flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry.
Lux, Markus 1; Brinkman, Ryan Remy 2, 3, 4; Chauve, Cedric 5; Laing, Adam 6; Lorenc, Anna 6; Abeler-Dorner, Lucie 6; Hammer, Barbara 7; Wren, Jonathan
[Article] Bioinformatics. 34(13):2245-2253, July 1, 2018.

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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 Symbol-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.

(C) Copyright Oxford University Press 2018.