title: Ontology based molecular signatures for immune cell types via gene expression analysis.
abstract: BACKGROUND
  New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an 'Ontologically BAsed Molecular Signature' (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type's identity.
  
  
  RESULTS
  We illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types.
  
  
  CONCLUSIONS
  This work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis - providing a new method for defining novel biomarkers and providing an opportunity for new biological insights.
authors.affiliation: Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, 100 High Street, Buffalo, NY 14203, USA. addiehl@buffalo.edu.
authors.forename: Terrence F
authors.initials: TF
authors.lastname: Meehan
authors.forename: Nicole A
authors.initials: NA
authors.lastname: Vasilevsky
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Mungall
authors.forename: David S
authors.initials: DS
authors.lastname: Dougall
authors.forename: Melissa A
authors.initials: MA
authors.lastname: Haendel
authors.forename: Judith A
authors.initials: JA
authors.lastname: Blake
authors.forename: Alexander D
authors.initials: AD
authors.lastname: Diehl
mesh_terms: D015415:Biomarkers / Q000032:analysis / Q000378:metabolism; D002477:Cells / Q000145:classification* / Q000166:cytology / Q000378:metabolism*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D063990:Gene Ontology; D005819:Genetic Markers / Q000235:genetics; D023281:Genomics / Q000379:methods*; D006801:Humans
chemical_list: D015415:Biomarkers; D005819:Genetic Markers