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

Front. Genet. 12, 766405:766405 (2021)
Verlagsversion Forschungsdaten DOI
Open Access Gold
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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 tascCODA1 constitutes a valuable addition to the growing statistical toolbox for generative modeling and analysis of compositional changes in microbial or cell population data.

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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Bayesian Modeling ; Differential Abundance Testing ; Dirichlet Multinomial ; Microbiome Data ; Single-cell Data ; Spike-and-slab Lasso ; Tree Aggregation
ISSN (print) / ISBN 1664-8021
e-ISSN 1664-8021
Zeitschrift Frontiers in Genetics
Quellenangaben Band: 12, Heft: , Seiten: 766405 Artikelnummer: 766405 Supplement: ,
Verlag Frontiers
Begutachtungsstatus Peer reviewed