Abstract
Cytometry analysis has grown in recent years with the expansion in the maximum number of parameters that can be acquired in a single experiment. In response to this there has been an increased effort to develop computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of cytometry. Here we present CytoPy, a Python framework for automated analysis of high dimensional cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. The capability of supervised classification algorithms in CytoPy to identify cell subsets was successfully confirmed by using the FlowCAP-I competition data. The applicability of the complete analytical pipeline to real world datasets was validated by immunophenotyping the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. Source code is available online at the https://github.com/burtonrj/CytoPy, and software documentation can be found at https://cytopy.readthedocs.io/.
