blog on microed preprint

_posts/2024-08-22-small-molecule-microed.md

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+---
+layout: post
+title: Merging Small Molecule MicroED Data
+subtitle: Preprint Demonstrates using Careless in Small Molecule MicroED
+author: Doris Mai
+usemathjax: true
+---
+
+### Small Molecule MicroED
+<!-- For decades, single-crystal X-ray diffraction (SCXRD) has been the gold standard for structure elucidation for small molecules.  -->
+Microcrystal electron diffraction (MicroED), also known as continuous rotational electron diffraction (cRED), or 3-dimensional electron diffraction (3DED), is an emerging technique for elucidating small molecule structures from micro- and nano-crystals. 
+However, data processing in microED can be challenging and often requires **merging datasets from multiple crystals** due to limited rotation ranges in many transmission electron microscopes (TEMs) and higher noise in intensity measurements than conventional X-ray diffraction (XRD) experiments. 
+A common practice in the field is to manually curate datasets and apply scaling programs such as [XDS/XSCALE](https://doi.org/10.1107/S0907444909047337) from rotational XRD, but this can be time-consuming and risks introducing human bias. In this [preprint](https://doi.org/10.26434/chemrxiv-2024-62bmk), we demonstrate that [Careless](https://github.com/rs-station/careless) can be used for merging small molecule microED data and investigate the impact of dataset curation.
+
+### Dataset Curation in Merging
+We benchmark Careless with XDS/XSCALE on more than 10 cases of multi-crystal merging and compare the performance between using all datasets and manually curated datasets (curated=True). 
+We also explore an extension to Careless ([MC-Careless](https://github.com/DorisMai/careless/tree/multi_xtal_sig)) to automate the curation of datasets. Specifically, an optimal weighting among datasets ($$w$$) is learned jointly with the scaling factors ($$K$$) and structure factor amplitudes ($$F$$). 
+This weight modulates the effective uncertainty of the intensities ($$\sigma_I$$) from different crystals to account for the variability of data quality across datasets.
+
+![Schematic of weighting](/assets/posts/2024-08-22-small-molecule-microed/mc-careless-schematic.png){: .blog-image-wide} 
+Schematic from the [preprint](https://doi.org/10.26434/chemrxiv-2024-62bmk) of the multi-crystal weighting extension to Careless. [(CC-BY-NC-ND license)](https://creativecommons.org/licenses/by-nc-nd/4.0/)
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+
+In the presented cases, Careless benefits from using all available data, and our multi-crystal weighting extension did not further improve the performance. Manual curation of datasets affects the $$CC_{1/2}$$ in XDS/XSCALE and Careless but has marginal impact on the accuracy with respect to structure factor amplitudes calculated from reference structures and on *ab initio* phasing outcomes. 
+
+![Initial map](/assets/posts/2024-08-22-small-molecule-microed/preliminary-maps.png){: .blog-image-wide} 
+Examples from the [preprint](https://doi.org/10.26434/chemrxiv-2024-62bmk) of *ab initio* phasing outcomes from different merging protocols. [(CC-BY-NC-ND license)](https://creativecommons.org/licenses/by-nc-nd/4.0/)
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+
+### Key points and Future Directions
+- Careless can merge microED and small molecule crystallography data.
+- The variational inference framework implemented in Careless is flexible for methods development as demonstrated in our [MC-Careless](https://github.com/DorisMai/careless/tree/multi_xtal_sig).
+- Additional exploration of the hyperparamter space and the Wilson prior might be helpful for challeging cases with pathologies.
+- The common practice of manual dataset curation should be cautioned and deserves future investigation.