Hierarchical clustering for multiple-crystal macromolecular crystallography experiments: the ccCluster program

Abstract

This article describes ccCluster, a software providing an intuitive graphical user interface (GUI) and multiple functions to perform hierarchical cluster analysis on multiple crystallographic datasets. The program makes it easier for users to choose, in the case of multi-crystal data collection, those datasets that will be merged together to give good final statistics. It provides a simple GUI to analyse the dendrogram and various options for automated clustering and data merging.

Automated data collection and real-time data analysis suite for serial synchrotron crystallography

“…pCC-Selection scaling. In parallel to unit-cell clustering, another hierarchical clustering is performed based on pairwise cross correlation (pCC) values [described by Giordano et al (2012) and Santoni et al (2017)]. Correlations between symmetry-allowed common reflections from any two datasets are calculated with the cctbx library flex module.…”

“…Decoupling these two types of errors and discarding datasets with large systematic errors (non-isomorphous) is not trivial. Various concepts have been developed for crystallographic data selection, scaling and merging such as hierarchical cluster analysis (HCA) based on cross-correlation among datasets (Giordano et al, 2012;Santoni et al, 2017), unit-cell-based hierarchical clustering (BLEND; Foadi et al, 2013), genetic algorithm (CODGAS; Zander et al, 2016) and xscale_ isocluster (Diederichs, 2017). All these concepts have been successfully demonstrated on various cases to provide complete and high-quality datasets by careful data selection and merging.…”

High-speed raster-scanning synchrotron serial microcrystallography with a high-precision piezo-scanner

“…The result was evaluated by POINTLESS to determine the best-fitted space group and the data merged again by XSCALE utilizing the given space group. The SciPy functions Linkage and Dendrogram (Jones et al, 2001) were utilized to perform the hierarchical clustering of the correlation coefficients from XSCALE, using the distance definition described by Giordano et al (2012) and Santoni et al (2017). The resultant dendrogram was plotted via Python matplotlib (Hunter, 2007) for further outlier removal (Fig.…”

“…unit-cell clustering followed by correlation coefficient clustering, was found to be necessary as the correlation coefficient clustering was not able to completely distinguish the difference in unit cells. Compared with other unit-cell clustering methods that use the edge lengths (Santoni et al, 2017) or face diagonal lengths (Giordano et al, 2012), this coordinate transformation method considers both the length and orientation of the volume diagonal vector, and worked very well for the datasets presented.…”

VMXi: a fully automated, fully remote, high-flux in situ macromolecular crystallography beamline

“…Crystal radiation damage can be outrun due to the increased dose-rate Schubert et al, 2016;Chapman et al, 2014) making it possible to collect small wedges of data at room temperature. Data collection and data processing tools have evolved around these new developments and it is now standard practice using in situ data collection to be able to obtain full structural information from partial datasets on multiple samples (Foadi et al, 2013;Zander et al, 2015;Santoni et al, 2017;Stellato et al, 2014). It is a huge step forward for many challenging projects to be able to analyse samples in situ, bypassing the additional sample manipulation associated with cryo-cooling methods.…”