单细胞 RNA 序列 (scRNA-seq) 技术已被广泛用于以单细胞分辨率揭示呼吸病理学和生理学的分子机制。在这里，我们通过整合 8 个公共数据集的数据建立了单细胞荟萃分析 (scMeta-analysis)，其中包括 104 个具有临床病理信息的肺部 scRNA-seq 样本，并指定了吸烟肺部图谱。该图谱揭示了早期致癌事件，并定义了吸烟引起的单细胞转录组学、细胞群和生物途径基本特性的变化。此外，我们开发了两种新颖的 scMeta 分析方法：VARIED（表达多样性关系可视化算法）和 AGED（衰老相关基因表达差异）。 VARIED 分析揭示了与吸烟致癌相关的表达多样性。 AGED 分析揭示了与衰老和吸烟状况相关的基因表达差异。 scMeta 分析为利用公开的 scRNA-seq 数据铺平了道路，并以单细胞分辨率提供了关于吸烟影响和人肺细胞多样性的新见解。

Single-cell RNA-seq (scRNA-seq) technologies have been broadly utilized to reveal molecular mechanisms of respiratory pathology and physiology at single-cell resolution. Here, we established single-cell meta-analysis (scMeta-analysis) by integrating data from 8 public datasets, including 104 lung scRNA-seq samples with clinicopathological information and designated a cigarette smoking lung atlas. The atlas revealed early carcinogenesis events and defined the alterations of single-cell transcriptomics, cell population, and fundamental properties of biological pathways induced by smoking. In addition, we developed two novel scMeta-analysis methods: VARIED (Visualized Algorithms of Relationships In Expressional Diversity) and AGED (Aging-related Gene Expressional Differences). VARIED analysis revealed expressional diversity associated with smoking carcinogenesis. AGED analysis revealed differences in gene expression related to both aging and smoking status. The scMeta-analysis pave the way to utilize publicly -available scRNA-seq data and provide new insights into the effects of smoking and into cellular diversity in human lungs, at single-cell resolution.