Cytek Investor Day Presentation Deck
Flow Can Now Analyze 50 Parameters
Cyto-Feature Engineering: A
Pipeline for Flow Cytometry
Analysis to Uncover Immune
Populations and Associations with
Disease
Amy Fox¹, Taru S. Dutt', Burton Karger¹, Mauricio Rojas?, Andrés Obregón-Henao¹,
G. Brooke Anderson³ & Marcela Henao-Tamayo¹
Flow cytometers can now analyze up to 50 parameters per cell and millions of cells per sample;
however, conventional methods to analyze data are subjective and time-consuming. To address
these issues, we have developed a novel flow cytometry analysis pipeline to identify a plethora of cell
populations efficiently. Coupled with feature engineering and immunological context, researchers can
immediately extrapolate novel discoveries through easy-to-understand plots. The R-based pipeline
uses Fluorescence Minus One (FMO) controls or distinct population differences to develop thresholds for
positive/negative marker expression. The continuous data is transformed into binary data, capturing
a positive/negative biological dichotomy often of interest in characterizing cells. Next, a filtering
step refines the data from all identified cell phenotypes to populations of interest. The data can be
partitioned by immune lineages and statistically correlated to other experimental measurements.
The pipeline's modularity allows customization of statistical testing, adoption of alternative initial
gating steps, and incorporation of other datasets. Validation of this pipeline through manual gating of
two datasets (murine splenocytes and human whole blood) confirmed its accuracy in identifying even
rare subsets. Lastly, this pipeline can be applied in all disciplines utilizing flow cytometry regardless
of cytometer or panel design. The code is available at https://github.com/aef1004/cyto-feature_
engineering.
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Flow cytometers can now analyze up to 50 parameters (antigens,
size, granularity, cytokines, transcription factors, etc.) per cell and
millions of cells per sample
Conventional flow cytometry data analysis uses manual gating of
cells on 2D plots to distinguish populations 1-2 dimensions at a
time; this makes it both subjective and time consuming (up to 15
hours per experiment)
Better methods are therefore critically needed to take full advantage
of this powerful technology.
Data Cleaning
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Visualization
Phenotype
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Population Correlation
Cell Percentage
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Hypothesis
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Population and CFU
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A Selection of Application Areas for FSP
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