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Quantitative study of single molecule location estimation techniques

Quantitative study of single molecule location estimation techniques
Quantitative study of single molecule location estimation techniques

Estimating the location of single molecules from microscopy images is a key step in many quantitative single molecule data analysis techniques. Different algorithms have been advocated for the fitting of single molecule data, particularly the nonlinear least squares and maximum likelihood estimators. Comparisons were carried out to assess the performance of these two algorithms in different scenarios. Our results show that both estimators, on average, are able to recover the true location of the single molecule in all scenarios we examined. However, in the absence of modeling inaccuracies and low noise levels, the maximum likelihood estimator is more accurate than the nonlinear least squares estimator, as measured by the standard deviations of its estimates, and attains the best possible accuracy achievable for the sets of imaging and experimental conditions that were tested. Although neither algorithm is consistently superior to the other in the presence of modeling inaccuracies or misspecifications, the maximum likelihood algorithm emerges as a robust estimator producing results with consistent accuracy across various model mismatches and misspecifications. At high noise levels, relative to the signal from the point source, neither algorithm has a clear accuracy advantage over the other. Comparisons were also carried out for two localization accuracy measures derived previously. Software packages with user-friendly graphical interfaces developed for single molecule location estimation (EstimationTool) and limit of the localization accuracy calculations (FandPLimitTool) are also discussed.

1094-4087
23352-23373
Abraham, Anish V.
4f71ee5b-b0b1-4e0e-8f64-5cea9ae8f0e0
Ram, Sripad
559bd560-3817-4e53-8c7a-2f08e4518412
Chao, Jerry
550e20b0-8365-42e3-a6fc-1048eb8c2e47
Ward, E. S.
b31c0877-8abe-485f-b800-244a9d3cd6cc
Ober, Raimund J.
31f4d47f-fb49-44f5-8ff6-87fc4aff3d36
Abraham, Anish V.
4f71ee5b-b0b1-4e0e-8f64-5cea9ae8f0e0
Ram, Sripad
559bd560-3817-4e53-8c7a-2f08e4518412
Chao, Jerry
550e20b0-8365-42e3-a6fc-1048eb8c2e47
Ward, E. S.
b31c0877-8abe-485f-b800-244a9d3cd6cc
Ober, Raimund J.
31f4d47f-fb49-44f5-8ff6-87fc4aff3d36

Abraham, Anish V., Ram, Sripad, Chao, Jerry, Ward, E. S. and Ober, Raimund J. (2009) Quantitative study of single molecule location estimation techniques. Optics Express, 17 (26), 23352-23373. (doi:10.1364/OE.17.023352).

Record type: Article

Abstract

Estimating the location of single molecules from microscopy images is a key step in many quantitative single molecule data analysis techniques. Different algorithms have been advocated for the fitting of single molecule data, particularly the nonlinear least squares and maximum likelihood estimators. Comparisons were carried out to assess the performance of these two algorithms in different scenarios. Our results show that both estimators, on average, are able to recover the true location of the single molecule in all scenarios we examined. However, in the absence of modeling inaccuracies and low noise levels, the maximum likelihood estimator is more accurate than the nonlinear least squares estimator, as measured by the standard deviations of its estimates, and attains the best possible accuracy achievable for the sets of imaging and experimental conditions that were tested. Although neither algorithm is consistently superior to the other in the presence of modeling inaccuracies or misspecifications, the maximum likelihood algorithm emerges as a robust estimator producing results with consistent accuracy across various model mismatches and misspecifications. At high noise levels, relative to the signal from the point source, neither algorithm has a clear accuracy advantage over the other. Comparisons were also carried out for two localization accuracy measures derived previously. Software packages with user-friendly graphical interfaces developed for single molecule location estimation (EstimationTool) and limit of the localization accuracy calculations (FandPLimitTool) are also discussed.

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More information

Accepted/In Press date: 2 December 2009
e-pub ahead of print date: 7 December 2009
Published date: 21 December 2009

Identifiers

Local EPrints ID: 423607
URI: https://eprints.soton.ac.uk/id/eprint/423607
ISSN: 1094-4087
PURE UUID: 40ba26cf-d4dd-4d9f-9657-eef8bdb02bc7
ORCID for E. S. Ward: ORCID iD orcid.org/0000-0003-3232-7238
ORCID for Raimund J. Ober: ORCID iD orcid.org/0000-0002-1290-7430

Catalogue record

Date deposited: 27 Sep 2018 16:30
Last modified: 24 Sep 2019 00:22

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Contributors

Author: Anish V. Abraham
Author: Sripad Ram
Author: Jerry Chao
Author: E. S. Ward ORCID iD
Author: Raimund J. Ober ORCID iD

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