A Python package based on robust statistical analysis for serial crystallography data processing
- Author(s)
- Hadian-Jazi, M; Sadri, A;
- Details
- Publication Year 2023-09-01,Volume 79,Issue #Pt 9,Page 820-829
- Journal Title
- Acta Crystallographica D Structural Biology
- Abstract
- The term robustness in statistics refers to methods that are generally insensitive to deviations from model assumptions. In other words, robust methods are able to preserve their accuracy even when the data do not perfectly fit the statistical models. Robust statistical analyses are particularly effective when analysing mixtures of probability distributions. Therefore, these methods enable the discretization of X-ray serial crystallography data into two probability distributions: a group comprising true data points (for example the background intensities) and another group comprising outliers (for example Bragg peaks or bad pixels on an X-ray detector). These characteristics of robust statistical analysis are beneficial for the ever-increasing volume of serial crystallography (SX) data sets produced at synchrotron and X-ray free-electron laser (XFEL) sources. The key advantage of the use of robust statistics for some applications in SX data analysis is that it requires minimal parameter tuning because of its insensitivity to the input parameters. In this paper, a software package called Robust Gaussian Fitting library (RGFlib) is introduced that is based on the concept of robust statistics. Two methods are presented based on the concept of robust statistics and RGFlib for two SX data-analysis tasks: (i) a robust peak-finding algorithm and (ii) an automated robust method to detect bad pixels on X-ray pixel detectors.
- Publisher
- IUCr Journals
- Keywords
- Crystallography, X-Ray; *Synchrotrons; *Algorithms; Lasers; RGFlib; robust bad pixel mask making; robust peak-finding; robust statistics; serial crystallography
- Research Division(s)
- Advanced Technology And Biology
- PubMed ID
- 37584428
- Publisher's Version
- https://doi.org/10.1107/s2059798323005855
- Open Access at Publisher's Site
https://doi.org/10.1107/S2059798323005855- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
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Creation Date: 2023-09-21 11:41:14
Last Modified: 2023-09-21 12:05:16