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Robust dose-response curve estimation applied to high content screening data analysis
BACKGROUND AND METHOD: Successfully automated sigmoidal curve fitting is highly challenging when applied to large data sets. In this paper, we describe a robust algorithm for fitting sigmoid dose-response curves by estimating four parameters (floor, window, shift, and slope), together with the detec...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279979/ https://www.ncbi.nlm.nih.gov/pubmed/25614758 http://dx.doi.org/10.1186/s13029-014-0027-x |
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author | Nguyen, Thuy Tuong Song, Kyungmin Tsoy, Yury Kim, Jin Yeop Kwon, Yong-Jun Kang, Myungjoo Edberg Hansen, Michael Adsetts |
author_facet | Nguyen, Thuy Tuong Song, Kyungmin Tsoy, Yury Kim, Jin Yeop Kwon, Yong-Jun Kang, Myungjoo Edberg Hansen, Michael Adsetts |
author_sort | Nguyen, Thuy Tuong |
collection | PubMed |
description | BACKGROUND AND METHOD: Successfully automated sigmoidal curve fitting is highly challenging when applied to large data sets. In this paper, we describe a robust algorithm for fitting sigmoid dose-response curves by estimating four parameters (floor, window, shift, and slope), together with the detection of outliers. We propose two improvements over current methods for curve fitting. The first one is the detection of outliers which is performed during the initialization step with correspondent adjustments of the derivative and error estimation functions. The second aspect is the enhancement of the weighting quality of data points using mean calculation in Tukey’s biweight function. RESULTS AND CONCLUSION: Automatic curve fitting of 19,236 dose-response experiments shows that our proposed method outperforms the current fitting methods provided by MATLAB®;’s nlinfit function and GraphPad’s Prism software. |
format | Online Article Text |
id | pubmed-4279979 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42799792015-01-22 Robust dose-response curve estimation applied to high content screening data analysis Nguyen, Thuy Tuong Song, Kyungmin Tsoy, Yury Kim, Jin Yeop Kwon, Yong-Jun Kang, Myungjoo Edberg Hansen, Michael Adsetts Source Code Biol Med Research BACKGROUND AND METHOD: Successfully automated sigmoidal curve fitting is highly challenging when applied to large data sets. In this paper, we describe a robust algorithm for fitting sigmoid dose-response curves by estimating four parameters (floor, window, shift, and slope), together with the detection of outliers. We propose two improvements over current methods for curve fitting. The first one is the detection of outliers which is performed during the initialization step with correspondent adjustments of the derivative and error estimation functions. The second aspect is the enhancement of the weighting quality of data points using mean calculation in Tukey’s biweight function. RESULTS AND CONCLUSION: Automatic curve fitting of 19,236 dose-response experiments shows that our proposed method outperforms the current fitting methods provided by MATLAB®;’s nlinfit function and GraphPad’s Prism software. BioMed Central 2014-12-10 /pmc/articles/PMC4279979/ /pubmed/25614758 http://dx.doi.org/10.1186/s13029-014-0027-x Text en © Nguyen et al.; licensee BioMed Central. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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 | Research Nguyen, Thuy Tuong Song, Kyungmin Tsoy, Yury Kim, Jin Yeop Kwon, Yong-Jun Kang, Myungjoo Edberg Hansen, Michael Adsetts Robust dose-response curve estimation applied to high content screening data analysis |
title | Robust dose-response curve estimation applied to high content screening data analysis |
title_full | Robust dose-response curve estimation applied to high content screening data analysis |
title_fullStr | Robust dose-response curve estimation applied to high content screening data analysis |
title_full_unstemmed | Robust dose-response curve estimation applied to high content screening data analysis |
title_short | Robust dose-response curve estimation applied to high content screening data analysis |
title_sort | robust dose-response curve estimation applied to high content screening data analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279979/ https://www.ncbi.nlm.nih.gov/pubmed/25614758 http://dx.doi.org/10.1186/s13029-014-0027-x |
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