Application of graphical lasso in estimating network structure in gene set

Huang, Yu-Jyun; Lu, Tzu‐Pin; Hsiao, Chuhsing Kate

doi:10.21037/atm-20-6490

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…The MB inference method has been used in discovering causal relationships between gene regulatory networks [ 18 ] and in modeling microbial ecological networks [ 19 ]. Glasso [ 5 ] has been used extensively in the medical field, for diverse applications such as estimating the network structure of lung cancer gene sets [ 20 ], diagnosing endometriosis in teenage girls [ 21 ], and investigating the relationship between anxiety and depression symptoms [ 22 ]. Glasso has also been used with network analysis to identify relationships between suicidal individuals and suicide attempts [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

Inferred networks, machine learning, and health data

et al. 2023

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This paper presents a network science approach to investigate a health information dataset, the Sexual Acquisition and Transmission of HIV Cooperative Agreement Program (SATHCAP), to uncover hidden relationships that can be used to suggest targeted health interventions. From the data, four key target variables are chosen: HIV status, injecting drug use, homelessness, and insurance status. These target variables are converted to a graph format using four separate graph inference techniques: graphical lasso, Meinshausen Bühlmann (MB), k-Nearest Neighbors (kNN), and correlation thresholding (CT). The graphs are then clustered using four clustering methods: Louvain, Leiden, and NBR-Clust with VAT and integrity. Promising clusters are chosen using internal evaluation measures and are visualized and analyzed to identify marker attributes and key relationships. The kNN and CT inference methods are shown to give useful results when combined with NBR-Clust clustering. Examples of cluster analysis indicate that the methodology produces results that will be relevant to the public health community.

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Section: Related Workmentioning

confidence: 99%

Inferred networks, machine learning, and health data

et al. 2023

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“…In contrast, network inference by graphical lasso [13,14] results in the identification of pairwise edges reflecting direct effects, such that the absence of an edge implies the conditional independence of the genes when all other genes are observed. Further, the formulation of graphical lasso enables flexible penalization based on network density which aids in the identification of a network structure that improves the discovery of true gene-gene interactions while reducing false positives [15] . Here, we focus on improving the statistical power of network inference by significantly increasing the number of samples used in network inference, leveraging large-scale publicly available and uniformly processed RNA-seq data from recount3 [16] which includes human RNA-seq samples from GTEx [17] , TCGA [18] , and SRA [19,20] .…”

Section: Introductionmentioning

confidence: 99%

Aggregation ofrecount3RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks

Ravichandran,

Parsana,

Keener

et al. 2024

Preprint

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Background: Gene co-expression networks (GCNs) describe relationships among expressed genes key to maintaining cellular identity and homeostasis. However, the small sample size of typical RNA-seq experiments which is several orders of magnitude fewer than the number of genes is too low to infer GCNs reliably. recount3, a publicly available dataset comprised of 316,443 uniformly processed human RNA-seq samples, provides an opportunity to improve power for accurate network reconstruction and obtain biological insight from the resulting networks. Results: We compared alternate aggregation strategies to identify an optimal workflow for GCN inference by data aggregation and inferred three consensus networks: a universal network, a non-cancer network, and a cancer network in addition to 27 tissue context-specific networks. Central network genes from our consensus networks were enriched for evolutionarily constrained genes and ubiquitous biological pathways, whereas central context-specific network genes included tissue-specific transcription factors and factorization based on the hubs led to clustering of related tissue contexts. We discovered that annotations corresponding to context-specific networks inferred from aggregated data were enriched for trait heritability beyond known functional genomic annotations and were significantly more enriched when we aggregated over a larger number of samples. Conclusion: This study outlines best practices for network GCN inference and evaluation by data aggregation. We recommend estimating and regressing confounders in each data set before aggregation and prioritizing large sample size studies for GCN reconstruction. Increased statistical power in inferring context-specific networks enabled the derivation of variant annotations that were enriched for concordant trait heritability independent of functional genomic annotations that are context-agnostic. While we observed strictly increasing held-out log-likelihood with data aggregation, we noted diminishing marginal improvements. Future directions aimed at alternate methods for estimating confounders and integrating orthogonal information from modalities such as Hi-C and ChIP-seq can further improve GCN inference.

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“…The network analysis of multi-dimensional data for structural information learning has attracted much attention in the biomedical research community. Examples include gene regulatory networks, brain connectivity networks, and microbial networks ( Zhang et al, 2019 ; Huang et al, 2020 ). An undirected graphical model, the Markov random field (MRF), is a common approach to describe the network structure of a group of genetic variables, because of its direct interpretation of edges with the conditional dependence between nodes.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic edge inference of gene networks with markov random field-based bayesian learning

Huang

Mukherjee²,

Hsiao³

2022

Front. Genet.

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Current algorithms for gene regulatory network construction based on Gaussian graphical models focuses on the deterministic decision of whether an edge exists. Both the probabilistic inference of edge existence and the relative strength of edges are often overlooked, either because the computational algorithms cannot account for this uncertainty or because it is not straightforward in implementation. In this study, we combine the Bayesian Markov random field and the conditional autoregressive (CAR) model to tackle simultaneously these two tasks. The uncertainty of edge existence and the relative strength of edges can be measured and quantified based on a Bayesian model such as the CAR model and the spike-and-slab lasso prior. In addition, the strength of the edges can be utilized to prioritize the importance of the edges in a network graph. Simulations and a glioblastoma cancer study were carried out to assess the proposed model’s performance and to compare it with existing methods when a binary decision is of interest. The proposed approach shows stable performance and may provide novel structures with biological insights.

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Application of graphical lasso in estimating network structure in gene set

Cited by 6 publications

References 19 publications

Inferred networks, machine learning, and health data

Inferred networks, machine learning, and health data

Aggregation ofrecount3RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks

Probabilistic edge inference of gene networks with markov random field-based bayesian learning

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