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
• Machine learning applied to flow cytometry data
• Reproduced analysis from a published paper
• A multi-dimensional induction in yeast
• Calibrated flow cytometry

or some screenshots from the GUI

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.

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.

• A point-and-click interface for easy analysis.

• Python modules to integrate into larger apps, automation, or for use in a Jupyter notebook

• 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 a Jupyter 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 analysis (and will make R users feel right at home.)

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

• Quantitative and statistically sound. Ready access to useful data-driven tools for analysis, such as fitting 2-dimensional Gaussians for automated gating and mixture modeling.

If you just want the point-and-click version (not the Python modules), you can install it from http://cytoflow.github.io/

See the installation notes on ReadTheDocs. Installation has been tested on Linux, Windows (x86_64) and Mac. Cytoflow is distributed as an Anaconda package (recommended) as well as a traditional Python package.

Cytoflow's documentation lives 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.