Sparse regression, classification, and microbial network estimation in QIIME2 with q2-classo and q2-gglasso
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910138228539392 |
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| author | Vlasovets, Oleg Schaipp, Fabian Simpson, Leo Bolyen, Evan Caporaso, J. Gregory Mueller, Christian L. |
| author_facet | Vlasovets, Oleg Schaipp, Fabian Simpson, Leo Bolyen, Evan Caporaso, J. Gregory Mueller, Christian L. |
| contents | Motivation: Statistical analysis of microbial count data derived from 16S rRNA or metagenomics sequencing poses unique challenges due to the sparse, compositional, and high-dimensional nature of the data. While QIIME 2 already provides many tools for data pre-processing and analysis, plugins for statistical regression, classification, and microbial network estimation tailored to compositional count data are relatively scarce. Results: We present q2-classo and q2-gglasso, two novel QIIME 2 plugins that implement penalized regression, classification, and graphical modeling approaches for microbial compositional data. q2-classo enables the prediction of a continuous or binary outcome of interest using compositional microbiome data as predictors. Both sparse log-contrast regression and classification, as well as tree-aggregated log-contrast models are available. q2-gglasso enables the estimation of taxon-taxon association networks through sparse graphical model estimation, such as, e.g., the SPIEC-EASI framework, as well as adaptive and latent graphical models. The latent model can decompose taxon-taxon associations into a sparse direct interaction matrix and a latent (low-rank) matrix which enables robust principal component embedding of a data set. Within the QIIME 2 ecosystem we demonstrate their application on the Atacama soil microbiome dataset, illustrating robust model selection, classification, and microbial network estimation with covariates and latent factors. Availability: The software is freely available under the BSD-3-Clause License. Source code is available at https://github.com/bio-datascience/q2-gglasso and https://github.com/bio-datascience/q2-classo-latest, with installation through QIIME 2 and Docker. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15520 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sparse regression, classification, and microbial network estimation in QIIME2 with q2-classo and q2-gglasso Vlasovets, Oleg Schaipp, Fabian Simpson, Leo Bolyen, Evan Caporaso, J. Gregory Mueller, Christian L. Quantitative Methods Motivation: Statistical analysis of microbial count data derived from 16S rRNA or metagenomics sequencing poses unique challenges due to the sparse, compositional, and high-dimensional nature of the data. While QIIME 2 already provides many tools for data pre-processing and analysis, plugins for statistical regression, classification, and microbial network estimation tailored to compositional count data are relatively scarce. Results: We present q2-classo and q2-gglasso, two novel QIIME 2 plugins that implement penalized regression, classification, and graphical modeling approaches for microbial compositional data. q2-classo enables the prediction of a continuous or binary outcome of interest using compositional microbiome data as predictors. Both sparse log-contrast regression and classification, as well as tree-aggregated log-contrast models are available. q2-gglasso enables the estimation of taxon-taxon association networks through sparse graphical model estimation, such as, e.g., the SPIEC-EASI framework, as well as adaptive and latent graphical models. The latent model can decompose taxon-taxon associations into a sparse direct interaction matrix and a latent (low-rank) matrix which enables robust principal component embedding of a data set. Within the QIIME 2 ecosystem we demonstrate their application on the Atacama soil microbiome dataset, illustrating robust model selection, classification, and microbial network estimation with covariates and latent factors. Availability: The software is freely available under the BSD-3-Clause License. Source code is available at https://github.com/bio-datascience/q2-gglasso and https://github.com/bio-datascience/q2-classo-latest, with installation through QIIME 2 and Docker. |
| title | Sparse regression, classification, and microbial network estimation in QIIME2 with q2-classo and q2-gglasso |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2604.15520 |