Clustering on localization data allows researchers to find biological structures and quantify their morphological properties. During this webinar, ONI's data scientist Haraman Johal covers the basics of localization data clustering, including its strengths and limitations and why clustering is important in super-resolution imaging.

Haraman also provides practical examples of successfully applying and optimizing clustering on single molecule localization microscopy datasets, such as immune cell receptors, extracellular vesicles, or tumor tissue.
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