tascCODA: Bayesian tree-aggregated analysis of compositional amplicon and single-cell data

Abstract

Accurate generative statistical modeling of count data is of critical relevance for the analysis of biological datasets from high-throughput sequencing technologies. Important instances include the modeling of microbiome compositions from amplicon sequencing surveys and the analysis of cell type compositions derived from single-cell RNA sequencing. Microbial and cell type abundance data share remarkably similar statistical features, including their inherent compositionality and a natural hierarchical ordering of the individual components from taxonomic or cell lineage tree information, respectively. To this end, we introduce a Bayesian model for tree-aggregated amplicon and single-cell compositional data analysis tascCODA that seamlessly integrates hierarchical information and experimental covariate data into the generative modeling of compositional count data. By combining latent parameters based on the tree structure with spike-and-slab Lasso penalization, tascCODA can determine covariate effects across different levels of the population hierarchy in a data-driven parsimonious way. In the context of differential abundance testing, we validate tascCODA's excellent performance on a comprehensive set of synthetic benchmark scenarios. Our analyses on human single-cell RNA-seq data from ulcerative colitis patients and amplicon data from patients with irritable bowel syndrome, respectively, identified aggregated cell type and taxon compositional changes that were more predictive and parsimonious than those proposed by other schemes. We posit that tascCODA constitutes a valuable addition to the growing statistical toolbox for generative modeling and analysis of compositional changes in microbial or cell population data.

Negative binomial factor regression with application to microbiome data analysis

“…Alternative generative statistical modeling approaches include the Dirichlet multinomial (mixture) framework, 22 latent Dirichlet allocation, 23,24 and Poisson distribution models 25 (including their respective zero‐inflated extensions). Several models also allow the inclusion of host or environmental covariate data in generative modeling, including Poisson factor models, 26‐28 latent Dirichlet allocation, 29 and Bayesian Dirichlet multinomial models 30‐32 . Due to the abundance of excess zeros at the amplicon or species level, some of these models also include a zero‐inflation modeling component.…”

“…Several models also allow the inclusion of host or environmental covariate data in generative modeling, including Poisson factor models, [26][27][28] latent Dirichlet allocation, 29 and Bayesian Dirichlet multinomial models. [30][31][32] Due to the abundance of excess zeros at the amplicon or species level, some of these models also include a zero-inflation modeling component. This and the high dimensionality of the data at the OTU/ASV level make both estimation and biological interpretability challenging.…”

Negative Binomial factor regression with application to microbiome data analysis

“…Alternative generative statistical modeling approaches include the Dirichlet Multinomial (mixture) framework 22 , latent Dirichlet allocation 23,24 , and Poisson distribution models 25 (including their respective zero-inflated extensions). Several models also allow the inclusion of host or environmental covariate data in generative modeling, including Poisson factor models 26,27,28 , latent Dirichlet allocation 29 , and Bayesian Dirichlet multinomial models 30,31 . The majority of these procedures, however, tackles the arguably more challenging problem of modeling data at the amplicon or species level.…”