tascCODA: Bayesian Tree-Aggregated Analysis of Compositional Amplicon and Single-Cell Data.
bayesian modeling
differential abundance testing
dirichlet multinomial
microbiome data
single-cell data
spike-and-slab lasso
tree aggregation
Journal
Frontiers in genetics
ISSN: 1664-8021
Titre abrégé: Front Genet
Pays: Switzerland
ID NLM: 101560621
Informations de publication
Date de publication:
2021
2021
Historique:
received:
29
08
2021
accepted:
01
11
2021
entrez:
24
12
2021
pubmed:
25
12
2021
medline:
25
12
2021
Statut:
epublish
Résumé
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
Identifiants
pubmed: 34950190
doi: 10.3389/fgene.2021.766405
pii: 766405
pmc: PMC8689185
doi:
Types de publication
Journal Article
Langues
eng
Pagination
766405Informations de copyright
Copyright © 2021 Ostner, Carcy and Müller.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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