PPubMed16 Aug 2023Cited 3×

A Python package based on robust statistical analysis for serial crystallography data processing <i> RGFlib </i>

Marjan Hadian-Jazi, Alireza Sadri, D. G. Waterman

Abstract

This article introduces RGFlib, a Python package for robust statistical analysis. The package is a useful tool for a variety of tasks in X-ray crystallography data analysis, such as peak-finding, bad pixel mask making and other outlier-detection tasks.

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The package is a useful tool for a variety of tasks in X-ray crystallography data analysis, such as peak-finding, bad pixel mask making and other outlier-detection tasks.

What’s new

i.Introduces RGFlib, a Python package for robust statistical analysis in X-ray crystallography.
ii.Implements fast least k-th order statistics (FLkOS) and modified selective statistical estimator (MSSE) for outlier detection.
iii.Provides functions for robust fitting of values, lines, planes, and background estimation.
iv.Applies robust methods to serial crystallography hit-finding and bad pixel mask creation (RPF and RMM).

How they did it

The authors developed RGFlib by implementing modern robust statistical and clustering algorithms (FLkOS, MSSE) in Python with C wrappers for speed. Functions were created for robust model fitting (fitValue, fitLine, fitPlane, fitBackground) and demonstrated on simulated and real serial crystallography data sets.

What they found

Default likely‑ratio parameter is set to 0.5 %.
Default certain‑ratio parameter is set to 0.3.
MSSE_LAMBDA default threshold is 3; minimum true data points k default is 12.
RPF uses an SNR threshold of 6 for peak labeling.
RMM uses a default window size of 32 × 32 pixels for background modeling.

Caveats

✕FLkOS requires a rough estimate of the true data structure size, which may be unknown.
✕Accurate outlier detection depends on user‑provided parameters such as likely‑ratio and thresholds.
✕Robust methods may be computationally intensive, requiring parallel processing for large data sets.

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