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Detecting outliers in non-redundant diffraction data.

Authors
  • Read, R J
Type
Published Article
Journal
Acta crystallographica. Section D, Biological crystallography
Publication Date
Oct 01, 1999
Volume
55
Issue
Pt 10
Pages
1759–1764
Identifiers
PMID: 10531526
Source
Medline
License
Unknown

Abstract

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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