Flow Cytometry Toolkit
Project description
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 adds 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 include:
- Reading Flow Cytometry Standard data (FCS files), including FCS versions:
- 2.0
- 3.0
- 3.1
- Exporting FCS data in any of the following formats:
- A new FCS 3.1 file, with modified metadata and/or filtered events
- NumPy array
- Pandas DataFrame
- CSV text file
- Compensation of FCS events
- Apply compensation from spillover matrices in multiple formats:
- As the $SPILL or $SPILLOVER keyword value format
- FlowJo tab-delimited text format
- NumPy array
- GatingML 2.0 spectrumMatrix XML element
- Automatically create a compensation matrix from a set of compensation bead files
- Apply compensation from spillover matrices in multiple formats:
- Transformation of FCS events in a variety of transforms used in the flow community:
- 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
- Programmatically create gating strategies including polygon, rectangle, range, ellipsoid, quadrant, and boolean gates
- Easily retrieve gating results from a gating strategy as a Pandas DataFrame. Results include:
- FCS sample ID
- Gate name
- Parent gate
- Gate path
- Gate level (position in gate hierarchy tree)
- Absolute event count
- Relative percentage
- Absolute percentage
- Full support for the GatingML 2.0 specification
- Optional, automatic filtering of negative scatter events and/or anomalous events
- Visualizing FCS event data:
- 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 3.6 or above. All dependencies are installable via pip, and are listed below.
Note: FlowKit and FlowUtils use C extensions for significant performance
improvements relating to various transformations. If using gcc
, version 5 or
above is required for correct Logicle and Hyperlog transformations.
Required Python dependencies:
- flowio >= 0.9.7
- flowutils >= 0.8.0
- numpy >= 1.13
- scipy >= 1.3
- statsmodels >= 0.8
- pandas >= 0.22
- matplotlib >= 3.0
- seaborn >= 0.9
- bokeh >= 1.4
- lxml >= 4.2
- anytree >= 2.6
Installation
From PyPI
FlowKit is available via the pip
command. However, NumPy must be installed prior in order to
compile the C extensions.
pip install numpy
pip install flowkit
From source
pip install numpy
git clone https://github.com/whitews/flowkit
cd flowkit
python setup.py install
Usage
Click on the links below to a few Jupyter notebooks that demonstrate basic usage of the library. 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.
- General Overview
- Applying Transforms to a Sample
- Compensating Spillover in a Sample
- Importing a FlowJo 10 WSP file & replicating analysis in FlowKit
Contributing
Want to get involved in the development of FlowKit?
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