Browsing by Author "White, Scott"
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Item Open Access Establishment of normative ranges of the healthy human immune system with comprehensive polychromatic flow cytometry profiling.(PloS one, 2019-01) Yi, John S; Rosa-Bray, Marilyn; Staats, Janet; Zakroysky, Pearl; Chan, Cliburn; Russo, Melissa A; Dumbauld, Chelsae; White, Scott; Gierman, Todd; Weinhold, Kent J; Guptill, Jeffrey TExisting normative flow cytometry data have several limitations including small sample sizes, incompletely described study populations, variable flow cytometry methodology, and limited depth for defining lymphocyte subpopulations. To overcome these issues, we defined high-dimensional flow cytometry reference ranges for the healthy human immune system using Human Immunology Project Consortium methodologies after carefully screening 127 subjects deemed healthy through clinical and laboratory testing. We enrolled subjects in the following age cohorts: 18-29 years, 30-39, 40-49, and 50-66 and enrolled cohorts to ensure an even gender distribution and at least 30% non-Caucasians. From peripheral blood mononuclear cells, flow cytometry reference ranges were defined for >50 immune subsets including T-cell (activation, maturation, T follicular helper and regulatory T cell), B-cell, and innate cells. We also developed a web tool for visualization of the dataset and download of raw data. This dataset provides the immunology community with a resource to compare and extract data from rigorously characterized healthy subjects across age groups, gender and race.Item Open Access FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows.(Frontiers in immunology, 2021-01) White, Scott; Quinn, John; Enzor, Jennifer; Staats, Janet; Mosier, Sarah M; Almarode, James; Denny, Thomas N; Weinhold, Kent J; Ferrari, Guido; Chan, CliburnAn important challenge for primary or secondary analysis of cytometry data is how to facilitate productive collaboration between domain and quantitative experts. Domain experts in cytometry laboratories and core facilities increasingly recognize the need for automated workflows in the face of increasing data complexity, but by and large, still conduct all analysis using traditional applications, predominantly FlowJo. To a large extent, this cuts domain experts off from the rapidly growing library of Single Cell Data Science algorithms available, curtailing the potential contributions of these experts to the validation and interpretation of results. To address this challenge, we developed FlowKit, a Gating-ML 2.0-compliant Python package that can read and write FCS files and FlowJo workspaces. We present examples of the use of FlowKit for constructing reporting and analysis workflows, including round-tripping results to and from FlowJo for joint analysis by both domain and quantitative experts.