Differential Expression Analysis in RNA-Seq by a Naive Bayes Classifier with Local Normalization.
To improve the applicability of RNA-seq technology, a large number of RNA-seq data analysis methods and correction algorithms have been developed. Although these new methods and algorithms have steadily improved transcriptome analysis, greater prediction accuracy is needed to better guide experiment...
| Published in: | BioMed Research International Vol. 2015; pp. 1 - 10 |
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| Main Authors: | , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
8/3/2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109031016&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109031016 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/3/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109031016 109031016 109031016 10.1155/2015/789516 109031016 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Differential Expression Analysis in RNA-Seq by a Naive Bayes Classifier with Local Normalization. aug: au: Dou, Yongchao Guo, Xiaomei Yuan, Lingling Holding, David R. Zhang, Chi affil: School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA sug: subj: RNA Sequence Analysis Computer Simulation Models, Statistical Funding Source ab: To improve the applicability of RNA-seq technology, a large number of RNA-seq data analysis methods and correction algorithms have been developed. Although these new methods and algorithms have steadily improved transcriptome analysis, greater prediction accuracy is needed to better guide experimental designs with computational results. In this study, a new tool for the identification of differentially expressed genes with RNA-seq data, named GExposer, was developed. This tool introduces a local normalization algorithm to reduce the bias of nonrandomly positioned read depth. The naive Bayes classifier is employed to integrate fold change, transcript length, and GC content to identify differentially expressed genes. Results on several independent tests show that GExposer has better performance than other methods. The combination of the local normalization algorithm and naive Bayes classifier with three attributes can achieve better results; both false positive rates and false negative rates are reduced. However, only a small portion of genes is affected by the local normalization and GC content correction. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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