15Background: In single-cell RNA sequencing (scRNA-seq) analysis, assignment of likely cell 16 types remains a time-consuming, error-prone, and biased process. Current packages for identity 17 assignment use limited types of reference data, and often have rigid data structure 18requirements. As such, a more flexible tool, capable of handling multiple types of reference data 19and data structures, would be beneficial. 20 21Findings: To address difficulties in cluster identity assignment, we developed the clustifyr R 22package. The package leverages external datasets, including gene expression profiles from 23 scRNA-seq, bulk RNA-seq, microarray expression data, and/or signature gene lists, to assign 24 likely cell types. We benchmark various parameters of a correlation-based approach, and also 25 implement a variety of gene list enrichment methods. By providing tools for exploratory data 26 analysis, we demonstrate the feasibility of a simple and effective data-driven approach for cell 27 type assignment in scRNA-seq cell clusters. 28 29 Conclusions: clustifyr is a lightweight and effective cell type assignment tool developed for 30 compatibility with various scRNA-seq analysis workflows. clustifyr is publicly available at 31 https://github.com/rnabioco/clustifyr 32 KEYWORDS 33 Single-cell RNA sequencing, cell type classification, gene expression profile, R package 34 35