title: Multi-task consensus clustering of genome-wide transcriptomes from related biological conditions.
abstract: Identifying the shared and pathogen-specific components of host transcriptional regulatory programs is important for understanding the principles of regulation of immune response. Recent efforts in systems biology studies of infectious diseases have resulted in a large collection of datasets measuring host transcriptional response to various pathogens. Computational methods to identify and compare gene expression modules across different infections offer a powerful way to identify strain-specific and shared components of the regulatory program. An important challenge is to identify statistically robust gene expression modules as well as to reliably detect genes that change their module memberships between infections.
  
  
  
  We present MULCCH (MULti-task spectral Consensus Clustering for Hierarchically related tasks), a consensus extension of a multi-task clustering algorithm to infer high-confidence strain-specific host response modules under infections from multiple virus strains. On simulated data, MULCCH more accurately identifies genes exhibiting pathogen-specific patterns compared to non-consensus and nonmulti-task clustering approaches. Application of MULCCH to mammalian transcriptional response to a panel of influenza viruses showed that our method identifies clusters with greater coherence compared to non-consensus methods. Further, MULCCH derived clusters are enriched for several immune system-related processes and regulators. In summary, MULCCH provides a reliable module-based approach to identify molecular pathways and gene sets characterizing commonality and specificity of host response to viruses of different pathogenicities.
  
  
  
  The source code is available at https://bitbucket.org/roygroup/mulcch
  
  
  
  sroy@biostat.wisc.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Niu
authors.affiliation: Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA.
authors.forename: Deborah
authors.initials: D
authors.lastname: Chasman
authors.affiliation: Influenza Research Institute, Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, 53711, USA.
authors.forename: Amie J
authors.initials: AJ
authors.lastname: Eisfeld
authors.affiliation: Influenza Research Institute, Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, 53711, USA Division of Virology, Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo, Tokyo, Japan.
authors.forename: Yoshihiro
authors.initials: Y
authors.lastname: Kawaoka
authors.affiliation: Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA.
authors.forename: Sushmita
authors.initials: S
authors.lastname: Roy
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology; D032921:Consensus; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D016678:Genome; D059467:Transcriptome