RNA Bioscience Initiative, University of Colorado School of Medicine, Aurora,, CO, 80045, USA.
Department of Biochemistry, University of Colorado Boulder, Boulder, CO, 80303, USA.
F1000Res. 2020 Apr 1;9:223. doi: 10.12688/f1000research.22969.2. eCollection 2020.
Assignment of cell types from single-cell RNA sequencing (scRNA-seq) data remains a time-consuming and error-prone process. Current packages for identity assignment use limited types of reference data and often have rigid data structure requirements. We developed the clustifyr R package to leverage several external data types, including gene expression profiles to assign likely cell types using data from scRNA-seq, bulk RNA-seq, microarray expression data, or signature gene lists. We benchmark various parameters of a correlation-based approach and implement gene list enrichment methods. 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.
从单细胞 RNA 测序 (scRNA-seq) 数据中分配细胞类型仍然是一个耗时且容易出错的过程。当前用于身份分配的软件包仅使用有限类型的参考数据,并且通常具有严格的数据结构要求。我们开发了 clustifyr R 包,以利用多种外部数据类型,包括基因表达谱,使用来自 scRNA-seq、批量 RNA-seq、微阵列表达数据或特征基因列表的数据来分配可能的细胞类型。我们对基于相关性方法的各种参数进行基准测试,并实现了基因列表富集方法。clustifyr 是一种轻量级且有效的细胞类型分配工具,专为与各种 scRNA-seq 分析工作流程兼容而开发。clustifyr 可在 https://github.com/rnabioco/clustifyr 上获得。