Markus Lux, Ryan Remy Brinkman, Cedric Chauve, Adam Laing, Anna Lorenc, Lucie Abeler-Dörner, Barbara Hammer: flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry, Bioinformatics, 2018.
Identification of cell populations in flow cytometry is a critical part of analysis and lays the groundwork for both clinical diagnostics and research discovery. The current paradigm of manual analysis is time consuming and subjective. If the goal is to match manual analysis, supervised tools provide the best performance, 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 at the same time.
flowLearn is a semi-supervised
approach for the quality-checked identification of cell populations. Using as few as one manually
gated sample, through density alignments it is able to predict gates on other samples with high
accuracy and speed. On two state-of-the-art data sets, our tool achieves median F_1-measures
exceeding F_1 > 0.99 for 31%, and F_1 > 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.
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FlowLearn is very well documented and provides a quick start vignette with some example data.
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Clone the repository and build the
Rpackage usingdevtools::install(build_vignettes = T) -
Load the package using
library(flowLearn)and view the quick-start vignette usingvignette('flowLearnVignette').
extra/eval.Randextra/eval.sh: Scripts used for evaluating flowLearn on different data setsextra/prepare_*scripts: Converting raw FCS files into flowLearn's customDensityDataformatextra/comparisons/Scripts for running and evaluating flowSOM and DeepCyTOF on the mice data set.
It is permitted to use flowLearn for non-commercial, i.e. academic or other scholarly research use. Please have a look at the LICENSE file for details.