arXiv subjects

Pariya Behrouzi

Publications and source records attributed to Pariya Behrouzi.

10 recordsLinked to original sources

arXiv2025

Hierarchical Causal Structure Learning

Traditional statistical approaches primarily aim to model associations between variables, but many scientific and practical questions require causal methods instead. These approaches rely on assumptions about an underlying structure, often represented by a directed acyclic graph (DAG). When all variables are measured at the same level, causal structures can be learned using existing techniques. However, no suitable methods exist when data are organized hierarchically or across multiple levels. This paper addresses such cases, where both unit-level and group-level variables are present. These multi-level structures frequently arise in fields such as agriculture, where plants (units) grow within different environments (groups). Building on nonlinear structural causal models, or additive noise models, we propose a method that accommodates unobserved confounders as well as group-specific causal functions. The approach is implemented in the R package HSCM, available at https://CRAN.R-project.org/package=HSCM.

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arXiv2024

Multi-Attribute Preferences: A Transfer Learning Approach

This contribution introduces a novel statistical learning methodology based on the Bradley-Terry method for pairwise comparisons, where the novelty arises from the method's capacity to estimate the worth of objects for a primary attribute by incorporating data of secondary attributes. These attributes are properties on which objects are evaluated in a pairwise fashion by individuals. By assuming that the main interest of practitioners lies in the primary attribute, and the secondary attributes only serve to improve estimation of the parameters underlying the primary attribute, this paper utilises the well-known transfer learning framework. To wit, the proposed method first estimates a biased worth vector using data pertaining to both the primary attribute and the set of informative secondary attributes, which is followed by a debiasing step based on a penalised likelihood of the primary attribute. When the set of informative secondary attributes is unknown, we allow for their estimation by a data-driven algorithm. Theoretically, we show that, under mild conditions, the $\ell_\infty$ and $\ell_2$ rates are improved compared to fitting a Bradley-Terry model on just the data pertaining to the primary attribute. The favourable (comparative) performance under more general settings is shown by means of a simulation study. To illustrate the usage and interpretation of the method, an application of the proposed method is provided on consumer preference data pertaining to a cassava derived food product: eba. An R package containing the proposed methodology can be found on https://CRAN.R-project.org/package=BTTL.

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arXiv2024

Joint Learning from Heterogeneous Rank Data

The statistical modelling of ranking data has a long history and encompasses various perspectives on how observed rankings arise. One of the most common models, the Plackett-Luce model, is frequently used to aggregate rankings from multiple rankers into a single ranking that corresponds to the underlying quality of the ranked objects. Given that rankers frequently exhibit heterogeneous preferences, mixture-type models have been developed to group rankers with more or less homogeneous preferences together to reduce bias. However, occasionally, these preference groups are known a-priori. Under these circumstances, current practice consists of fitting Plackett-Luce models separately for each group. Nevertheless, there might be some commonalities between different groups of rankers, such that separate estimation implies a loss of information. We propose an extension of the Plackett-Luce model, the Sparse Fused Plackett-Luce model, that allows for joint learning of such heterogeneous rank data, whereby information from different groups is utilised to achieve better model performance. The observed rankings can be considered a function of variables pertaining to the ranked objects. As such, we allow for these types of variables, where information on the coefficients is shared across groups. Moreover, as not all variables might be relevant for the ranking of an object, we impose sparsity on the coefficients to improve interpretability, estimation and prediction of the model. Simulations studies indicate superior performance of the proposed method compared to existing approaches. To illustrate the usage and interpretation of the method, an application on data consisting of consumer preferences regarding various sweet potato varieties is provided. An R package containing the proposed methodology can be found on https://CRAN.R-project.org/package=SFPL.

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arXiv2024

Multi-omics network reconstruction with collaborative graphical lasso

Motivation: In recent years, the availability of multi-omics data has increased substantially. Multi-omics data integration methods mainly aim to leverage different molecular layers to gain a complete molecular description of biological processes. An attractive integration approach is the reconstruction of multi-omics networks. However, the development of effective multi-omics network reconstruction strategies lags behind. Results: In this study, we introduce collaborative graphical lasso, a novel approach that extends graphical lasso by incorporating collaboration between omics layers, thereby improving multi-omics data integration and enhancing network inference. Our method leverages a collaborative penalty term, which harmonizes the contribution of the omics layers to the reconstruction of the network structure. This promotes a cohesive integration of information across modalities, and it is introduced alongside a dual regularization scheme that separately controls sparsity within and between layers. To address the challenge of model selection in this framework, we propose XStARS, a stability-based criterion for multi-dimensional hyperparameter tuning. We assess the performance of collaborative graphical lasso and the corresponding model selection procedure through simulations, and we apply them to publicly available multi-omics data. This application demonstrated collaborative graphical lasso recovers established biological interactions while suggesting novel, biologically coherent connections. Availability and implementation: We implemented collaborative graphical lasso as an R package, available on CRAN as coglasso. The results of the manuscript can be reproduced running the code available at https://github.com/DrQuestion/coglasso_reproducible_code

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arXiv2023

A Spatial Autoregressive Graphical Model with Applications in Intercropping

Within the statistical literature, a significant gap exists in methods capable of modeling asymmetric multivariate spatial effects that elucidate the relationships underlying complex spatial phenomena. For such a phenomenon, observations at any location are expected to arise from a combination of within- and between- location effects, where the latter exhibit asymmetry. This asymmetry is represented by heterogeneous spatial effects between locations pertaining to different categories, that is, a feature inherent to each location in the data, such that based on the feature label, asymmetric spatial relations are postulated between neighbouring locations with different labels. Our novel approach synergises the principles of multivariate spatial autoregressive models and the Gaussian graphical model. This synergy enables us to effectively address the gap by accommodating asymmetric spatial relations, overcoming the usual constraints in spatial analyses. Using a Bayesian-estimation framework, the model performance is assessed in a simulation study. We apply the model on intercropping data, where spatial effects between different crops are unlikely to be symmetric, in order to illustrate the usage of the proposed methodology. An R package containing the proposed methodology can be found on https://CRAN.R-project.org/package=SAGM.

