dc.contributor.author | Galligan, Marie C. | |
dc.contributor.author | Saldova, R. | |
dc.contributor.author | Campbell, Matthew P. | |
dc.contributor.author | Rudd, Pauline M. | |
dc.contributor.author | Murphy, Thomas B. | |
dc.date.accessioned | 2017-07-31T03:58:10Z | |
dc.date.available | 2017-07-31T03:58:10Z | |
dc.date.issued | 2013 | |
dc.identifier.issn | 1471-2105 | |
dc.identifier.doi | 10.1186/1471-2105-14-155 | |
dc.identifier.uri | http://hdl.handle.net/10072/342830 | |
dc.description.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. | |
dc.description.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | BioMed Central | |
dc.relation.ispartofpagefrom | 155-1 | |
dc.relation.ispartofpageto | 155-25 | |
dc.relation.ispartofjournal | BMC Bioinformatics | |
dc.relation.ispartofvolume | 14 | |
dc.subject.fieldofresearch | Mathematical sciences | |
dc.subject.fieldofresearch | Biological sciences | |
dc.subject.fieldofresearch | Biochemistry and cell biology not elsewhere classified | |
dc.subject.fieldofresearch | Information and computing sciences | |
dc.subject.fieldofresearchcode | 49 | |
dc.subject.fieldofresearchcode | 31 | |
dc.subject.fieldofresearchcode | 310199 | |
dc.subject.fieldofresearchcode | 46 | |
dc.title | Greedy feature selection for glycan chromatography data with the generalized Dirichlet distribution | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dc.type.code | C - Journal Articles | |
dcterms.license | http://creativecommons.org/licenses/by/2.0 | |
dc.description.version | Version of Record (VoR) | |
gro.description.notepublic | Page numbers are not for citation purposes. Instead, this article has the unique article number of 155. | |
gro.rights.copyright | © 2013 Galligan et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Campbell, Matthew | |