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            ‐Penalized Methods in High‐Dimensional Gaussian Markov Random Fields

Augugliaro, Luigi; Mineo, Angelo; Wit, Ernst

doi:10.1002/9783527694365.ch8

Cited by 4 publications

(3 citation statements)

References 88 publications

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“…If λ 2 is zero (i.e., no penalty is imposed) then Θ ( k ) are independent and no information is shared between them. To select the proper hyperparameters, we used a goodness-of-fit approach where a grid search was performed to select values that minimize the Bayesian information criterion [BIC] (Schwarz 1978) specified in Eq.5 (Augugliaro et al 2016), yielding values that balance model likelihood and complexity. …”

Section: Methodsmentioning

confidence: 99%

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

Bornhofen

Fè

Nagy

et al. 2022

Preprint

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Understanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce. Therefore, this study aimed to combine multi-omics from grass hybrids by prioritizing omic features based on the reconstruction of interacting networks and assessing their relevance in explaining economically important phenotypes. Transcriptomic and NMR-based metabolomic data were used for sparse estimation via the fused graphical lasso, followed by modularity-based gene expression and metabolite-metabolite network reconstruction, node hub identification, omic-phenotype association via pairwise fitting of a multivariate genomic model, and machine learning-based prediction study. Analyses were jointly performed across two data sets composed of family pools of hybrid ryegrass (Lolium perenne x L. multiflorum) and Festulolium loliaceum (L. perenne x Festuca pratensis), whose phenotypes were recorded for eight traits in field trials across two European countries in 2020/21. Our results suggest substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing L. perenne with another species within the genus relative to across genera. However, conserved hub genes and hub metabolomic features were detected between pedigree classes, some of which were highly heritable and displayed one or more significant edges with agronomic traits in a weighted omics-phenotype network. In spite of tagging relevant biological molecules as, for example, the light-induced rice 1 (LIR1), hub features were not necessarily better explanatory variables for omics-assisted prediction than features stochastically sampled. The use of the graphical lasso method for network reconstruction and identification of biological targets is discussed with an emphasis on forage grass breeding.

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Section: Methodsmentioning

confidence: 99%

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

Bornhofen

Fè

Nagy

et al. 2022

Preprint

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“…Friedman et al, 2008). The interested reader is referred to Augugliaro et al (2016) for an extensive review.…”

Section: Introductionmentioning

confidence: 99%

The conditional censored graphical lasso estimator

2020

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The Gaussian graphical model is one of the most used tools for inferring genetic networks. Nowadays, the data are often collected from different sources or under different biological conditions, resulting in heterogeneous datasets that exhibit a dependency structure that varies across groups. The complex structure of these data is typically recovered using regularized inferential procedures that use two penalties, one that encourages sparsity within each graph and the other that encourages common structures among the different groups. To this date, these approaches have not been developed for handling the case of censored data. However, these data are often generated by gene expression technologies such as RT-qPCR experiments. In this paper, we fill this gap and propose an extension of joint Gaussian graphical modelling to account for censored, or more generally missing, data.

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“…Menéndez et al, 2010;Vinciotti et al, 2016) and widely studied in the computational (e.g., Friedman et al, 2008;Witten et al, 2011;Mazumder and Hastie, 2012) as well as theoretical (e.g., Bickel and Levina, 2008;Guo et al, 2011) literature. The interested reader is referred to Augugliaro et al (2016) for an extensive review. Despite the widespread literature on the graphical lasso estimator, there is a great number of fields in applied research where modern measurement technologies make the use of this graphical model theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied.…”

Section: Introductionmentioning

confidence: 99%

$\ell_1$ -Penalized censored Gaussian graphical model

2018

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Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithms for inference. We evaluate the computational efficiency of the proposed algorithms by an extensive simulation study and show that, when censored data are available, our proposal is superior to existing competitors both in terms of network recovery and parameter estimation. We apply the proposed method to gene expression data generated by microfluidic Reverse Transcription quantitative Polymerase Chain Reaction technology in order to make inference on the regulatory mechanisms of blood development. A software implementation of our method is available on github (https://github.com/LuigiAugugliaro/cglasso).

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ℓ ₁ ‐Penalized Methods in High‐Dimensional Gaussian Markov Random Fields

Cited by 4 publications

References 88 publications

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

The conditional censored graphical lasso estimator

$\ell_1$ -Penalized censored Gaussian graphical model

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ℓ 1 ‐Penalized Methods in High‐Dimensional Gaussian Markov Random Fields

Cited by 4 publications

References 88 publications

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

The conditional censored graphical lasso estimator

$\ell_1$ -Penalized censored Gaussian graphical model

Contact Info

Product

Resources

About

ℓ ₁ ‐Penalized Methods in High‐Dimensional Gaussian Markov Random Fields