clustifyr: an R package for automated single-cell RNA sequencing cluster classification

Fu, Rui; Gillen, Austin E.; Sheridan, Ryan M.; Tian, Chengzhe; Daya, Michelle; Hao, Yue; Hesselberth, Jay R.; Riemondy, Kent A.

doi:10.12688/f1000research.22969.2

Cited by 136Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Phylogenetic Studies

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clustifyr: an R package for automated single-cell RNA sequencing cluster classification

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Fu, Rui, Gillen, Austin E., Sheridan, Ryan M., Tian, Chengzhe, Daya, Michelle, Hao, Yue, Hesselberth, Jay R., Riemondy, Kent A.·2020·F1000Research

DOI 10.12688/f1000research.22969.2PMC PMC7383722

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Abstract

ABSTRACT Background In single-cell RNA sequencing (scRNA-seq) analysis, assignment of likely cell types remains a time-consuming, error-prone, and biased process. Current packages for identity assignment use limited types of reference data, and often have rigid data structure requirements. As such, a more flexible tool, capable of handling multiple types of reference data and data structures, would be beneficial. Findings To address difficulties in cluster identity assignment, we developed the clustifyr R package. The package leverages external datasets, including gene expression profiles from scRNA-seq, bulk RNA-seq, microarray expression data, and/or signature gene lists, to assign likely cell types. We benchmark various parameters of a correlation-based approach, and also implement a variety of gene list enrichment methods. By providing tools for exploratory data analysis, we demonstrate the feasibility of a simple and effective data-driven approach for cell type assignment in scRNA-seq cell clusters. Conclusions clustifyr is a lightweight and effective cell type assignment tool developed for compatibility with various scRNA-seq analysis workflows. clustifyr is publicly available at https://github.com/rnabioco/clustifyr

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Immune clusters in human skin tumor scRNA-seq were annotated by comparison to reference PBMC data using clustifyR.
“Immune clusters were annotated by comparison to reference PBMC data, implemented in clustifyR package for R.60”
From Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy (2023) · cites this paper as [60]

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Authors: Fu, R., Gillen, A. E., Sheridan, R. M., Tian, C., Daya, M., Hao, Y., Hesselberth, J. R., & Riemondy, K. A.
Affiliations: University of Colorado Anschutz (US)
University of Colorado Boulder (US)
North Carolina State University (US)
Corresponding author: Riemondy, Kent A.
ORCID: Fu, Rui
Gillen, Austin E.
Sheridan, Ryan M.
Tian, Chengzhe
Daya, Michelle
Hao, Yue
Hesselberth, Jay R.
Riemondy, Kent A.
Venue: F1000Research
Publisher: F1000 Research Ltd
Published: 2020-07-16
Volume: 9
Pages: 223
ISSN: 2046-1402 (ISSN-L)
DOI: 10.12688/f1000research.22969.2
PMC: PMC7383722
OpenAlex: W3014185549
Cited by: 135
References: 33
Citations by year: 2026: 13 · 2025: 34 · 2024: 27 · 2023: 25 · 2022: 19 · 2021: 14 · 2020: 3
FWCI: 4.95
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (tdm)
Locations: F1000Research (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Institute of General Medical Sciences (R35GM119550)
University of Colorado School of Medicine (RNA Bioscience Initiative)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords: single-cell RNA sequencing, cell type classification, reference data, gene expression profiles, bulk RNA-seq, gene expression microarrays, R package
MeSH terms: Humans, Software, Sequence Analysis, RNA, RNA, Small Cytoplasmic, Gene Expression Profiling, Single-Cell Analysis

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