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RNA-seq count data modelling by grey relational analysis and nonparametric Gaussian process

Nguyen, Thanh, Bhatti, Asim, Yang, Samuel and Nahavandi, Saeid 2016, RNA-seq count data modelling by grey relational analysis and nonparametric Gaussian process, PLoS one, vol. 11, no. 10, pp. 1-18, doi: 10.1371/journal.pone.0164766.

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Title RNA-seq count data modelling by grey relational analysis and nonparametric Gaussian process
Author(s) Nguyen, Thanh
Bhatti, AsimORCID iD for Bhatti, Asim orcid.org/0000-0001-6876-1437
Yang, Samuel
Nahavandi, Saeid
Journal name PLoS one
Volume number 11
Issue number 10
Article ID e0164766
Start page 1
End page 18
Total pages 18
Publisher Public Library of Science
Place of publication San Francisco, Calif.
Publication date 2016-10-26
ISSN 1932-6203
Keyword(s) cervical cancer
RNA sequencing
entropy
sequence analysis
gene expression
microarrays
RNA analysis
statistical data
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
DIFFERENTIAL EXPRESSION
CANCER CLASSIFICATION
SEQUENCING DATA
GENE SELECTION
DISPERSION
DISCOVERY
Summary This paper introduces an approach to classification of RNA-seq read counts using grey relational analysis (GRA) and Bayesian Gaussian process (GP) models. Read counts are transformed to microarray-like data to facilitate normal-based statistical methods. GRA is designed to select differentially expressed genes by integrating outcomes of five individual feature selection methods including two-sample t-test, entropy test, Bhattacharyya distance, Wilcoxon test and receiver operating characteristic curve. GRA performs as an aggregate filter method through combining advantages of the individual methods to produce significant feature subsets that are then fed into a nonparametric GP model for classification. The proposed approach is verified by using two benchmark real datasets and the five-fold cross-validation method. Experimental results show the performance dominance of the GRA-based feature selection method as well as GP classifier against their competing methods. Moreover, the results demonstrate that GRA-GP considerably dominates the sparse Poisson linear discriminant analysis classifiers, which were introduced specifically for read counts, on different number of features. The proposed approach therefore can be implemented effectively in real practice for read count data analysis, which is useful in many applications including understanding disease pathogenesis, diagnosis and treatment monitoring at the molecular level.
Language eng
DOI 10.1371/journal.pone.0164766
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2016, Nguyen et al
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089018

Document type: Journal Article
Collections: Centre for Intelligent Systems Research
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.