Dataset Information

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

ABSTRACT:

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.

Availability and implementation

pyInfinityFlow is freely available in GitHub (https://github.com/KyleFerchen/pyInfinityFlow) and on PyPI (https://pypi.org/project/pyInfinityFlow/). Package documentation with tutorials on a test dataset is available by Read the Docs (pyinfinityflow.readthedocs.io). The scripts and data for reproducing the results are available at https://github.com/KyleFerchen/pyInfinityFlow/tree/main/analysis_scripts, along with the raw flow cytometry input data.

SUBMITTER: Ferchen K

PROVIDER: S-EPMC10166583 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

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

Ferchen Kyle K Salomonis Nathan N Grimes H Leighton HL

Bioinformatics (Oxford, England) 20230501 5

<h4>Motivation</h4>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.<h4>Results</h4>pyInfinityFlow enables the efficient analysis of millions of cells, without down-sampling, through direct integration with well-established ...[more]

PMID: 37097893

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