{"doi":"10.1101/655753","title":"Combinatorial prediction of marker panels from single-cell transcriptomic data","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 standalone software package ( https://github.com/MSingerlab/COMETSC ).","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2019,"id":12420,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0462,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2019-05-30","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":14737,"name":"Alexandra Schnell","orcid":"0000-0003-3442-7750","position":1,"is_corresponding":false},{"id":72640,"name":"Louis V. Cammarata","orcid":null,"position":2,"is_corresponding":false},{"id":72641,"name":"Aaron Yao‐Smith","orcid":null,"position":3,"is_corresponding":false},{"id":29633,"name":"Prisca Liberali","orcid":"0000-0003-0695-6081","position":4,"is_corresponding":false},{"id":6940,"name":"Vijay K. Kuchroo","orcid":"0000-0001-7177-2110","position":5,"is_corresponding":false},{"id":34472,"name":"Meromit Singer","orcid":"0000-0002-1755-3598","position":6,"is_corresponding":false},{"id":50397,"name":"Conor P. Delaney","orcid":"0000-0002-8068-5569","position":7,"is_corresponding":false},{"id":50395,"name":"Conor Delaney","orcid":null,"position":0,"is_corresponding":true}],"reference_count":46,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}