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Single-cell RNA sequencing (scRNA-seq) is now a commonly used technique to measure the transcriptome of populations of cells. Clustering heterogeneous cells based on these transcriptomes enables identification of cell populations (Butler, Hoffman, Smibert, Papalexi, &amp; Satija, 2018; Trapnell et al., 2014). There are multiple methods available to identify "marker" genes that differ between these populations (Butler et al., 2018; Love, Huber, &amp; Anders, 2014; Robinson, McCarthy, &amp; Smyth, 2009). However, there are usually too many genes in these lists to directly suggest an experimental follow-up strategy for selecting them from a bulk population (e.g. via FACS (Tung et al., 2007)). Here we present scTree, a tool that aims to provide biologists using the R programming language and scRNA-seq analysis programs a minimal set of genes that can be used in downstream experiments. The package is free, open source and available though GitHub at github.com/jspaezp/sctree.

scTree: An R package to generate antibody-compatible classifiers from single-cell sequencing data