Cytoflow: Quantitative Analysis for Flow Cytometry

Flow cytometry is rapidly becoming a foundational tool for building, analyzing and optimizing synthetic gene circuits. Measuring circuit performance in populations of individual cells yields both insight into circuit operation and quantitative data necessary for modelling, prediction and forward design. Unfortunately, existing tools for analyzing these large multi-parametric data sets are ill-suited for new applications, difficult to extend, and make sharing analysis workflows difficult.

To address these limitations, we developed Cytoflow, a Python package for quantitative analysis of flow cytometry data. Cytoflow includes low-level modules that programmers can use to manipulate, analyze and visualize cytometry data, as well as a graphical user interface that lets non-programmers perform analyses and share workflows with colleagues. Importantly, the quantitative approach enabled by Cytoflow and similar tools enables more reliable engineering of synthetic gene networks: the approach reveals circuit dynamics, highlights process failures in quantification and characterization, and enables model-guided circuit design.

The Cytoflow project is entirely free software, licensed under the GNU Public License v2. Documentation, including example workflows, is available at http://bpteague.github.io/cytoflow.