Dataset Information

High-Throughput Analysis of Clinical Flow Cytometry Data by Automated Gating.

ABSTRACT: Advancements in flow cytometers with capability to measure 15 or more parameters have enabled us to characterize cell populations at unprecedented levels of detail. Beyond discovery research, there is now a growing demand to dive deeper into evaluating the immune response in clinical trials for immune modulating compounds. However, for high-volume, complex flow cytometry data generated in clinical trials, conventional manual gating remains the standard of practice. Traditional manual gating is resource intense and becomes a bottleneck and an impractical method to complete high volumes of flow cytometry data analysis. Current efforts to automate "manual gating" have shown that computational algorithms can facilitate the analysis of daunting multi-parameter data; however, a greater degree of precision in comparison with traditional manual gating is needed for wide-scale adoption of automated gating methods. In an effort to more closely follow the manual gating process, our automated gating pipeline was created to include negative controls (Fluorescence Minus One [FMO]) to enhance the reliability of gate placement. We demonstrate that use of an automated pipeline, heavily relying on FMO controls for population discrimination, can analyze multi-parameter, large-scale clinical datasets with comparable precision and accuracy to traditional manual gating.

SUBMITTER: Lee H

PROVIDER: S-EPMC6448119 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

High-Throughput Analysis of Clinical Flow Cytometry Data by Automated Gating.

Lee Hunjoong H Sun Yongliang Y Patti-Diaz Lisa L Hedrick Michael M Ehrhardt Anka G AG

Bioinformatics and biology insights 20190403

Advancements in flow cytometers with capability to measure 15 or more parameters have enabled us to characterize cell populations at unprecedented levels of detail. Beyond discovery research, there is now a growing demand to dive deeper into evaluating the immune response in clinical trials for immune modulating compounds. However, for high-volume, complex flow cytometry data generated in clinical trials, conventional manual gating remains the standard of practice. Traditional manual gating is r ...[more]

PMID: 30983860

Similar Datasets

Project description:BackgroundIn a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations.ResultsWe compare the performance of parameter-optimized and default-parameter (in flowCore) data transformations on real and simulated data by measuring the variation in the locations of cell populations across samples, discovered via automated gating in both the scatter and fluorescence channels. We find that parameter-optimized transformations improve visualization, reduce variability in the location of discovered cell populations across samples, and decrease the misclassification (mis-gating) of individual events when compared to default-parameter counterparts.ConclusionsOur results indicate that the preferred transformation for fluorescence channels is a parameter- optimized biexponential or generalized Box-Cox, in accordance with current best practices. Interestingly, for populations in the scatter channels, we find that the optimized hyperbolic arcsine may be a better choice in a high-throughput setting than current standard practice of no transformation. However, generally speaking, the choice of transformation remains data-dependent. We have implemented our algorithm in the BioConductor package, flowTrans, which is publicly available.

Dataset Information

High-Throughput Analysis of Clinical Flow Cytometry Data by Automated Gating.

Publications

High-Throughput Analysis of Clinical Flow Cytometry Data by Automated Gating.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets