DataSheet_1_sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq.docx

Accurately identifying immune cell types in single-cell RNA-sequencing (scRNA-Seq) data is critical to uncovering immune responses in health or disease conditions. However, the high heterogeneity and sparsity of scRNA-Seq data, as well as the similarity in gene expression among immune cell types, poses a great challenge for accurate identification of immune cell types in scRNA-Seq data. Here, we developed a tool named sc-ImmuCC for hierarchical annotation of immune cell types from scRNA-Seq data, based on the optimized gene sets and ssGSEA algorithm. sc-ImmuCC simulates the natural differentiation of immune cells, and the hierarchical annotation includes three layers, which can annotate nine major immune cell types and 29 cell subtypes. The test results showed its stable performance and strong consistency among different tissue datasets with average accuracy of 71-90%. In addition, the optimized gene sets and hierarchical annotation strategy could be applied to other methods to improve their annotation accuracy and the spectrum of annotated cell types and subtypes. We also applied sc-ImmuCC to a dataset composed of COVID-19, influenza, and healthy donors, and found that the proportion of monocytes in patients with COVID-19 and influenza was significantly higher than that in healthy people. The easy-to-use sc-ImmuCC tool provides a good way to comprehensively annotate immune cell types from scRNA-Seq data, and will also help study the immune mechanism underlying physiological and pathological conditions.

精准识别单细胞RNA测序（scRNA-Seq）数据中的免疫细胞类型，对于揭示健康或疾病状态下的免疫应答至关重要。然而，scRNA-Seq数据的高度异质性与稀疏性，以及不同免疫细胞类型间的基因表达相似性，给该类数据中免疫细胞类型的精准识别带来了巨大挑战。本研究基于优化基因集与单样本基因集富集分析（ssGSEA）算法，开发了一款名为sc-ImmuCC的工具，用于对scRNA-Seq数据中的免疫细胞类型进行分层注释。sc-ImmuCC模拟了免疫细胞的自然分化过程，其分层注释体系共包含三层，可注释9种主要免疫细胞类型与29种细胞亚型。测试结果表明，该工具在不同组织数据集上表现稳定且一致性优异，平均准确率可达71%-90%。此外，该优化基因集与分层注释策略可推广至其他分析方法，以提升其注释准确率以及可注释细胞类型与亚型的覆盖范围。我们还将sc-ImmuCC应用于由新型冠状病毒肺炎（COVID-19）、流感患者及健康志愿者组成的数据集，发现新冠与流感患者体内的单核细胞占比显著高于健康人群。这款操作简便的sc-ImmuCC工具为从scRNA-Seq数据中全面注释免疫细胞类型提供了优质方案，也将助力生理与病理状态下的免疫机制研究。