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, PhD, Ken Lau, PhD, and colleagues at Vanderbilt University 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.
Reporting in PLOS Biology, the investigators demonstrated the utility of sc-UniFrac in multiple applications, including regional specification of brain cells and identification of altered cells in tumor samples. The authors expect that sc-UniFrac will facilitate single-cell studies, in particular studies aimed at tracking how tumor cell populations evolve during disease progression and respond to drug treatments.
Overview of the sc-UniFrac method
(A) A hierarchical tree is built by clustering the combined single-cell transcriptome profiles from two samples and by calculating distances between cluster centroids. Each cell, as a function of their cluster membership, is then assigned to branches. Branch lengths weighted by the relative abundance of each sample are used to calculate the sc-UniFrac distance. In the second step, the sample labels of all cells are swapped without altering the tree topology to generate a null distribution of sc-UniFrac distances, where a p-value for the sc-UniFrac distance can be calculated. (B) Workflow overview of the sc-UniFrac package for characterizing dissimilarities between two samples.
Availability – sc-Unifrac is freely available as an R package at https://github.com/liuqivandy/scUnifrac.
Source – Vanderbilt University