Combinatorial prediction of gene-marker panels from single-cell transcriptomic data

摘要: Abstract Single-cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various vivo contexts, but identification of succinct gene-marker panels for such remains a challenge. In this work we introduce COMET, computational framework the candidate marker consisting one or more genes interest identified single-cell RNA-seq data. We show that COMET outperforms other methods single-gene panels, and enables, first time, prediction multi-gene ranked by relevance. Staining flow-cytometry assay confirmed accuracy COMET’s predictions marker-panels cellular subtypes, at both single- levels, validating applicability predicting favorable from input. is general non-parametric statistical can be used as-is on high-throughput datasets addition to RNA-sequencing available use via web interface (http://www.cometsc.com/) standalone software package (https://github.com/MSingerLab/COMETSC).