FlowKit

• Overview
• Features
• Requirements
• Installation
• Documentation
  • Tutorials
  • Advanced Examples
• Contributing
• Cite FlowKit
• Projects & Research Using FlowKit

Overview

FlowKit is a Python toolkit for flow cytometry analysis and visualization, with full support for the GatingML 2.0 standard and limited support for FlowJo 10 workspace files.

Features

• Read / Write FCS Files
  • Read FCS files, supporting FCS versions 2.0, 3.0, and 3.1
  • Export FCS data as:
    • A new FCS 3.1 file
    • NumPy array
    • Pandas DataFrame
    • CSV text file
• Compensation
  • Compensate events using spillover matrices from:
    • $SPILL or $SPILLOVER keyword value
    • FlowJo tab-delimited text
    • NumPy array
    • GatingML 2.0 spectrumMatrix XML element
• Transformation
  • Logicle
  • Inverse hyperbolic sine (ArcSinh)
  • FlowJo Bi-exponential
  • Hyperlog
  • Logarithmic
  • Channel ratios
  • Linear
• Gating
  • Full support for the GatingML 2.0 specification
    • Import GatingML XML documents as gating strategies
    • Export gating strategies as a valid GatingML XML document
  • Import FlowJo 10 workspace files, supporting the following features:
    • Linear, logarithmic, bi-exponential, and logicle transforms
    • Polygon, rectangle, ellipse, quadrant and Boolean gates
  • Programmatically create gating strategies including polygon, rectangle, range, ellipsoid, quadrant, and boolean gates
  • Retrieve gating results as a Pandas DataFrame
• Visualization
  • Histogram of single channel data
  • Contour density plot of two channels
  • Interactive scatter plot of two channels
  • Interactive scatter plot matrix of any combination of channels
  • Interactive scatter plots of gates with sample events

Requirements

FlowKit supports Python version 3.9 - 3.12. All dependencies are installable via pip, and are listed below.

NOTE: FlowUtils uses C extensions for significant performance improvements. For most platforms and Python versions, pre-built binaries of FlowUtils are available in PyPI (i.e. installable via pip). If a pre-built binary is not available for your environment, the C extensions must be compiled using the source package.

Required Python dependencies:

• flowio >= 1.3.0
• flowutils >= 1.1.0
• anytree >= 2.12
• bokeh >= 3.4
• contourpy >= 1.2.0
• lxml >= 5.1
• networkx >= 3.2
• numpy >= 1.22, <2
• pandas >= 2.1
• psutils >= 5.9
• scipy >= 1.11.1

Installation

NOTE: For macOS users running on Apple Silicon, the version of pip may need to be upgraded prior to installing FlowKit in order to install the required dependencies.

From PyPI

pip install flowkit

From source

Clone the repository and ensure pip is up-to-date.

git clone https://github.com/whitews/flowkit
cd flowkit
pip install --upgrade pip
pip install .

Documentation

The FlowKit API documentation is available on ReadTheDocs here. The tutorial notebooks in the docs/notebooks directory are a great place to get started with FlowKit, and are linked below. If you have any questions about FlowKit, find any bugs, or feel something is missing from the tutorials below please submit an issue to the GitHub repository here.

Changelogs

Changelogs for versions are available here

Tutorials

The series of Jupyter notebook tutorials can be found in the docs/notebooks directory of this repository. Note, the interactive scatterplots do not render on GitHub. The rendered versions are available on our documentation page here. Or, clone the repo and run them locally to see the fully interactive plots.

• Part 1 - Sample Class
• Part 2 - transforms Module & Matrix Class
• Part 3 - GatingStrategy & GatingResults Classes
• Part 4 - gates Module
• Part 5 - Session Class
• Part 6 - Workspace Class

Advanced Examples

Below are more advanced and practical examples for using FlowKit. If you have an example you would like to submit for consideration in this list (preferably with data), please submit an issue.

• Compare mean fluorescence intensity (MFI) in gated populations
• Importing a FlowJo 10 WSP file & replicating analysis in FlowKit
• Dimension reduction on gated populations
• Comparison between Leiden & Louvain clustering
• Saving Flow Analysis Data as AnnData objects for ScanPy

Contributing

Want to get involved in the development of FlowKit?

Read our CONTRIBUTING guidelines

Cite FlowKit

White, S., Quinn, J., Enzor, J., Staats, J., Mosier, S. M., Almarode, J., Denny, T. N., Weinhold, K. J., Ferrari, G., & Chan, C. (2021). FlowKit: A Python toolkit for integrated manual and automated cytometry analysis workflows. Frontiers in Immunology, 12. https://doi.org/10.3389/fimmu.2021.768541

Projects & Research Using FlowKit

The following projects and publications have utilized FlowKit. If you have a package or publication where FlowKit was used, and you want it listed here, feel free to submit an issue letting me know.

• Ishii, Hiroto et al. “Establishing a method for the cryopreservation of viable peripheral blood mononuclear cells in the International Space Station.” npj Microgravity vol. 10, Article number: 84 (2024), doi:10.1038/s41526-024-00423-2
• Dyikanov, Daniiar et al. “Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in patients with cancer.” Cancer Cell vol. 42,5 (2024), doi:10.1016/j.ccell.2024.04.008
• Mortelecque, Justine et al. “A selection and optimization strategy for single-domain antibodies targeting the PHF6 linear peptide within the Tau intrinsically disordered protein.” Journal of Biological Chemistry (2024), doi:10.1016/j.jbc.2024.107163
• Schmidt, Florian et al. “In-depth analysis of human virus-specific CD8+ T cells delineates unique phenotypic signatures for T cell specificity prediction.” Cell Reports vol. 42,10 (2023), doi:10.1016/j.celrep.2023.113250
• Schuster, Jonas et al. “Combining real-time fluorescence spectroscopy and flow cytometry to reveal new insights in DOC and cell characterization of drinking water.” Frontiers in Environmental Chemistry (2022), doi:10.3389/fenvc.2022.931067
• Rendeiro, André F et al. “Profiling of immune dysfunction in COVID-19 patients allows early prediction of disease progression.” Life science alliance vol. 4,2 e202000955. 24 Dec. 2020, doi:10.26508/lsa.202000955