clustifyr: an R package for automated single-cell RNA sequencing cluster classification
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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
- Venue
- F1000Research
- Publisher
- F1000 Research Ltd
- Published
- 2020-07-16
- Volume
- 9
- Pages
- 223
- ISSN
- 2046-1402 (ISSN-L)
- 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
- 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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