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

Abstract

MotivationIdentification 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.ResultsflowLearn 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 implementationFlowLearn is available as an 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.

Integration of Mechanistic Immunological Knowledge into a Machine Learning Pipeline Increases Predictive Power

“…This process is labor intensive, error-prone, and may not identify all cell populations of interest 55 . In our future studies, we will combine state-of-the-art cell population identification algorithms [56][57][58][59][60] with our prior knowledge integrated to dynamically match clusters to the prior knowledge tensors for a more unbiased analysis. Third, this work only investigated incorporation of prior knowledge into the EN algorithm.…”

cytometree: A binary tree algorithm for automatic gating in cytometry analysis

“…High F-measure values have been reported by Li et al(25) who proposed a deep learning algorithm, DeepCyTOF; these values, however, are not comparable to those of unsupervised algorithms; in a very recent paper, Lux et al(26) report rather modest F-measure values for DeepCyTOF. High F-measure values have been reported by Li et al(25) who proposed a deep learning algorithm, DeepCyTOF; these values, however, are not comparable to those of unsupervised algorithms; in a very recent paper, Lux et al(26) report rather modest F-measure values for DeepCyTOF.…”

Improving the Rigor and Reproducibility of Flow Cytometry‐Based Clinical Research and Trials Through Automated Data Analysis

“…Data were generated using DuraClone's dry reagent technology (Beckman Coulter) preformatted panel antibody cocktails. Machine learning holds the promise of reducing the time required to parameterize supervised algorithms (20,21). In addition, we compared the reference manual values with those obtained by two additional manual analyzers who followed an identical gating strategy.…”

“…This is no different in principle from what would happen with manual analysis. Machine learning holds the promise of reducing the time required to parameterize supervised algorithms (20,21). However, like unsupervised methods, they have not yet shown the necessary performance.…”