Faculty Opinions recommendation of Combinatorial prediction of marker panels from single-cell transcriptomic data.
Year: 2019
Type: peer-review
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 singlecell 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).
Author: Alejandro Schaffer
Institutions: National Institutes of Health, National Cancer Institute
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4237187901