#CytoFlow ##Python tools for quantitative, reproducible flow cytometry analysis

Welcome to a different style of flow cytometry analysis. Take a look at some example Jupyter notebooks:

• Basic flow cytometry analysis
• An small-molecule induction curve with yeast
• Data-driven gating with gaussian mixture models
• Reproduced some analysis from a published paper
• Calibrated flow cytometry in MEFLs

What's wrong with other packages?

Packages such as FACSDiva and FlowJo are focused on primarily on identifying and counting subpopulations of cells in a multi-channel flow cytometry experiment. While this is important for many different applications, it reflects flow cytometry's origins in separating mixtures of cells based on differential staining of their cell surface markers.

Cytometers can also be used to measure internal cell state, frequently as reported by fluorescent proteins such as GFP. In this context, they function in a manner similar to a high-powered plate-reader: instead of reporting the sum fluorescence of a population of cells, the cytometer shows you the distribution of the cells' fluorescence. Thinking in terms of distributions, and how those distributions change as you vary an experimental variable, is something existing packages don't handle gracefully.

What's different about CytoFlow?

A few things.

• Free and open-source. Use the software free-of-charge; modify it to suit your own needs, then contribute your changes back so the rest of the community can benefit from them.

• Provides both Python modules (relatively complete) and a point-and-click interface (still in development)

• An emphasis on metadata. CytoFlow assumes that you are measuring fluorescence on several samples that were treated differently: either they were collected at different times, treated with varying levels of inducers, etc. You specify the conditions for each sample up front, then use those conditions to facet the analysis.

• Cytometry analysis conceptualized as a workflow. Raw cytometry data is usually not terribly useful: you may gate out cellular debris and aggregates (using FSC and SSC channels), then compensate for channel bleed-through, and finally select only transfected cells before actually looking at the parameters you're interested in experimentally. CytoFlow implements a workflow paradigm, where operations are applied sequentially; a workflow can be saved and re-used, or shared with your coworkers.

• Easy to use. Sane defaults; good documentation; focused on doing one thing and doing it well.

• Good visualization. I don't know about you, but I'm getting really tired of FACSDiva plots.

• Versatile. Built on Python, with a well-defined library of operations and visualizations that are well separated from the user interface. Need an analysis that CytoFlow doesn't have? Export your workflow to an IPython notebook and use any Python module you want to complete your analysis. Data is stored in a pandas.DataFrame, which is rapidly becoming the standard for Python data management (and will make R users feel right at home.)

• Extensible. Adding a new analysis module is simple; the interface to implement is only two or three functions.

• Quantitative and tatistically sound. Ready access to useful data-driven tools for analysis, such as fitting 2-dimensional Gaussians for automated gating and mixture modeling. cytoflow discourages (but does not prevent) wholesale rescaling of data, for example using the log10 operation. Instead, it encourages rescaling of the data's plots instead.

Note: this is still beta software! Caveat emptor!

Installation

See the installation notes on ReadTheDocs. Installation has been tested on Linux (Ubuntu Trusty) and Windows 7 (x86_64). Mac installation should be similar.

Documentation

There is some basic documentation at ReadTheDocs. Perhaps of most use is the module index. The example Jupyter notebooks, above, demonstrate how the package is intended to be used interactively.

Required packages

These are all in the setuptools spec.

For the core cytoflow library, you need the following Python packages:

python >= 2.7
pandas >= 0.17.0
numpy >= 1.9.0
numexpr >= 2.1
bottleneck >= 1.0
matplotlib == 1.4.3
scipy >= 0.14
scikit-learn >= 0.16
seaborn >= 0.7.0
traits >= 4.0
fcsparser >= 0.1.1

For the GUI, you additionally need:

pyface == 4.4.0
envisage >= 4.0
pyqt >= 4.10 -- this must be installed separately!

Note that many of these packages have additional dependencies, including but not limited to traitsui, decorator, etc. Everything except PyQT should be a well well-behaved PyPI package; you should be able to install all the above with pip install or the Canopy package manager.