title: Outlier detection at the transcriptome-proteome interface.
abstract: BACKGROUND
  In high-throughput experimental biology, it is widely acknowledged that while expression levels measured at the levels of transcriptome and the corresponding proteome do not, in general, correlate well, messenger RNA levels are used as convenient proxies for protein levels. Our interest is in developing data-driven computational models that can bridge the gap between these two levels of measurement at which different mechanisms of regulation may act on different molecular species causing any observed lack of correlations. To this end, we build data-driven predictors of protein levels using mRNA levels and known proxies of translation efficiencies as covariates. Previous work showed that in such a setting, outliers with respect to the model are reliable candidates for post-translational regulation.
  
  
  RESULTS
  Here, we present and compare two novel formulations of deriving a protein concentration predictor from which outliers may be extracted in a systematic manner. The first approach, outlier rejecting regression, allows explicit specification of a certain fraction of the data as outliers. In a regression setting, this is a non-convex optimization problem which we solve by deriving a difference of convex functions algorithm (DCA). With post-translationally regulated proteins, one expects their concentrations to be affected primarily by disruption of protein stability. Our second algorithm exploits this observation by minimizing an asymmetric loss using quantile regression and extracts outlier proteins whose measured concentrations are lower than what a genome-wide regression would predict. We validate the two approaches on a dataset of yeast transcriptome and proteome. Functional annotation check on detected outliers demonstrate that the methods are able to identify post-translationally regulated genes with high statistical confidence.
authors.affiliation: School of Electronics and Computer Science, University of Southampton, Southampton, UK.
authors.forename: Yawwani
authors.initials: Y
authors.lastname: Gunawardana
authors.affiliation: Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan and.
authors.forename: Shuhei
authors.initials: S
authors.lastname: Fujiwara
authors.affiliation: Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan and.
authors.forename: Akiko
authors.initials: A
authors.lastname: Takeda
authors.affiliation: Faculty of Medicine, Southampton General Hospital, University of Southampton, Southampton, UK.
authors.forename: Jeongmin
authors.initials: J
authors.lastname: Woo
authors.affiliation: Faculty of Medicine, Southampton General Hospital, University of Southampton, Southampton, UK.
authors.forename: Christopher
authors.initials: C
authors.lastname: Woelk
authors.affiliation: School of Electronics and Computer Science, University of Southampton, Southampton, UK.
authors.forename: Mahesan
authors.initials: M
authors.lastname: Niranjan
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000032:analysis; D019295:Computational Biology / Q000379:methods*; D015966:Gene Expression Regulation, Fungal; D016681:Genome, Fungal; D020543:Proteome / Q000378:metabolism*; D012333:RNA, Messenger / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics* / Q000378:metabolism*; D059467:Transcriptome
chemical_list: D015415:Biomarkers; D020543:Proteome; D012333:RNA, Messenger; D029701:Saccharomyces cerevisiae Proteins