. Bioinformatics, 2002, 18(1): 39–50

[66] Nguyen D V, Rocke D M. Partial least squares proportional hazard regression for application to DNA microarray survival data. Bioinformatics, 2002, 18(12): 1625–1632

[67] Chang H Y, Nuyten D S, Sneddon J B, Hastie T, Tibshirani R, Sørlie T, Dai H, He Y D, van’t Veer L J, Bartelink H, van de Rijn M, Brown P O, van de Vijver M J. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(10): 3738–3743

[68] Khan J, Wei J S, Ringnér M, Saal L H, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu C R, Peterson C, Meltzer P S. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, 2001, 7(6): 673–679

[69] O’Neill M, Song L. Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect. BMC Bioinformatics, 2003, 4: 13

[70] Liu B, Cui Q, Jiang T, Ma S. A combinational feature selection and ensemble neural network method for classification of gene expression data. BMC Bioinformatics, 2004, 5: 136

[71] Linder R, Dew D, Sudhoff H, Theegarten D, Remberger K, Pöppl S J, Wagner M. The ‘subsequent artificial neural network’ (SANN) approach might bring more classificatory power to ANN-based DNA microarray analyses. Bioinformatics, 2004, 20(18): 3544–3552

[72] Zhang W, Rekaya R, Bertrand K. A method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer. Bioinformatics, 2006, 22(3): 317–325

[73] Gevaert O, De Smet F, Timmerman D, Moreau Y, De Moor B. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics, 2006, 22(14): e184–e190

[74] Goeman J J, Oosting J, Cleton-Jansen A M, Anninga J K, van Houwelingen H C. Testing association of a pathway with survival using gene expression data. Bioinformatics, 2005, 21(9): 1950–1957

[75] Gui J, Li H. Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics, 2005, 21(13): 3001–3008

[76] Schumacher M, Binder H, Gerds T. Assessment of survival prediction models based on microarray data. Bioinformatics, 2007, 23(14): 1768–1774

[77] Kaderali L, Zander T, Faigle U, Wolf J, Schultze J L, Schrader R. CASPAR: a hierarchical bayesian approach to predict survival times in cancer from gene expression data. Bioinformatics, 2006, 22(12): 1495–1502

[78] Parmigiani G, Garrett-Mayer E S, Anbazhagan R, Gabrielson E. A cross-study comparison of gene expression studies for the molecular classification of lung cancer. Clinical Cancer Research, 2004, 10(9): 2922–2927

[79] Fernandez-Teijeiro A, Betensky R A, Sturla L M, Kim J Y, Tamayo P, Pomeroy S L. Combining gene expression profiles and clinical parameters for risk stratification in medulloblastomas. Journal of Clinical Oncology, 2004, 22(6): 994–998

[80] Barry W T, Nobel A B, Wright F A. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics, 2005, 21(9): 1943–1949

[81] Zhang C, Lu X, Zhang X. Significance of gene ranking for classification of microarray samples. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2006, 3(3): 312–320

[82] Dettling M. BagBoosting for tumor classification with gene expression data. Bioinformatics, 2004, 20(18): 3583–3593

[83] Lu X, Li Y, Zhang X. A simple strategy for detecting outlier samples in microarray data. In: Proceedings of the Eighth International Conference on Control, Automation, Robotics and Vision. Kunming: IEEE, 2004, 2: 1331–1335

[84] Gamberoni G, Storari S, Volinia S. Finding biological process modifications in cancer tissues by mining gene expression correlations. BMC Bioinformatics, 2006, 7: 6

[85] Subramanian A, Tamayo P, Mootha V K, Mukherjee S, Ebert B L, Gillette M A, Paulovich A, Pomeroy S L, Golub T R, Lander E S, Mesirov J P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(43): 15545–15550

[86] Al-Shahrour F, Diaz-Uriarte R, Dopazo J. Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information. Bioinformatics, 2005, 21(13): 2988–2993

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg