title: RchyOptimyx: cellular hierarchy optimization for flow cytometry.
abstract: Analysis of high-dimensional flow cytometry datasets can reveal novel cell populations with poorly understood biology. Following discovery, characterization of these populations in terms of the critical markers involved is an important step, as this can help to both better understand the biology of these populations and aid in designing simpler marker panels to identify them on simpler instruments and with fewer reagents (i.e., in resource poor or highly regulated clinical settings). However, current tools to design panels based on the biological characteristics of the target cell populations work exclusively based on technical parameters (e.g., instrument configurations, spectral overlap, and reagent availability). To address this shortcoming, we developed RchyOptimyx (cellular hieraRCHY OPTIMization), a computational tool that constructs cellular hierarchies by combining automated gating with dynamic programming and graph theory to provide the best gating strategies to identify a target population to a desired level of purity or correlation with a clinical outcome, using the simplest possible marker panels. RchyOptimyx can assess and graphically present the trade-offs between marker choice and population specificity in high-dimensional flow or mass cytometry datasets. We present three proof-of-concept use cases for RchyOptimyx that involve 1) designing a panel of surface markers for identification of rare populations that are primarily characterized using their intracellular signature; 2) simplifying the gating strategy for identification of a target cell population; 3) identification of a non-redundant marker set to identify a target cell population.
authors.affiliation: Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, Canada.
authors.forename: Nima
authors.initials: N
authors.lastname: Aghaeepour
authors.forename: Adrin
authors.initials: A
authors.lastname: Jalali
authors.forename: Kieran
authors.initials: K
authors.lastname: O'Neill
authors.forename: Pratip K
authors.initials: PK
authors.lastname: Chattopadhyay
authors.forename: Mario
authors.initials: M
authors.lastname: Roederer
authors.forename: Holger H
authors.initials: HH
authors.lastname: Hoos
authors.forename: Ryan R
authors.initials: RR
authors.lastname: Brinkman
mesh_terms: D000465:Algorithms; D015703:Antigens, CD / Q000032:analysis / Q000276:immunology; D015415:Biomarkers / Q000032:analysis; D001854:Bone Marrow Cells / Q000166:cytology* / Q000276:immunology; D019295:Computational Biology / Q000379:methods; D005434:Flow Cytometry / Q000379:methods*; D015658:HIV Infections / Q000276:immunology; D006801:Humans; D016130:Immunophenotyping / Q000379:methods; D015851:Interleukin-7 / Q000276:immunology; D008070:Lipopolysaccharides / Q000276:immunology; D010641:Phenotype; D012984:Software; D013194:Staining and Labeling; D013601:T-Lymphocytes / Q000166:cytology / Q000276:immunology
chemical_list: D015703:Antigens, CD; D015415:Biomarkers; D015851:Interleukin-7; D008070:Lipopolysaccharides