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Optimizing transformations for automated, high throughput analysis of flow cytometry data
BACKGROUND: In 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 da...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3243046/ https://www.ncbi.nlm.nih.gov/pubmed/21050468 http://dx.doi.org/10.1186/1471-2105-11-546 |
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author | Finak, Greg Perez, Juan-Manuel Weng, Andrew Gottardo, Raphael |
author_facet | Finak, Greg Perez, Juan-Manuel Weng, Andrew Gottardo, Raphael |
author_sort | Finak, Greg |
collection | PubMed |
description | BACKGROUND: In 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. RESULTS: We 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. CONCLUSIONS: Our 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. |
format | Online Article Text |
id | pubmed-3243046 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-32430462011-12-20 Optimizing transformations for automated, high throughput analysis of flow cytometry data Finak, Greg Perez, Juan-Manuel Weng, Andrew Gottardo, Raphael BMC Bioinformatics Methodology Article BACKGROUND: In 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. RESULTS: We 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. CONCLUSIONS: Our 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. BioMed Central 2010-11-04 /pmc/articles/PMC3243046/ /pubmed/21050468 http://dx.doi.org/10.1186/1471-2105-11-546 Text en Copyright ©2010 Finak et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Finak, Greg Perez, Juan-Manuel Weng, Andrew Gottardo, Raphael Optimizing transformations for automated, high throughput analysis of flow cytometry data |
title | Optimizing transformations for automated, high throughput analysis of flow cytometry data |
title_full | Optimizing transformations for automated, high throughput analysis of flow cytometry data |
title_fullStr | Optimizing transformations for automated, high throughput analysis of flow cytometry data |
title_full_unstemmed | Optimizing transformations for automated, high throughput analysis of flow cytometry data |
title_short | Optimizing transformations for automated, high throughput analysis of flow cytometry data |
title_sort | optimizing transformations for automated, high throughput analysis of flow cytometry data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3243046/ https://www.ncbi.nlm.nih.gov/pubmed/21050468 http://dx.doi.org/10.1186/1471-2105-11-546 |
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