title: Greedy feature selection for glycan chromatography data with the generalized Dirichlet distribution.
abstract: BACKGROUND
  Glycoproteins are involved in a diverse range of biochemical and biological processes. Changes in protein glycosylation are believed to occur in many diseases, particularly during cancer initiation and progression. The identification of biomarkers for human disease states is becoming increasingly important, as early detection is key to improving survival and recovery rates. To this end, the serum glycome has been proposed as a potential source of biomarkers for different types of cancers.High-throughput hydrophilic interaction liquid chromatography (HILIC) technology for glycan analysis allows for the detailed quantification of the glycan content in human serum. However, the experimental data from this analysis is compositional by nature. Compositional data are subject to a constant-sum constraint, which restricts the sample space to a simplex. Statistical analysis of glycan chromatography datasets should account for their unusual mathematical properties.As the volume of glycan HILIC data being produced increases, there is a considerable need for a framework to support appropriate statistical analysis. Proposed here is a methodology for feature selection in compositional data. The principal objective is to provide a template for the analysis of glycan chromatography data that may be used to identify potential glycan biomarkers.
  
  
  RESULTS
  A greedy search algorithm, based on the generalized Dirichlet distribution, is carried out over the feature space to search for the set of "grouping variables" that best discriminate between known group structures in the data, modelling the compositional variables using beta distributions. The algorithm is applied to two glycan chromatography datasets. Statistical classification methods are used to test the ability of the selected features to differentiate between known groups in the data. Two well-known methods are used for comparison: correlation-based feature selection (CFS) and recursive partitioning (rpart). CFS is a feature selection method, while recursive partitioning is a learning tree algorithm that has been used for feature selection in the past.
  
  
  CONCLUSIONS
  The proposed feature selection method performs well for both glycan chromatography datasets. It is computationally slower, but results in a lower misclassification rate and a higher sensitivity rate than both correlation-based feature selection and the classification tree method.
authors: School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland. mgalliga@tcd.ie
authors: Marie C
authors: MC
authors: Galligan
authors: Radka
authors: R
authors: Saldova
authors: Matthew P
authors: MP
authors: Campbell
authors: Pauline M
authors: PM
authors: Rudd
authors: Thomas B
authors: TB
authors: Murphy
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000097:blood / Q000737:chemistry; D002851:Chromatography, High Pressure Liquid / Q000379:methods; D005260:Female; D006031:Glycosylation; D006801:Humans; D008175:Lung Neoplasms / Q000097:blood / Q000737:chemistry* / Q000175:diagnosis; D008297:Male; D009367:Neoplasm Staging / Q000379:methods; D011134:Polysaccharides / Q000097:blood / Q000737:chemistry*; D011470:Prostatic Hyperplasia / Q000097:blood / Q000175:diagnosis; D011471:Prostatic Neoplasms / Q000097:blood / Q000737:chemistry* / Q000175:diagnosis; D015203:Reproducibility of Results
chemical_list: D014408:Biomarkers, Tumor; D011134:Polysaccharides
references: Glycobiology. 2011 Feb;21(2):195-205
references: 20861084
references: Proc Natl Acad Sci U S A. 2002 May 14;99(10):6562-6
references: 11983868
references: Proteomics. 2009 Aug;9(15):3796-801
references: 19670245
references: Proteomics. 2008 Jan;8(1):8-20
references: 18095367
references: CA Cancer J Clin. 2011 Mar-Apr;61(2):69-90
references: 21296855
references: Anal Biochem. 2008 May 1;376(1):1-12
references: 18194658
references: Comput Stat Data Anal. 2008 Aug 15;52(12):5344-5355
references: 22058582
references: Bioinformatics. 2005 Oct 15;21(20):3940-1
references: 16096348
references: BMC Bioinformatics. 2007 Jun 06;8:182
references: 17553157
references: Bioinformatics. 2008 May 1;24(9):1214-6
references: 18344517
references: Proteomics. 2008 Aug;8(16):3284-93
references: 18646009
references: J Proteome Res. 2011 Apr 1;10(4):1755-64
references: 21214223
references: Plant Signal Behav. 2011 Jan;6(1):113-6
references: 21270539
references: Anal Chem. 2010 Dec 15;82(24):10208-15
references: 21073175
references: Nat Methods. 2005 Nov;2(11):817-24
references: 16278650
references: Ann Intern Med. 2011 Dec 6;155(11):762-71
references: 21984740
references: Anal Biochem. 1997 Jul 15;250(1):82-101
references: 9234902
references: BMC Genomics. 2008;9 Suppl 1:S13
references: 18366602
references: Ann Appl Stat. 2010 Mar 1;4(1):396-421
references: 20936055
references: Postgrad Med J. 2009 Jul;85(1005):358-63
references: 19581246
references: Anal Biochem. 1995 Sep 20;230(2):229-38
references: 7503412