Dataset Information

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

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.

SUBMITTER: Lux M

PROVIDER: S-EPMC6022609 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Lux Markus M Brinkman Ryan Remy RR Chauve Cedric C Laing Adam A Lorenc Anna A Abeler-Dörner Lucie L Hammer Barbara B

Bioinformatics (Oxford, England) 20180701 13

<h4>Motivation</h4>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, ...[more]

PMID: 29462241

Similar Datasets

Project description:BackgroundFlow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another.ResultsWe introduce BayesFlow, a pipeline for latent modeling of flow cytometry cell populations built upon a Bayesian hierarchical model. The model systematizes variation in location as well as shape. Expert knowledge can be incorporated through informative priors and the results can be supervised through compact and comprehensive visualizations. BayesFlow is applied to two synthetic and two real flow cytometry data sets. For the first real data set, taken from the FlowCAP I challenge, BayesFlow does not only give a gating which would place it among the top performers in FlowCAP I for this dataset, it also gives a more consistent treatment of different samples than either manual gating or other automated gating methods. The second real data set contains replicated flow cytometry measurements of samples from healthy individuals. BayesFlow gives here cell populations with clear expression patterns and small technical intra-donor variation as compared to biological inter-donor variation.ConclusionsModeling latent relations between samples through BayesFlow enables a systematic analysis of inter-sample variation. As opposed to other joint gating methods, effort is put at ensuring that the obtained partition of the data corresponds to actual cell populations, and the result is therefore directly biologically interpretable. BayesFlow is freely available at GitHub.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets