Bioinformatics Vol. 19 no. 13 2003
Pages 1636-1643
© 2003 Oxford University Press
Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data
1 Department of Epidemiology and Public Health, 2 HHMI Biopolymer/Keck Laboratory, 3 Department of Obstetrics and Gynecology, 4 Department of Genetics, Yale University School of Medicine, New Haven, CT, USA and 5 Department of OB/GYN, Northwestern University School of Medicine, Chicago, IL, USA
Received on December 18, 2002
; revised on March 3, 2003
; accepted on March 6, 2003
Motivation: Novel methods, both molecular and statistical, are urgently needed to take advantage of recent advances in biotechnology and the human genome project for disease diagnosis and prognosis. Mass spectrometry (MS) holds great promise for biomarker identification and genome-wide protein profiling. It has been demonstrated in the literature that biomarkers can be identified to distinguish normal individuals from cancer patients using MS data. Such progress is especially exciting for the detection of early-stage ovarian cancer patients. Although various statistical methods have been utilized to identify biomarkers from MS data, there has been no systematic comparison among these approaches in their relative ability to analyze MS data.
Results: We compare the performance of several classes of statistical methods for the classification of cancer based on MS spectra. These methods include: linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor classifier, bagging and boosting classification trees, support vector machine, and random forest (RF). The methods are applied to ovarian cancer and control serum samples from the National Ovarian Cancer Early Detection Program clinic at Northwestern University Hospital. We found that RF outperforms other methods in the analysis of MS data.
Supplementary information: http://bioinformatics.med.yale.edu/proteomics/BioSupp1.html
Contact: hongyu.zhao{at}yale.edu
* To whom correspondence should be addressed.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  J. H. Oh, Y. B. Kim, P. Gurnani, K. P. Rosenblatt, and J. X. Gao Biomarker selection and sample prediction for multi-category disease on MALDI-TOF data Bioinformatics, August 15, 2008; 24(16): 1812 - 1818. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Hilario and A. Kalousis Approaches to dimensionality reduction in proteomic biomarker studies Brief Bioinform, March 1, 2008; 9(2): 102 - 118. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Y. Guo, Z. Chen, L. Zhang, F. Zhou, S. Shi, X. Feng, B. Li, X. Meng, X. Ma, M. Luo, et al. Distinctive MicroRNA Profiles Relating to Patient Survival in Esophageal Squamous Cell Carcinoma Cancer Res., January 1, 2008; 68(1): 26 - 33. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Y. Lai A moment-based method for estimating the proportion of true null hypotheses and its application to microarray gene expression data Biostat., October 1, 2007; 8(4): 744 - 755. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Saeys, I. Inza, and P. Larranaga A review of feature selection techniques in bioinformatics Bioinformatics, October 1, 2007; 23(19): 2507 - 2517. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. McIlwain, D. Page, E. L. Huttlin, and M. R. Sussman Using dynamic programming to create isotopic distribution maps from mass spectra Bioinformatics, July 1, 2007; 23(13): i328 - i336. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Park, S. R. Downing, D. Kim, W. C. Hahn, C. Li, P. W. Kantoff, and L.J. Wei Simultaneous and exact interval estimates for the contrast of two groups based on an extremely high dimensional variable: application to mass spec data Bioinformatics, June 15, 2007; 23(12): 1451 - 1458. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. C. Walker, K. A. Salinas, P. S. Harris, S. S. Wilkinson, J. D. Watts, and M. J. Hemmer A Proteomic (SELDI-TOF-MS) Approach to Estrogen Agonist Screening Toxicol. Sci., January 1, 2007; 95(1): 74 - 81. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. E. Moore, E. T. Fung, M. McGuire, C. C. Rabkin, A. Molinaro, Z. Wang, F. Zhang, J. Wang, C. Yip, X.-Y. Meng, et al. Evaluation of apolipoprotein A1 and posttranslationally modified forms of transthyretin as biomarkers for ovarian cancer detection in an independent study population. Cancer Epidemiol. Biomarkers Prev., September 1, 2006; 15(9): 1641 - 1646. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Pang, A. Lin, M. Holford, B. E. Enerson, B. Lu, M. P. Lawton, E. Floyd, and H. Zhao Pathway analysis using random forests classification and regression Bioinformatics, August 15, 2006; 22(16): 2028 - 2036. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Larranaga, B. Calvo, R. Santana, C. Bielza, J. Galdiano, I. Inza, J. A. Lozano, R. Armananzas, G. Santafe, A. Perez, et al. Machine learning in bioinformatics Brief Bioinform, March 1, 2006; 7(1): 86 - 112. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. Wu Differential gene expression detection and sample classification using penalized linear regression models Bioinformatics, February 15, 2006; 22(4): 472 - 476. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. Chung, D. Clifford, M. Buckley, and R. C. Baxter Novel Biomarkers of Human Growth Hormone Action from Serum Proteomic Profiling Using Protein Chip Mass Spectrometry J. Clin. Endocrinol. Metab., February 1, 2006; 91(2): 671 - 677. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. W. Randolph, B. L. Mitchell, D. F. McLerran, P. D. Lampe, and Z. Feng Quantifying Peptide Signal in MALDI-TOF Mass Spectrometry Data Mol. Cell. Proteomics, December 1, 2005; 4(12): 1990 - 1999. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. Spentzos, D. A. Levine, S. Kolia, H. Otu, J. Boyd, T. A. Libermann, and S. A. Cannistra Unique Gene Expression Profile Based on Pathologic Response in Epithelial Ovarian Cancer J. Clin. Oncol., November 1, 2005; 23(31): 7911 - 7918. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Geurts, M. Fillet, D. de Seny, M.-A. Meuwis, M. Malaise, M.-P. Merville, and L. Wehenkel Proteomic mass spectra classification using decision tree based ensemble methods Bioinformatics, July 15, 2005; 21(14): 3138 - 3145. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. Bao, M. Zhou, and Y. Cui nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms Nucleic Acids Res., July 1, 2005; 33(suppl_2): W480 - W482. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Bao and Y. Cui Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information Bioinformatics, May 15, 2005; 21(10): 2185 - 2190. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. S. Yu, S. Ongarello, R. Fiedler, X. W. Chen, G. Toffolo, C. Cobelli, and Z. Trajanoski Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data Bioinformatics, May 15, 2005; 21(10): 2200 - 2209. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Listgarten and A. Emili Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry Mol. Cell. Proteomics, April 1, 2005; 4(4): 419 - 434. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. Guan and H. Zhao A semiparametric approach for marker gene selection based on gene expression data Bioinformatics, February 15, 2005; 21(4): 529 - 536. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 N. Carriero, M. V. Osier, K.-H. Cheung, P. L. Miller, M. Gerstein, H. Zhao, B. Wu, S. Rifkin, J. Chang, H. Zhang, et al. A High Productivity/Low Maintenance Approach to High-performance Computation for Biomedicine: Four Case Studies J. Am. Med. Inform. Assoc., January 1, 2005; 12(1): 90 - 98. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Spentzos, D. A. Levine, M. F. Ramoni, M. Joseph, X. Gu, J. Boyd, Towia. A. Libermann, and S. A. Cannistra Gene Expression Signature With Independent Prognostic Significance in Epithelial Ovarian Cancer J. Clin. Oncol., December 1, 2004; 22(23): 4700 - 4710. [Abstract] [Full Text] [PDF]
|
|