Condition-adaptive fused graphical lasso (CFGL): an adaptive procedure for inferring condition-specific gene co-expression network

Lyu, Yafei; Xue, Lingzhou; Zhang, Feipeng; Koch, Hillary; Saba, Laura; Kechris, Katerina; Li, Qunhua

doi:10.1101/290346

Cited by 5 publications

(15 citation statements)

References 71 publications

(60 reference statements)

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“…In (S5), three conditions were considered. The first and third conditions had the same networks, February 8, 2022 9/29 structures [44,52], we used the Barabasi-Albert model [53] to simulate the unweighted network topology, i.e., the adjacency matrix with indicator elements, 1 if an edge was present between a pair of genes and 0 otherwise. Next, the k-th weighted network, A (k) was generated as,…”

Section: Simulation Setupmentioning

confidence: 99%

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RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks

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Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When gene-expression data from multiple conditions (e.g., treatments, tissues, strains) are available, joint estimation of networks harnessing shared information across them can significantly increase the power of analysis. In addition, examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. Condition adaptive fused graphical lasso (CFGL) is an existing method that incorporates condition specificity in a fused graphical lasso (FGL) model for estimating multiple co-expression networks. However, the current implementation of CFGL is prohibitively slow even for a moderate number of genes and can only be used for a maximum of three conditions. In this paper, we propose a fast alternative of CFGL known as rapid condition adaptive fused graphical lasso (RCFGL). In RCFGL, we incorporate the condition specificity into another popular model for joint network estimation, known as fused multiple graphical lasso (FMGL). We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation and making it easily generalizable for more than three conditions. We also present a novel screening rule to determine if the full network estimation problem can be broken down into estimation of smaller disjoint sub-networks, thereby reducing the complexity further. We demonstrate the computational advantage and superior performance of our method compared to two non-condition adaptive methods, FGL and FMGL, and one condition adaptive method, CFGL in several simulation scenarios. We use RCFGL to jointly estimate the gene co-expression networks of different brain regions (conditions) using a cohort of heterogeneous stock rats. We also provide an accommodating C and Python based package that implements RCFGL.

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Section: Simulation Setupmentioning

confidence: 99%

“…al. [44] developed condition adaptive fused graphical lasso (CFGL). The penalty term considered in CFGL is a modification of the pair-wise fused lasso penalty by incorporating binary weight matrices capturing condition-specificity.…”

Section: Introductionmentioning

confidence: 99%

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks

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“…Several works introduced modifications to the penalization in (1) to induce the estimated GGMs to be similar while allowing for structural differences, see e.g. Guo et al (2011); Mohan et al (2014); Danaher et al (2014); Lyu et al (2018).…”

Section: Preliminariesmentioning

confidence: 99%

Group-wise shrinkage estimation in penalized model-based clustering

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Fop³

2021

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Gaussian Graphical Models are widely employed for modelling dependence among variables. Likewise, finite Gaussian mixtures are often the standard way to go for modelbased clustering of continuous features. With the increasing availability of high-dimensional datasets, a methodological link between these two approaches has been established in order to provide a framework for performing penalized model-based clustering in the presence of large precision matrices. Notwithstanding, current methodologies do not account for the fact that groups may possess different degrees of association among the variables, thus implicitly assuming similar levels of sparsity across the classes. We overcome this limitation by deriving group-wise penalty factors, automatically enforcing under or over-connectivity in the estimated graphs. The approach is entirely data-driven and does not require any additional hyper-parameter specification. Simulated data experiments showcase the validity of our proposal.

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“…These two methods were demonstrated to have leading performance on simulated and real scRNA-seq data in a recent comparison [16]. In addition, our comparison also included glasso, a general statistical method for estimating sparse networks [34], and its variants have been used to address different challenges in gene network construction [33,45]. Finally, we also considered gene networks constructed by thresholding the Pearson or Spearman's correlation coefficients, as these are commonly used statistical measures for constructing gene co-expression networks [18].…”

Section: Sclink Demonstrates Efficiency In Simulation Studiesmentioning

confidence: 99%

“…Under this framework, the absence of an edge between two genes indicates the independence of these two genes conditioned on all other genes. Gaussian graphical models have been widely used to infer biological networks from genomic data and have revealed cancer-type-specific gene interactions that potentially contribute to cancer development and progression [31,32,33]. Third, scLink uses a penalized approach to identify relatively sparse gene networks in a data-adaptive manner, adjusting the penalty strength on each edge based on the observed co-expression strength in single cells.…”

Section: Introductionmentioning

confidence: 99%

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

2020

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A system-level understanding of the regulation and coordination mechanisms of gene expression is essential to understanding the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell-type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The source code used in this article is available at https://github.com/Vivianstats/scLink.

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Condition-adaptive fused graphical lasso (CFGL): an adaptive procedure for inferring condition-specific gene co-expression network

Cited by 5 publications

References 71 publications

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks

Group-wise shrinkage estimation in penalized model-based clustering

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

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