What question did this study set out to answer?

To develop an automated quality-control algorithm that identifies and flags transient fluorescence anomalies in high-throughput flow cytometry data.

March 18, 2016Cytometry Part AOpen Access

flow C lean: Automated identification and removal of fluorescence anomalies in flow cytometry data

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To develop an automated quality-control algorithm that identifies and flags transient fluorescence anomalies in high-throughput flow cytometry data.
Surveyed 29,228 publicly available FCS files from laboratories worldwide to quantify baseline fluorescence anomalies.
Developed flowClean, an algorithm tracking cell subset frequency shifts in centered log-ratio space across sample acquisition time.
Validated the tool using proof-of-concept datasets and clinical trial data from a recent vaccine study, comparing performance to manual inspection and current state-of-the-art tools.
Analysis of 29,228 public flow cytometry files revealed that 13.7% contained fluorescence anomalies caused by fluid dynamic instabilities during acquisition.
The algorithm consistently detected visually evident anomalies as well as subtle perturbations that manual inspection missed, out-performing existing automated quality-control methods.
The tool writes flagged aberrant events into a revised data file parameter, enabling seamless review and event exclusion in downstream analytical pipelines.

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Implication

Computational validation demonstrates flowClean effectively flags acquisition anomalies across flow cytometry datasets, suggesting improved quality control for high-throughput experiments.

Cite This Study

Fletez‐Brant et al. (2016) studied this question.

synapsesocial.com/papers/6aaa3b1f522fb5f5774ab250 https://doi.org/10.1002/cyto.a.22837

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Implication

Computational validation demonstrates flowClean effectively flags acquisition anomalies across flow cytometry datasets, suggesting improved quality control for high-throughput experiments.

flow C lean: Automated identification and removal of fluorescence anomalies in flow cytometry data

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Discussion

Implication

Cite This Study

flow C lean: Automated identification and removal of fluorescence anomalies in flow cytometry data

Key Points

Discussion

Implication

Cite This Study

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