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Robust background modelling in DIALS

DOI: 10.1107/S1600576716013595 DOI Help

Authors: James M. Parkhurst (Diamond Light Source) , Graeme Winter (Diamond Light Source) , David G. Waterman (Diamond Light Source; STFC) , Luis Fuentes-montero (Diamond Light Source) , Richard J. Gildea (Diamond Light Source) , Garib N. Murshudov (Laboratory of Molecular Biology) , Gwyndaf Evans (Diamond Light Source)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Journal Of Applied Crystallography , VOL 49

State: Published (Approved)
Published: December 2016

Open Access Open Access

Abstract: 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algorithm is most applicable to data with a very low background level where assumptions of a normal distribution are no longer valid as an approximation to the Poisson distribution.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.

Journal Keywords: integration; robust outlier rejection; generalized linear models; background modelling

Subject Areas: Technique Development, Physics


Instruments: I02-Macromolecular Crystallography , I03-Macromolecular Crystallography , I04-Macromolecular Crystallography

Documents:
ap5008.pdf

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