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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of applied crystallography Ročník 49; číslo 6; s. 1912 - 1921
Hlavní autoři: Parkhurst, James M., Winter, Graeme, Waterman, David G., Fuentes-Montero, Luis, Gildea, Richard J., Murshudov, Garib N., Evans, Gwyndaf
Médium: Journal Article
Jazyk:angličtina
Vydáno: 5 Abbey Square, Chester, Cheshire CH1 2HU, England International Union of Crystallography 01.12.2016
Blackwell Publishing Ltd
Témata:
Algorithms
background modelling
Crystallography
Data reduction
Estimating
Generalized linear models
integration
Mathematical models
Modelling
Outliers (statistics)
Pixels
Reflection
Research Papers
robust outlier rejection
Robustness
generalized linear models
integration
robust outlier rejection
background modelling
ISSN:1600-5767, 0021-8898, 1600-5767
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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algorithm is most applicable to data with a very low background level where assumptions of a normal distribution are no longer valid as an approximation to the Poisson distribution.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. The application of a robust generalized linear model framework for the modelling of reflection backgrounds in X‐ray diffraction images is described.
Bibliografie:istex:E238E804CBB1269A33FC7134E15DBF67FF96F66B
ark:/67375/WNG-8BK645S4-3
ArticleID:JCR2AP5008
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1600-5767
0021-8898
1600-5767
DOI:10.1107/S1600576716013595