HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data.
High-dimensional mixed-effects models are an increasingly important form of regression in which the number of covariates rivals or exceeds the number of samples, which are collected in groups or clusters. The penalized likelihood approach to fitting these models relies on a coordinate descent algori...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012143 |
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| author | Evan Gorstein Rosa Aghdam Claudia Solís-Lemus |
| author_facet | Evan Gorstein Rosa Aghdam Claudia Solís-Lemus |
| author_sort | Evan Gorstein |
| collection | DOAJ |
| description | High-dimensional mixed-effects models are an increasingly important form of regression in which the number of covariates rivals or exceeds the number of samples, which are collected in groups or clusters. The penalized likelihood approach to fitting these models relies on a coordinate descent algorithm that lacks guarantees of convergence to a global optimum. Here, we empirically study the behavior of this algorithm on simulated and real examples of three types of data that are common in modern biology: transcriptome, genome-wide association, and microbiome data. Our simulations provide new insights into the algorithm's behavior in these settings, and, comparing the performance of two popular penalties, we demonstrate that the smoothly clipped absolute deviation (SCAD) penalty consistently outperforms the least absolute shrinkage and selection operator (LASSO) penalty in terms of both variable selection and estimation accuracy across omics data. To empower researchers in biology and other fields to fit models with the SCAD penalty, we implement the algorithm in a Julia package, HighDimMixedModels.jl. |
| format | Article |
| id | doaj-art-95f8cf0ec8934b39a394cf3c74e3e1e4 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-95f8cf0ec8934b39a394cf3c74e3e1e42025-02-05T05:30:41ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-01-01211e101214310.1371/journal.pcbi.1012143HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data.Evan GorsteinRosa AghdamClaudia Solís-LemusHigh-dimensional mixed-effects models are an increasingly important form of regression in which the number of covariates rivals or exceeds the number of samples, which are collected in groups or clusters. The penalized likelihood approach to fitting these models relies on a coordinate descent algorithm that lacks guarantees of convergence to a global optimum. Here, we empirically study the behavior of this algorithm on simulated and real examples of three types of data that are common in modern biology: transcriptome, genome-wide association, and microbiome data. Our simulations provide new insights into the algorithm's behavior in these settings, and, comparing the performance of two popular penalties, we demonstrate that the smoothly clipped absolute deviation (SCAD) penalty consistently outperforms the least absolute shrinkage and selection operator (LASSO) penalty in terms of both variable selection and estimation accuracy across omics data. To empower researchers in biology and other fields to fit models with the SCAD penalty, we implement the algorithm in a Julia package, HighDimMixedModels.jl.https://doi.org/10.1371/journal.pcbi.1012143 |
| spellingShingle | Evan Gorstein Rosa Aghdam Claudia Solís-Lemus HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data. PLoS Computational Biology |
| title | HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data. |
| title_full | HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data. |
| title_fullStr | HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data. |
| title_full_unstemmed | HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data. |
| title_short | HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data. |
| title_sort | highdimmixedmodels jl robust high dimensional mixed effects models across omics data |
| url | https://doi.org/10.1371/journal.pcbi.1012143 |
| work_keys_str_mv | AT evangorstein highdimmixedmodelsjlrobusthighdimensionalmixedeffectsmodelsacrossomicsdata AT rosaaghdam highdimmixedmodelsjlrobusthighdimensionalmixedeffectsmodelsacrossomicsdata AT claudiasolislemus highdimmixedmodelsjlrobusthighdimensionalmixedeffectsmodelsacrossomicsdata |