Single-cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene marker panels for such populations remains a challenge. Researchers at the Dana-Farber Cancer Institute have developed COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single-cell RNA-seq data. They show that COMET outperforms other methods for the identification of single-gene panels and enables, for the first time, prediction of multi-gene marker panels ranked by relevance. Staining by flow cytometry assay confirmed the accuracy of COMET’s predictions in identifying marker panels for cellular subtypes, at both the single- and multi-gene levels, validating COMET’s applicability and accuracy in predicting favorable marker panels from transcriptomic input. COMET is a general non-parametric statistical framework and can be used as-is on various high-throughput datasets in addition to single-cell RNA-sequencing data.
The COMET framework objective and output
Following the identification of a cell population of interest from single-cell high-throughput data (e.g. single-cell RNA-seq), COMET provides a ranking of favorable single- and multi-gene marker panels along with useful statistics and visualizations. The identification of marker panels for a population of interest is important to conduct followup functional studies such as isolation, visualization and perturbation of the population.
Delaney C, Schnell A, Cammarata LV, Yao-Smith A, Regev A, Kuchroo VK, Singer M. (2019) Combinatorial prediction of marker panels from single-cell transcriptomic data. Mol Syst Biol 15(10):e9005. [article]