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Cytometry A. 2016 May;89(5):461-71. doi: 10.1002/cyto.a.22837. Epub 2016 Mar 18.

flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data.

Author information

1
McKusick-Nathans Institute of Genetic Medicine and Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland.
2
Vaccine Research Center, National Institutes of Allergy and Infectious Disease, National Institutes of Health, Baltimore, Maryland.
3
Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, Canada.

Abstract

Modern flow cytometry systems can be coupled to plate readers for high-throughput acquisition. These systems allow hundreds of samples to be analyzed in a single day. Quality control of the data remains challenging, however, and is further complicated when a large number of parameters is measured in an experiment. Our examination of 29,228 publicly available FCS files from laboratories worldwide indicates 13.7% have a fluorescence anomaly. In particular, fluorescence measurements for a sample over the collection time may not remain stable due to fluctuations in fluid dynamics; the impact of instabilities may differ between samples and among parameters. Therefore, we hypothesized that tracking cell populations (which represent a summary of all parameters) in centered log ratio space would provide a sensitive and consistent method of quality control. Here, we present flowClean, an algorithm to track subset frequency changes within a sample during acquisition, and flag time periods with fluorescence perturbations leading to the emergence of false populations. Aberrant time periods are reported as a new parameter and added to a revised data file, allowing users to easily review and exclude those events from further analysis. We apply this method to proof-of-concept datasets and also to a subset of data from a recent vaccine trial. The algorithm flags events that are suspicious by visual inspection, as well as those showing more subtle effects that might not be consistently flagged by investigators reviewing the data manually, and out-performs the current state-of-the-art. flowClean is available as an R package on Bioconductor, as a module on the free-to-use GenePattern web server, and as a plugin for FlowJo X.

KEYWORDS:

automated; flow cytometry; high throughput; quality control

PMID:
26990501
PMCID:
PMC5522377
DOI:
10.1002/cyto.a.22837
[Indexed for MEDLINE]
Free PMC Article

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