title: OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis.
abstract: Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.
authors.affiliation: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
authors.forename: Greg
authors.initials: G
authors.lastname: Finak
authors.affiliation: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
authors.forename: Jacob
authors.initials: J
authors.lastname: Frelinger
authors.affiliation: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
authors.forename: Wenxin
authors.initials: W
authors.lastname: Jiang
authors.affiliation: Agency for Science Technology and Research, Singapore Immunology Network, Singapore.
authors.forename: Evan W
authors.initials: EW
authors.lastname: Newell
authors.affiliation: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
authors.forename: John
authors.initials: J
authors.lastname: Ramey
authors.affiliation: Department of Microbiology and Immunology, Stanford University, Stanford, California, United States of America; Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, California, United States of America; The Howard Hughes Medical Institute, Stanford University, Stanford, California, United States of America.
authors.forename: Mark M
authors.initials: MM
authors.lastname: Davis
authors.affiliation: Infectious Diseases Division, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America; Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
authors.forename: Spyros A
authors.initials: SA
authors.lastname: Kalams
authors.affiliation: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America; Department of Laboratory Medicine, University of Washington, Seattle, Washington, United States of America.
authors.forename: Stephen C
authors.initials: SC
authors.lastname: De Rosa
authors.affiliation: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America; Department of Statistics, University of Washington, Seattle, Washington, United States of America.
authors.forename: Raphael
authors.initials: R
authors.lastname: Gottardo
mesh_terms: D018414:CD8-Positive T-Lymphocytes; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D015203:Reproducibility of Results; D012984:Software