Inter-platform concordance of gene expression data for the prediction of chemical mode of action. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Siriwardhana, Chathura; Datta, Susmita; Datta, Somnath
• Is Part Of: Biology direct. Volume 11:Issue 1(2016)
• Page Start: 1
• Page End: 14
• Publication Date: 2016-12
• Subjects: Classification -- Microarray -- RNASeq; Biology -- Periodicals; 570.5
• DOI: 10.1186/s13062-016-0167-9
• Languages: English
• ISSNs: 1745-6150

Abstract

Abstract Background It is interesting to study the consistency of outcomes arising from two genomic platforms: Microarray and RNAseq, which are established on fundamentally different technologies. This topic has been frequently discussed from the prospect of comparing differentially expressed genes (DEGs). In this study, we explore the inter-platform concordance between microarray and RNASeq in their ability to classify samples based on genomic information. We use a set of 7 standard multi-class classifiers and an adaptive ensemble classifier developed around them to predict Chemical Modes of Actions (MOA) of data profiled by microarray and RNASeq platforms from Rat Liver samples exposed to a variety of chemical compounds. We study the concordance between microarray and RNASeq data in various forms, based on classifier's performance between two platforms. Results Using an ensemble classifier we observe improved prediction performance compared to a set of standard classifiers. We discover a clear concordance between each individual classifier's performances in two genomic platforms. Additionally, we identify a set of important genes those specifies MOAs, by focusing on their impact on the classification and later we find that some of these top genes have direct associations with the presence of toxic compounds in the liver. Conclusion Overall there appears to be fair amount of concordance between the two platforms as far as classification is concerned. We observe widely different classification performances among individual classifiers, which reflect the unreliability of restricting to a single classifier in the case of high dimensional classification problems. Reviewers An extended abstract of this research paper was selected for theCamda Satellite Meeting toIsmb 2015 by theCamda Programme Committee. The full research paper then underwent two rounds of Open Peer Review under a responsibleCamda Programme Committee member, Lan Hu, PhD (Bio-Rad Laboratories, Digital Biology Center-Cambridge). Open Peer Review was provided by Yiyi Liu and Partha Dey. The Reviewer Comments section shows the full reviews and author responses.