Robust background modelling in DIALS
Robust background modelling in DIALS
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DOI：
10.1107/s1600576716013595
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发表时间：
2016-12-01
影响因子：
6.1
通讯作者：
Evans, Gwyndaf
中科院分区：
文献类型：
--
作者：
Parkhurst, James M.;Winter, Graeme;Evans, Gwyndaf
提出了一种在积分过程中估计每个反射下的背景的方法，该方法在存在像素离群值的情况下是鲁棒的。该方法使用广义线性模型的方法，这是更适合使用泊松分布的数据比传统的方法，像素离群值处理集成程序。该算法最适用于具有非常低背景水平的数据，其中正态分布的假设不再有效作为泊松分布的近似。结果表明，传统的方法可能会导致系统低估的背景值。然后，这导致反射强度被高估，并且引起数据集中反射强度的总体分布的变化，使得似乎记录了太少的弱反射。数据简化期间进行的统计测试可能会错误地将其归因于晶体中的缺面体孪生。应用强大的广义线性模型算法，以纠正这种偏见。
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algorithm is most applicable to data with a very low background level where assumptions of a normal distribution are no longer valid as an approximation to the Poisson distribution.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.