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中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。由于人体细胞和外源（微生物）材料的复杂和异质混合物，痰样品的 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管道，以应对异质性。LDA（潜在狄利克雷分配）-link 使用降维 LDA 主题将微生物与基因连接起来。我们用单细胞 RNA-seq 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，以发现微生物与基因之间的已知和新关系。