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. Attached Files Name Description MIMEType Size Downloads nguyen-rnaseqcount-2016.pdf Published version application/pdf 1.62MB 78 Title RNA-seq count data modelling by grey relational analysis and nonparametric Gaussian process Author(s) Nguyen, Thanh orcid.org/0000-0001-9709-1663 Bhatti, Asim orcid.org/0000-0001-6876-1437 Yang, Samuel Nahavandi, Saeid orcid.org/0000-0002-0360-5270 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30089018 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) Open Access Collection GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Wed, 16 Nov 2016, 13:06:02 EST Thu, 17 Nov 2016, 04:02:39 EST Wed, 11 Jan 2017, 09:54:16 EST Mon, 03 Apr 2017, 10:03:16 EST Tue, 04 Apr 2017, 06:03:18 EST Tue, 06 Jun 2017, 12:10:33 EST Tue, 06 Jun 2017, 12:14:26 EST Thu, 15 Jun 2017, 08:14:12 EST Thu, 15 Jun 2017, 10:53:11 EST Fri, 07 Sep 2018, 12:36:38 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 402 Abstract Views, 80 File Downloads  -  Detailed Statistics Created: Mon, 03 Apr 2017, 10:03:16 EST

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.