Approaches for integrating heterogeneous RNA-seq data reveal cross-talk between microbes and genes in asthmatic patients
Approaches for integrating heterogeneous RNA-seq data reveal cross-talk between microbes and genes in asthmatic patients
复制标题
DOI：
10.1186/s13059-020-02033-z
复制
发表时间：
2020-06-22
期刊：
影响因子：
12.3
通讯作者：
Gerstein, Mark
中科院分区：
文献类型：
--
作者：
Spakowicz, Daniel;Lou, Shaoke;Gerstein, Mark
痰液诱导是一种非侵入性的方法来评估气道环境，特别是哮喘。由于人类细胞和外源性（微生物）物质的复杂和异质混合物，痰液样本的RNA测序（RNA-seq）可能具有挑战性。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（Latent Dirichlet allocation）-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中，以发现微生物和基因之间已知和新的关系。
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.