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CFAssay: statistical analysis of the colony formation assay

BACKGROUND: Colony formation assay is the gold standard to determine cell reproductive death after treatment with ionizing radiation, applied for different cell lines or in combination with other treatment modalities. Associated linear-quadratic cell survival curves can be calculated with different...

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Autores principales: Braselmann, Herbert, Michna, Agata, Heß, Julia, Unger, Kristian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634140/
https://www.ncbi.nlm.nih.gov/pubmed/26537797
http://dx.doi.org/10.1186/s13014-015-0529-y
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author Braselmann, Herbert
Michna, Agata
Heß, Julia
Unger, Kristian
author_facet Braselmann, Herbert
Michna, Agata
Heß, Julia
Unger, Kristian
author_sort Braselmann, Herbert
collection PubMed
description BACKGROUND: Colony formation assay is the gold standard to determine cell reproductive death after treatment with ionizing radiation, applied for different cell lines or in combination with other treatment modalities. Associated linear-quadratic cell survival curves can be calculated with different methods. For easy code exchange and methodological standardisation among collaborating laboratories a software package CFAssay for R (R Core Team, R: A Language and Environment for Statistical Computing, 2014) was established to perform thorough statistical analysis of linear-quadratic cell survival curves after treatment with ionizing radiation and of two-way designs of experiments with chemical treatments only. METHODS: CFAssay offers maximum likelihood and related methods by default and the least squares or weighted least squares method can be optionally chosen. A test for comparision of cell survival curves and an ANOVA test for experimental two-way designs are provided. RESULTS: For the two presented examples estimated parameters do not differ much between maximum-likelihood and least squares. However the dispersion parameter of the quasi-likelihood method is much more sensitive for statistical variation in the data than the multiple R(2) coefficient of determination from the least squares method. CONCLUSION: The dispersion parameter for goodness of fit and different plot functions in CFAssay help to evaluate experimental data quality. As open source software interlaboratory code sharing between users is facilitated. AVAILABILITY: The package is available at http://www.bioconductor.org/packages/release/bioc/html/CFAssay.html.
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spelling pubmed-46341402015-11-06 CFAssay: statistical analysis of the colony formation assay Braselmann, Herbert Michna, Agata Heß, Julia Unger, Kristian Radiat Oncol Methodology BACKGROUND: Colony formation assay is the gold standard to determine cell reproductive death after treatment with ionizing radiation, applied for different cell lines or in combination with other treatment modalities. Associated linear-quadratic cell survival curves can be calculated with different methods. For easy code exchange and methodological standardisation among collaborating laboratories a software package CFAssay for R (R Core Team, R: A Language and Environment for Statistical Computing, 2014) was established to perform thorough statistical analysis of linear-quadratic cell survival curves after treatment with ionizing radiation and of two-way designs of experiments with chemical treatments only. METHODS: CFAssay offers maximum likelihood and related methods by default and the least squares or weighted least squares method can be optionally chosen. A test for comparision of cell survival curves and an ANOVA test for experimental two-way designs are provided. RESULTS: For the two presented examples estimated parameters do not differ much between maximum-likelihood and least squares. However the dispersion parameter of the quasi-likelihood method is much more sensitive for statistical variation in the data than the multiple R(2) coefficient of determination from the least squares method. CONCLUSION: The dispersion parameter for goodness of fit and different plot functions in CFAssay help to evaluate experimental data quality. As open source software interlaboratory code sharing between users is facilitated. AVAILABILITY: The package is available at http://www.bioconductor.org/packages/release/bioc/html/CFAssay.html. BioMed Central 2015-11-04 /pmc/articles/PMC4634140/ /pubmed/26537797 http://dx.doi.org/10.1186/s13014-015-0529-y Text en © Braselmann et al. 2015 Open Access This 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 Methodology
Braselmann, Herbert
Michna, Agata
Heß, Julia
Unger, Kristian
CFAssay: statistical analysis of the colony formation assay
title CFAssay: statistical analysis of the colony formation assay
title_full CFAssay: statistical analysis of the colony formation assay
title_fullStr CFAssay: statistical analysis of the colony formation assay
title_full_unstemmed CFAssay: statistical analysis of the colony formation assay
title_short CFAssay: statistical analysis of the colony formation assay
title_sort cfassay: statistical analysis of the colony formation assay
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634140/
https://www.ncbi.nlm.nih.gov/pubmed/26537797
http://dx.doi.org/10.1186/s13014-015-0529-y
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