Quantitative assessment of cell population diversity in single-cell landscapes

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution.Author summary: Single-cell technologies generate hundreds to thousands of data points per sample, presenting a conundrum in determining similarities and differences across multiple samples. Currently, similarity is determined by the degree of “intermixing” of data points among samples, a local approach, but this approach cannot accurately evaluate the similarity of samples with cell populations close in data space but not overlapping. We present sc-UniFrac, an approach to compare hierarchical trees that represent single-cell landscapes, taking both global and local similarities into account. Furthermore, sc-UniFrac allows cells that drive differences between samples to be easily identified as unbalanced branches on trees. We used sc-UniFrac to evaluate experimental design based on biological and technical replicates, regional specification of brain cells, degree and identity of stromal infiltrate into tumor, and computational batch-correction tools. sc-UniFrac will be an important analysis tool going forward as the cost of single-cell technologies drops and more studies adopt multi-sample experimental designs.