Combinatorial prediction of marker panels from single‐cell transcriptomic data - Conor Delaney; Alexandra Schnell; Louis V Cammarata; Aaron Yao‐Smith; Aviv Regev; Vijay K Kuchroo; Meromit Singer (Molecular Systems Biology)
Abstract 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. In this work, we introduce 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. We 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. COMET is available for use via a web interface ( http://www.cometsc.com/ ) or a stand‐alone software package ( https://github.com/MSingerLab/COMETSC ).