PDF(Pubmed)
Abstract：
Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify pluripotency associated pathways from scRNA-seq data. On this occasion, we extend this approach to quantify the activity of a pathway associated with any biological process, or even any list of genes. A systems-level characterization of pathway activities across multiple cell types provides a broadly applicable tool for the analysis of pathways in both healthy and disease conditions. Dysregulated cellular functions are a hallmark of a wide spectrum of human disorders, including cancer and autoimmune diseases. Here, we illustrate our method by analyzing various biological processes in healthy and cancer breast samples. Using this approach we found that tumor breast cells, even when they form a single group in the UMAP space, keep diverse biological programs active in a differentiated manner within the cluster.•We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes.•The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.
摘要：
通路分析是解释单细胞转录组数据的重要步骤，因为它提供了强大的信息来检测每个细胞中哪些细胞过程是活跃的。我们最近开发了一种基于蛋白质-蛋白质相互作用网络的框架，以从scRNA-seq数据中量化多能性相关途径。在这个场合,我们扩展了这种方法来量化与任何生物过程相关的通路的活性，甚至任何基因列表。跨多种细胞类型的途径活性的系统级表征提供了广泛适用的工具，用于分析健康和疾病状况中的途径。细胞功能失调是广泛的人类疾病的标志，包括癌症和自身免疫性疾病。这里,我们通过分析健康和癌症乳腺癌样本中的各种生物过程来说明我们的方法。使用这种方法，我们发现肿瘤乳腺细胞，即使它们在UMAP空间中形成一个组，保持不同的生物程序在集群内以差异化的方式活跃。•我们实施基于蛋白质-蛋白质相互作用网络的方法来量化不同生物过程的活性。•该方法可用于scRNA-seq研究中的细胞注释,并且可作为R包免费获得。
