Single-cell RNA sequencing is a powerful tool for studying cellular diversity, for example in cancer where varied tumor cell types determine diagnosis, prognosis and response to therapy. Single-cell technologies generate hundreds to thousands of data points per sample, generating a need for new methods to define cell populations across different single-cell landscapes.
Qi Liu, Ph.D., Ken Lau, Ph.D., and colleagues have developed a new tool, sc-UniFrac, to quantify diverse cell types in single-cell studies. The tool compares hierarchical trees that represent single-cell landscapes and allows cells that drive differences to be identified as unbalanced branches on the trees.
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