FlowKit 0.9.0

Flow Cytometry Toolkit

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

Overview

FlowKit is an intuitive 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.

Version 0.6 added experimental support for exporting FlowJo 10 workspace files from a Session. Support is currently limited to exporting data from a single sample group. Please submit an issue if you find any bugs related to this feature.

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
  • Create a compensation matrix from a set of compensation bead files
• 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
  • Limited support for importing FlowJo 10 workspace files. Workspace files are currently limited to the following features:
    • Linear, logarithmic, bi-exponential, and logicle transforms
    • Polygon, rectangle, ellipse, and quadrant gates
    • Export a Session's sample group analysis as a FlowJo 10 workspace file
  • 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 3.7 or above. All dependencies are installable via pip, and are listed below.

Note: FlowUtils uses C extensions for significant performance improvements. For the most common platforms and Python versions, pre-built binaries are available in PyPI (and installable via pip).

If a pre-built binary of FlowUtils is not available for your environment, then the C extensions must be compiled using the source package. NumPy >=1.19 must be installed prior to compiling FlowUtils. If compiling using gcc, version 5 or later is required.

Required Python dependencies:

• flowio == 1.0.0
• flowutils == 1.0.0
• anytree >= 2.6
• bokeh >= 1.4, <3
• lxml >= 4.4
• matplotlib >= 3.1
• networkx >= 2.3
• numpy >= 1.20
• pandas >= 1.1
• psutils ~= 5.8
• scipy >= 1.3
• seaborn >= 0.11
• statsmodels

Installation

####Note for MacOS users running on Apple Silicon (e.g. M1 CPUs)

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

git clone https://github.com/whitews/flowkit
cd flowkit

python setup.py install

Documentation

The FlowKit API documentation is available on ReadTheDocs here. The tutorial notebooks in the examples 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 examples directory of this repository. Note, the interactive scatterplots do not render on GitHub. Clone the repo (or download the example notebooks), 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

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
• Customize gate for a single Sample in a Session sample group
• Importing a FlowJo 10 WSP file & replicating analysis in FlowKit
• Dimension reduction on gated populations
• Comparison between Leiden & Louvain clustering

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.

• 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