BACKGROUND Single cell RNA sequencing (scRNAseq) has provided invaluable insights into cellular heterogeneity and functional states in health and disease. During the analysis of scRNAseq data, annotating the biological identity of cell clusters is an important step before downstream analyses and it remains technically challenging. The current solutions for annotating single cell clusters generally lack a graphical user interface, can be computationally intensive or have a limited scope. On the other hand, manually annotating single cell clusters by examining the expression of marker genes can be subjective and labor-intensive. To improve the quality and efficiency of annotating cell clusters in scRNAseq data, we present a web-based R/Shiny app and R package, Cluster Identity PRedictor (CIPR), which provides a graphical user interface to quickly score gene expression profiles of unknown cell clusters against mouse or human references, or a custom dataset provided by the user. CIPR can be easily integrated into the current pipelines to facilitate scRNAseq data analysis. RESULTS CIPR employs multiple approaches for calculating the identity score at the cluster level and can accept inputs generated by popular scRNAseq analysis software. CIPR provides 2 mouse and 5 human reference datasets, and its pipeline allows inter-species comparisons and the ability to upload a custom reference dataset for specialized studies. The option to filter out lowly variable genes and to exclude irrelevant reference cell subsets from the analysis can improve the discriminatory power of CIPR suggesting that it can be tailored to different experimental contexts. Benchmarking CIPR against existing functionally similar software revealed that our algorithm is less computationally demanding, it performs significantly faster and provides accurate predictions for multiple cell clusters in a scRNAseq experiment involving tumor-infiltrating immune cells. CONCLUSIONS CIPR facilitates scRNAseq data analysis by annotating unknown cell clusters in an objective and efficient manner. Platform independence owing to Shiny framework and the requirement for a minimal programming experience allows this software to be used by researchers from different backgrounds. CIPR can accurately predict the identity of a variety of cell clusters and can be used in various experimental contexts across a broad spectrum of research areas.

中文翻译：

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CIPR：基于Web的R / shiny应用程序和R包，用于在单细胞RNA测序实验中注释细胞簇。

背景技术单细胞RNA测序（scRNAseq）已经为健康和疾病中的细胞异质性和功能状态提供了宝贵的见解。在scRNAseq数据分析过程中，注释细胞簇的生物学特性是下游分析之前的重要步骤，并且在技术上仍然具有挑战性。用于注释单个小区群集的当前解决方案通常缺少图形用户界面，可能需要大量计算或具有有限的范围。另一方面，通过检查标记基因的表达手动注释单个细胞簇可能是主观的且需要大量劳动。为了提高在scRNAseq数据中注释细胞簇的质量和效率，我们提供了一个基于Web的R / Shiny应用程序和R程序包，Cluster Identity PRedictor（CIPR），它提供了图形用户界面，可针对鼠标或人类参考或用户提供的自定义数据集快速对未知细胞簇的基因表达谱进行评分。CIPR可以轻松集成到当前管道中，以促进scRNAseq数据分析。结果CIPR采用多种方法来计算簇级别的身份得分，并且可以接受流行的scRNAseq分析软件生成的输入。CIPR提供了2个鼠标和5个人类参考数据集，其流水线可以进行种间比较，并可以上传自定义参考数据集以进行专门研究。筛选出低可变基因并从分析中排除无关的参考细胞亚群的选择可以提高CIPR的鉴别能力，表明它可以针对不同的实验环境进行定制。针对现有功能相似的软件对CIPR进行基准测试后发现，在涉及肿瘤浸润免疫细胞的scRNAseq实验中，我们的算法对计算的要求较低，执行速度显着提高，并且可以为多个细胞簇提供准确的预测。结论CIPR通过以客观有效的方式注释未知细胞簇来促进scRNAseq数据分析。由于Shiny框架的平台独立性以及对最低编程经验的要求，使得该软件可供来自不同背景的研究人员使用。