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Bioinformatics. 2018 Jul 1;34(13):2245-2253. doi: 10.1093/bioinformatics/bty082.

flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry.

Author information

1
Computational Methods for the Analysis of the Diversity and Dynamics of Genomes, Bielefeld University, Bielefeld, Germany.
2
Terry Fox Laboratory, BC Cancer Research Centre, Vancouver, Canada.
3
Department of Medical Genetics, University of British Columbia, Vancouver, Canada.
4
Cytapex Bioinformatics Inc., Vancouver, Canada.
5
Department of Mathematics, Simon Fraser University, Vancouver, Canada.
6
Department of Immunobiology, King's College London, London, UK.
7
CITEC Centre of Excellence, Bielefeld, Germany.

Abstract

Motivation:

Identification of cell populations in flow cytometry is a critical part of the analysis and lays the groundwork for many applications and research discovery. The current paradigm of manual analysis is time consuming and subjective. A common goal of users is to replace manual analysis with automated methods that replicate their results. Supervised tools provide the best performance in such a use case, however they require fine parameterization to obtain the best results. Hence, there is a strong need for methods that are fast to setup, accurate and interpretable.

Results:

flowLearn is a semi-supervised approach for the quality-checked identification of cell populations. Using a very small number of manually gated samples, through density alignments it is able to predict gates on other samples with high accuracy and speed. On two state-of-the-art datasets, our tool achieves median(F1)-measures exceeding 0.99 for 31%, and 0.90 for 80% of all analyzed populations. Furthermore, users can directly interpret and adjust automated gates on new sample files to iteratively improve the initial training.

Availability and implementation:

FlowLearn is available as an R package on https://github.com/mlux86/flowLearn. Evaluation data is publicly available online. Details can be found in the Supplementary Material.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
29462241
PMCID:
PMC6022609
DOI:
10.1093/bioinformatics/bty082
[Indexed for MEDLINE]
Free PMC Article

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