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A parameter estimation method for fluorescence lifetime data

BACKGROUND: When modeling single-molecule fluorescence lifetime experimental data, the analysis often involves fitting a biexponential distribution to binned data. When dealing with small sample sizes, there is the potential for convergence failure in numerical optimization, for convergence to local...

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Autores principales: Sewell, Daniel, Kim, Hajin, Ha, Taekjip, Ma, Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4467687/
https://www.ncbi.nlm.nih.gov/pubmed/26054354
http://dx.doi.org/10.1186/s13104-015-1176-y
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author Sewell, Daniel
Kim, Hajin
Ha, Taekjip
Ma, Ping
author_facet Sewell, Daniel
Kim, Hajin
Ha, Taekjip
Ma, Ping
author_sort Sewell, Daniel
collection PubMed
description BACKGROUND: When modeling single-molecule fluorescence lifetime experimental data, the analysis often involves fitting a biexponential distribution to binned data. When dealing with small sample sizes, there is the potential for convergence failure in numerical optimization, for convergence to local optima resulting in physically unreasonable parameter estimates, and also for overfitting the data. RESULTS: To avoid the problems that arise in small sample sizes, we have developed a gamma conversion method to estimate the lifetime components. The key idea is to use a gamma distribution for initial numerical optimization and then convert the gamma parameters to biexponential ones via moment matching. A simulation study is undertaken with 30 unique configurations of parameter values. We also performed the same analysis on data obtained from a fluorescence lifetime experiment using the fluorophore Cy3. In both the simulation study and the real data analysis, fitting the biexponential directly led to a large number of data sets whose estimates were physically unreasonable, while using the gamma conversion yielded estimates consistently close to the true values. CONCLUSIONS: Our analysis shows that using numerical optimization methods to fit the biexponential distribution directly can lead to failure to converge, convergence to physically unreasonable parameter estimates, and overfitting the data. The proposed gamma conversion method avoids these numerical difficulties, yielding better results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13104-015-1176-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-44676872015-06-16 A parameter estimation method for fluorescence lifetime data Sewell, Daniel Kim, Hajin Ha, Taekjip Ma, Ping BMC Res Notes Technical Note BACKGROUND: When modeling single-molecule fluorescence lifetime experimental data, the analysis often involves fitting a biexponential distribution to binned data. When dealing with small sample sizes, there is the potential for convergence failure in numerical optimization, for convergence to local optima resulting in physically unreasonable parameter estimates, and also for overfitting the data. RESULTS: To avoid the problems that arise in small sample sizes, we have developed a gamma conversion method to estimate the lifetime components. The key idea is to use a gamma distribution for initial numerical optimization and then convert the gamma parameters to biexponential ones via moment matching. A simulation study is undertaken with 30 unique configurations of parameter values. We also performed the same analysis on data obtained from a fluorescence lifetime experiment using the fluorophore Cy3. In both the simulation study and the real data analysis, fitting the biexponential directly led to a large number of data sets whose estimates were physically unreasonable, while using the gamma conversion yielded estimates consistently close to the true values. CONCLUSIONS: Our analysis shows that using numerical optimization methods to fit the biexponential distribution directly can lead to failure to converge, convergence to physically unreasonable parameter estimates, and overfitting the data. The proposed gamma conversion method avoids these numerical difficulties, yielding better results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13104-015-1176-y) contains supplementary material, which is available to authorized users. BioMed Central 2015-06-09 /pmc/articles/PMC4467687/ /pubmed/26054354 http://dx.doi.org/10.1186/s13104-015-1176-y Text en © Sewell et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Note
Sewell, Daniel
Kim, Hajin
Ha, Taekjip
Ma, Ping
A parameter estimation method for fluorescence lifetime data
title A parameter estimation method for fluorescence lifetime data
title_full A parameter estimation method for fluorescence lifetime data
title_fullStr A parameter estimation method for fluorescence lifetime data
title_full_unstemmed A parameter estimation method for fluorescence lifetime data
title_short A parameter estimation method for fluorescence lifetime data
title_sort parameter estimation method for fluorescence lifetime data
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4467687/
https://www.ncbi.nlm.nih.gov/pubmed/26054354
http://dx.doi.org/10.1186/s13104-015-1176-y
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