Sputum induction is a non-invasive method to evaluate the airway environment, particularly for asthma. RNA sequencing (RNA-seq) of sputum samples can be challenging to interpret due to the complex and heterogeneous mixtures of human cells and exogenous (microbial) material. In this study, we develop a pipeline that integrates dimensionality reduction and statistical modeling to grapple with the heterogeneity. LDA(Latent Dirichlet allocation)-link connects microbes to genes using reduced-dimensionality LDA topics. We validate our method with single-cell RNA-seq and microscopy and then apply it to the sputum of asthmatic patients to find known and novel relationships between microbes and genes.

中文翻译：

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整合异质 RNA-seq 数据的方法揭示了哮喘患者微生物和基因之间的串扰


痰诱导是一种评估气道环境的非侵入性方法，特别是对于哮喘。由于人类细胞和外源（微生物）物质的混合物复杂且异质，因此痰样本的 RNA 测序 (RNA-seq) 可能难以解释。在这项研究中，我们开发了一个集成降维和统计建模的流程来应对异质性。 LDA（潜在狄利克雷分配）链接使用降维 LDA 主题将微生物与基因连接起来。我们通过单细胞 RNA 测序和显微镜验证了我们的方法，然后将其应用于哮喘患者的痰液，以发现微生物和基因之间已知的和新颖的关系。