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Robust background modelling in DIALS

A method for estimating the background under each reflection during integration that is robust in the presence of pixel outliers is presented. The method uses a generalized linear model approach that is more appropriate for use with Poisson distributed data than traditional approaches to pixel outli...

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Detalles Bibliográficos
Autores principales: Parkhurst, James M., Winter, Graeme, Waterman, David G., Fuentes-Montero, Luis, Gildea, Richard J., Murshudov, Garib N., Evans, Gwyndaf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5139990/
https://www.ncbi.nlm.nih.gov/pubmed/27980508
http://dx.doi.org/10.1107/S1600576716013595
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author Parkhurst, James M.
Winter, Graeme
Waterman, David G.
Fuentes-Montero, Luis
Gildea, Richard J.
Murshudov, Garib N.
Evans, Gwyndaf
author_facet Parkhurst, James M.
Winter, Graeme
Waterman, David G.
Fuentes-Montero, Luis
Gildea, Richard J.
Murshudov, Garib N.
Evans, Gwyndaf
author_sort Parkhurst, James M.
collection PubMed
description A method for estimating the background under each reflection during integration that is robust in the presence of pixel outliers is presented. The method uses a generalized linear model approach that is more appropriate for use with Poisson distributed data than traditional approaches to pixel outlier handling in integration programs. The algorithm is most applicable to data with a very low background level where assumptions of a normal distribution are no longer valid as an approximation to the Poisson distribution. It is shown that traditional methods can result in the systematic underestimation of background values. This then results in the reflection intensities being overestimated and gives rise to a change in the overall distribution of reflection intensities in a dataset such that too few weak reflections appear to be recorded. Statistical tests performed during data reduction may mistakenly attribute this to merohedral twinning in the crystal. Application of the robust generalized linear model algorithm is shown to correct for this bias.
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spelling pubmed-51399902016-12-15 Robust background modelling in DIALS Parkhurst, James M. Winter, Graeme Waterman, David G. Fuentes-Montero, Luis Gildea, Richard J. Murshudov, Garib N. Evans, Gwyndaf J Appl Crystallogr Research Papers A method for estimating the background under each reflection during integration that is robust in the presence of pixel outliers is presented. The method uses a generalized linear model approach that is more appropriate for use with Poisson distributed data than traditional approaches to pixel outlier handling in integration programs. The algorithm is most applicable to data with a very low background level where assumptions of a normal distribution are no longer valid as an approximation to the Poisson distribution. It is shown that traditional methods can result in the systematic underestimation of background values. This then results in the reflection intensities being overestimated and gives rise to a change in the overall distribution of reflection intensities in a dataset such that too few weak reflections appear to be recorded. Statistical tests performed during data reduction may mistakenly attribute this to merohedral twinning in the crystal. Application of the robust generalized linear model algorithm is shown to correct for this bias. International Union of Crystallography 2016-10-21 /pmc/articles/PMC5139990/ /pubmed/27980508 http://dx.doi.org/10.1107/S1600576716013595 Text en © James M. Parkhurst et al. 2016 http://creativecommons.org/licenses/by/2.0/uk/ This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.
spellingShingle Research Papers
Parkhurst, James M.
Winter, Graeme
Waterman, David G.
Fuentes-Montero, Luis
Gildea, Richard J.
Murshudov, Garib N.
Evans, Gwyndaf
Robust background modelling in DIALS
title Robust background modelling in DIALS
title_full Robust background modelling in DIALS
title_fullStr Robust background modelling in DIALS
title_full_unstemmed Robust background modelling in DIALS
title_short Robust background modelling in DIALS
title_sort robust background modelling in dials
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5139990/
https://www.ncbi.nlm.nih.gov/pubmed/27980508
http://dx.doi.org/10.1107/S1600576716013595
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