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This package is deprecated. It will probably be removed from Bioconductor. Please refer to the package end-of-life guidelines for more information.

Sparse Data Observations for Simulating Synthetic Abundance

Bioconductor version: Release (3.19)

The package is to provide a model based Bayesian method to characterize and simulate microbiome data. sparseDOSSA's model captures the marginal distribution of each microbial feature as a truncated, zero-inflated log-normal distribution, with parameters distributed as a parent log-normal distribution. The model can be effectively fit to reference microbial datasets in order to parameterize their microbes and communities, or to simulate synthetic datasets of similar population structure. Most importantly, it allows users to include both known feature-feature and feature-metadata correlation structures and thus provides a gold standard to enable benchmarking of statistical methods for metagenomic data analysis.

Author: Boyu Ren<bor158 at>, Emma Schwager<emma.schwager at>, Timothy Tickle<ttickle at>, Curtis Huttenhower <chuttenh at>

Maintainer: Boyu Ren<bor158 at>, Emma Schwager <emma.schwager at>, George Weingart<george.weingart at>

Citation (from within R, enter citation("sparseDOSSA")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


Reference Manual PDF


biocViews Bayesian, ImmunoOncology, Metagenomics, Microbiome, Software
Version 1.28.0
In Bioconductor since BioC 3.5 (R-3.4) (7 years)
License MIT + file LICENSE
Imports stats, utils, optparse, MASS, tmvtnorm (>= 1.4.10), MCMCpack
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Suggests knitr, BiocStyle, BiocGenerics, rmarkdown
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