pyInfinityFlow: optimized imputation and analysis of high-dimensional flow cytometry data for millions of cells

Motivation: While conventional flow cytometry is limited to dozens of markers, new experimental and computational strategies, such as Infinity Flow, allow for the generation and imputation of hundreds of cell surface protein markers in millions of cells. Here, we describe an end-to-end analysis workflow for Infinity Flow data in Python. Results: pyInfinityFlow enables the efficient analysis of millions of cells, without down-sampling, through direct integration with well-established Python packages for single-cell genomics analysis. pyInfinityFlow accurately identifies both common and extremely rare cell populations which are challenging to define from single-cell genomics studies alone. We demonstrate that this workflow can nominate novel markers to design new flow cytometry gating strategies for predicted cell populations. pyInfinityFlow can be extended to diverse cell discovery analyses with flexibility to adapt to diverse Infinity Flow experimental designs.

Automated summary

Conventional flow cytometry is limited in its ability to detect markers, while new strategies like Infinity Flow can generate and impute hundreds of markers in millions of cells. This study introduces a Python workflow, pyInfinityFlow, for analyzing Infinity Flow data, allowing for the efficient analysis of millions of cells without down-sampling. The workflow accurately identifies both common and rare cell populations and can nominate novel markers for flow cytometry gating strategies.