Detecting outliers in non-redundant diffraction data.

By Randy J. Read. 1999

Outliers are observations which are very unlikely to be correct, as judged by independent observations or other prior information. Such unexpected observations are treated, effectively, as being more informative about possible models, so they can seriously impede the course of structure determination and refinement. The best way to detect and eliminate outliers is to collect highly redundant data, but it is not always possible to make multiple measurements of every reflection. For non-redundant data, the prior expectation given either by a Wilson distribution of intensities or model-based structure-factor probability distributions can be used to detect outliers. This captures mostly the excessively strong reflections, which dominate the features of electron-density maps or, even more so, Patterson maps. The outlier rejection tests have been implemented in a program, Outliar.

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Citations in the corpus

Towards automated crystallographic structure refinement with phenix.refine (2012, background): “A preliminary crystallographic data analysis is performed in order to detect and ignore potential reflection outliers (Read, 1999).”
Using SAD data in Phaser (2011, positive): “Firstly, the F + and F À measurements are both checked for implausibly large values using a test based on the Wilson distribution (Read, 1999).”
A New Generation of Crystallographic Validation Tools for the Protein Data Bank (2011, background): “Individual potential outliers in the experimental data can be identiﬁed by analysis of normalized intensities (Read, 1999).”