A0720
Title: A Gaussian mixture model to integrate metagenome and metatranscriptome data
Authors: Di Wu - University of North Carolina at Chapel Hill (United States) [presenting]
Abstract: Bacterial dysbiosis has been implicated in various clinical conditions, e.g., caries, gut diseases and cancer. Microbial composition and abundance can be captured by microbiome DNA sequencing for the question of what bacteria are there?, while metatranscriptomics through RNA sequencing identifies functional characterization of complex microbial communities to answer what the bacteria do there. The joint analyses of paired metagenomics and metatranscriptomics data are one way to study bacterial species and genes' functional roles in statuses of health and diseases but remain challenging due to the high dimension and sparsity of the data. To address this knowledge gap, we will study the differential transcriptional activity by investigating the RNA/DNA ratios at species. We propose a two-step differential expression analysis approach that includes testing at each of the two modalities and fitting the log-RNA/DNA ratio to a novel Gaussian Mixture statistical model. Our proposed method IntegRatio is flexible to control batch effects and accommodate multiple study covariates. It also comprehensively tests more microbiome-specific hypotheses simultaneously than the conventional method. Real data-inspired simulations show the controlled type I error and decent power. The proposed method has been applied in studying Early Childhood Caries (ECC) and Inflammatory Bowel Diseases (IBD) to identify species that have differential regulatory activities associated with diseases.
