title: Inter-platform concordance of gene expression data for the prediction of chemical mode of action.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  An extended abstract of this research paper was selected for the CAMDA Satellite Meeting to ISMB 2015 by the CAMDA Programme Committee. The full research paper then underwent two rounds of Open Peer Review under a responsible CAMDA 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.
authors.affiliation: Office of Biostatistics & Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, 96813, HI, USA.
authors.forename: Chathura
authors.initials: C
authors.lastname: Siriwardhana
authors.affiliation: Department of Biostatistics, University of Florida, Gainesville, 32603, FL, USA.
authors.forename: Susmita
authors.initials: S
authors.lastname: Datta
authors.affiliation: Department of Biostatistics, University of Florida, Gainesville, 32603, FL, USA. somnath.datta@ufl.edu.
authors.forename: Somnath
authors.initials: S
authors.lastname: Datta
mesh_terms: D000818:Animals; D015870:Gene Expression; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012313:RNA / Q000235:genetics; D051381:Rats; D017207:Rats, Sprague-Dawley; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D012313:RNA
keywords: Classification; Microarray; RNASeq