Name
Abstract | ||
|---|---|---|
Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSA's model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbe-microbe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. "taxa ") or between features and "phenotypes " to be simulated for method benchmarking. Here, we demonstrate SparseDOSSA's performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSA's utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at .</p> |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1371/journal.pcbi.1008913 | PLOS COMPUTATIONAL BIOLOGY |
DocType | Volume | Issue |
Journal | 17 | 9 |
ISSN | Citations | PageRank |
1553-734X | 0 | 0.34 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyuan Ma | 1 | 0 | 0.68 |
| Boyu Ren | 2 | 0 | 0.34 |
| Himel Mallick | 3 | 3 | 2.21 |
| Yo Sup Moon | 4 | 0 | 0.34 |
| Schwager Emma | 5 | 0 | 1.01 |
| Sagun Maharjan | 6 | 0 | 0.34 |
| Timothy L Tickle | 7 | 0 | 0.68 |
| Yiren Lu | 8 | 0 | 0.34 |
| Rachel N Carmody | 9 | 0 | 0.34 |
| Eric A Franzosa | 10 | 0 | 1.69 |
| Lucas Janson | 11 | 61 | 6.20 |
| Curtis Huttenhower | 12 | 438 | 30.18 |