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. 2021 Nov 5:12:768541.
doi: 10.3389/fimmu.2021.768541. eCollection 2021.

FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows

Scott White  1   2   3 , John Quinn  4 , Jennifer Enzor  5   6 , Janet Staats  5   6 , Sarah M Mosier  1   6 , James Almarode  4 , Thomas N Denny  1   7 , Kent J Weinhold  1   5   6 , Guido Ferrari  1   3   6 , Cliburn Chan  1   2   3
Affiliations

FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows

Scott White et al. Front Immunol. .

Abstract

An 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.

Keywords: FlowJo; GatingML; flow cytometry; python (programming language); single cell data science; software; systems immunology.

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Conflict of interest statement

Authors JQ and JA were employed by BD Biosciences - FlowJo. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Basic elements provided by FlowKit for generating reports and downstream analysis. Clockwise from top left – metadata as key:value pairs, gating hierarchy as ASCII text, scatter plot of a gate, DataFrame of results from applying gating strategy specified in the Session class.
Figure 2
Figure 2
Comparison of marker distributions across CD4+ and CD8+ T cells.
Figure 3
Figure 3
Comparison of dimension reduction algorithms on the Singlets (left) and CD3+ (right) gated events. Events are pseudo-colored by the CD4 marker intensity.
Figure 4
Figure 4
(A) Comparison of the Leiden and Louvain community detection algorithms for clustering flow cytometry data. Labels in boxes are the assigned cluster indexes. Events plotted using PaCMAP for dimension reduction and colored according to the event cluster label. (B) Visualization of marker distribution for each cluster found using the Leiden algorithm shown in the top panel. Distributions are for values scaled to have zero mean and unit standard deviation; values to the left (right) of the thin vertical indicate mean marker values for that cluster that are below (above) the average for all cells.
Figure 5
Figure 5
Screenshot taken from FlowJo, showing an imported workspace that is entirely programmatically constructed.
See this image and copyright information in PMC

References

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