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Selecting representative samples from complex biological datasets using k-medoids clustering

Name: Selecting representative samples from complex biological datasets using k-medoids clustering
Keywords: Single-cell, Sampling, k-medoids, R, Antibody candidate selection

descriptionPublicationkeyboard_double_arrow_right Article 13 Jun 2022Publisher:Zenodo

Authors: Li, Lei; Wilson, Patrick C.;

doi: 10.5281/zenodo.6639035 , 10.5281/zenodo.6639034

Selecting representative samples from complex biological datasets using k-medoids clustering

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Abstract

This method quantifies the relationships/similarities among samples using their Euclidian distances by vectorizing all given properties, and then determines an appropriate sample size by evaluating the coverage of key proprieties from multiple candidate sizes, following by a k-medoids clustering to group samples into several clusters, and selects centers from each cluster as the most representatives.

Related Organizations

University of Chicago
United States

Keywords

Single-cell, Sampling, k-medoids, R, Antibody candidate selection

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