Approaches for integrating heterogeneous RNA-seq data reveal cross-talk between microbes and genes in asthmatic patients
Daniel Spakowicz, Shaoke Lou, Brian Barron, José L. Gómez, Tianxiao Li, Qing Liu, Nicole Grant, Xiting Yan, Rebecca Hoyd, George M. Weinstock, Geoffrey Chupp, Mark Gerstein
Abstract
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.