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arXiv2023

netgwas: An R Package for Network-Based Genome-Wide Association Studies

Graphical models are a powerful tool in modelling and analysing complex biological associations in high-dimensional data. The R-package netgwas implements the recent methodological development on copula graphical models to (i) construct linkage maps, (ii) infer linkage disequilibrium networks from genotype data, and (iii) detect high-dimensional genotype-phenotype networks. The netgwas learns the structure of networks from ordinal data and mixed ordinal-and-continuous data. Here, we apply the functionality in netgwas to various multivariate example datasets taken from the literature to demonstrate the kind of insight that can be obtained from the package. We show that our package offers a more realistic association analysis than the classical approaches, as it discriminates between direct and induced correlations by adjusting for the effect of all other variables while performing pairwise associations. This feature controls for spurious interactions between variables that can arise from conventional approaches in a biological sense. The netgwas package uses a parallelization strategy on multi-core processors to speed-up computations. The netgwas package is freely available at https://cran.r-project.org/web/packages/netgwas

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arXiv2022

Copula Graphical Models for Heterogeneous Mixed Data

This article proposes a graphical model that handles mixed-type, multi-group data. The motivation for such a model originates from real-world observational data, which often contain groups of samples obtained under heterogeneous conditions in space and time, potentially resulting in differences in network structure among groups. Therefore, the i.i.d. assumption is unrealistic, and fitting a single graphical model on all data results in a network that does not accurately represent the between group differences. In addition, real-world observational data is typically of mixed discrete-and-continuous type, violating the Gaussian assumption that is typical of graphical models, which leads to the model being unable to adequately recover the underlying graph structure. The proposed model takes into account these properties of data, by treating observed data as transformed latent Gaussian data, by means of the Gaussian copula, and thereby allowing for the attractive properties of the Gaussian distribution such as estimating the optimal number of model parameter using the inverse covariance matrix. The multi-group setting is addressed by jointly fitting a graphical model for each group, and applying the fused group penalty to fuse similar graphs together. In an extensive simulation study, the proposed model is evaluated against alternative models, where the proposed model is better able to recover the true underlying graph structure for different groups. Finally, the proposed model is applied on real production-ecological data pertaining to on-farm maize yield in order to showcase the added value of the proposed method in generating new hypotheses for production ecologists.

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arXiv2022

Bayesian Structural Learning with Parametric Marginals for Count Data: An Application to Microbiota Systems

High dimensional and heterogeneous count data are collected in various applied fields. In this paper, we look closely at high-resolution sequencing data on the microbiome, which have enabled researchers to study the genomes of entire microbial communities. Revealing the underlying interactions between these communities is of vital importance to learn how microbes influence human health. To perform structural learning from multivariate count data such as these, we develop a novel Gaussian copula graphical model with two key elements. Firstly, we employ parametric regression to characterize the marginal distributions. This step is crucial for accommodating the impact of external covariates. Neglecting this adjustment could potentially introduce distortions in the inference of the underlying network of dependences. Secondly, we advance a Bayesian structure learning framework, based on a computationally efficient search algorithm that is suited to high dimensionality. The approach returns simultaneous inference of the marginal effects and of the dependence structure, including graph uncertainty estimates. A simulation study and a real data analysis of microbiome data highlight the applicability of the proposed approach at inferring networks from multivariate count data in general, and its relevance to microbiome analyses in particular. The proposed method is implemented in the R package BDgraph.

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arXiv2018

Dynamic Chain Graph Models for Ordinal Time Series Data

This paper introduces sparse dynamic chain graph models for network inference in high dimensional non-Gaussian time series data. The proposed method parametrized by a precision matrix that encodes the intra time-slice conditional independence among variables at a fixed time point, and an autoregressive coefficient that contains dynamic conditional independences interactions among time series components across consecutive time steps. The proposed model is a Gaussian copula vector autoregressive model, which is used to model sparse interactions in a high-dimensional setting. Estimation is achieved via a penalized EM algorithm. In this paper, we use an efficient coordinate descent algorithm to optimize the penalized log-likelihood with the smoothly clipped absolute deviation penalty. We demonstrate our approach on simulated and genomic datasets. The method is implemented in an R package tsnetwork.

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arXiv2018

De novo construction of polyploid linkage maps using discrete graphical models

Linkage maps are used to identify the location of genes responsible for traits and diseases. New sequencing techniques have created opportunities to substantially increase the density of genetic markers. Such revolutionary advances in technology have given rise to new challenges, such as creating high-density linkage maps. Current multiple testing approaches based on pairwise recombination fractions are underpowered in the high-dimensional setting and do not extend easily to polyploid species. We propose to construct linkage maps using graphical models either via a sparse Gaussian copula or a nonparanormal skeptic approach. Linkage groups (LGs), typically chromosomes, and the order of markers in each LG are determined by inferring the conditional independence relationships among large numbers of markers in the genome. Through simulations, we illustrate the utility of our map construction method and compare its performance with other available methods, both when the data are clean and contain no missing observations and when data contain genotyping errors and are incomplete. We apply the proposed method to two genotype datasets: barley and potato from diploid and polypoid populations, respectively. Our comprehensive map construction method makes full use of the dosage SNP data to reconstruct linkage map for any bi-parental diploid and polyploid species. We have implemented the method in the R package netgwas.

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