Differential network analysis: A statistical perspective

Shojaie, Ali

doi:10.1002/wics.1508

Cited by 46 publications

(54 citation statements)

References 126 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Statistical networks provide a convenient framework for representing the interactions between multiple genes (or other molecular features). Differential network analysis (DiNA) quantifies how this network structure differs between two or more groups/phenotypes (e.g., disease subjects and healthy controls), and is a growing field of research ( de la Fuente, 2010 ; Kayano et al, 2014 ; Singh et al, 2018 ; Shojaie, 2020 ). One major application of DiNA is to identify “modules” (subsets of 3 or more genes), where the network connections within a module differ between phenotype groups, known as differentially co-expressed modules (DCMs).…”

Section: Discussionmentioning

confidence: 99%

“…(1) Given that the TPRs of methods depends on the true network structure, it would be interesting to consider methods that combine multiple test statistics, in order to increase sensitivity across a greater variety of network structures. (2) Further research is needed for comparing other types of similarity measures for constructing the test statistics, such as various types of “conditional” partial correlation measures ( Shojaie, 2020 ), or settings where using the TOM may improve power compared to correlation (3). Although one may use predefined modules from an existing database (e.g., KEGG, Kanehisa and Goto, 2000 ; GO; Ashburner et al, 2000 ), further research is needed to compare clustering methods for deriving data dependent modules, and determining the optimal number of modules.…”

Section: Discussionmentioning

confidence: 99%

“…As previously mentioned, several similarity measures have been used in practice: correlation (Pearson, Spearman, or Kendall), partial correlation, mutual information ( Gill et al, 2010 ; Kumari et al, 2012 ; Kayano et al, 2014 ; van Dam et al, 2018 ), and adjacency or TOM matrices ( Zhang and Horvath, 2005 ; Langfelder and Horvath, 2008 ). Gaussian and semi/nonparametric graphical models have also been used to measure the conditional dependence between each pair of genes (i.e., partial correlations) ( Friedman et al, 2008 ; Wang et al, 2016 ; Zhang et al, 2018 ; Shojaie, 2020 ). It is beyond the scope of this paper to compare all of these similarity measures for constructing networks.…”

Section: Methodsmentioning

confidence: 99%

“…In differential network analysis (DiNA), the goal is to determine whether the network structure differs between two or more phenotype groups (see de la Fuente, 2010 ; Kayano et al, 2014 ; Singh et al, 2018 ; Shojaie, 2020 for review). Many of the methods of DiNA of gene co-expression networks can be classified into three categories: (1) Identifying a single node (gene) in the network where the connections at that node differ between phenotype groups.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparing Statistical Tests for Differential Network Analysis of Gene Modules

et al. 2021

View full text Add to dashboard Cite

Genes often work together to perform complex biological processes, and “networks” provide a versatile framework for representing the interactions between multiple genes. Differential network analysis (DiNA) quantifies how this network structure differs between two or more groups/phenotypes (e.g., disease subjects and healthy controls), with the goal of determining whether differences in network structure can help explain differences between phenotypes. In this paper, we focus on gene co-expression networks, although in principle, the methods studied can be used for DiNA for other types of features (e.g., metabolome, epigenome, microbiome, proteome, etc.). Three common applications of DiNA involve (1) testing whether the connections to a single gene differ between groups, (2) testing whether the connection between a pair of genes differs between groups, or (3) testing whether the connections within a “module” (a subset of 3 or more genes) differs between groups. This article focuses on the latter, as there is a lack of studies comparing statistical methods for identifying differentially co-expressed modules (DCMs). Through extensive simulations, we compare several previously proposed test statistics and a new p-norm difference test (PND). We demonstrate that the true positive rate of the proposed PND test is competitive with and often higher than the other methods, while controlling the false positive rate. The R package discoMod (differentially co-expressed modules) implements the proposed method and provides a full pipeline for identifying DCMs: clustering tools to derive gene modules, tests to identify DCMs, and methods for visualizing the results.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparing Statistical Tests for Differential Network Analysis of Gene Modules

et al. 2021

View full text Add to dashboard Cite

show abstract

“…This can reduce the high sample and computational requirements of current causal inference algorithms, since while the full regulatory network is often large and dense, the difference between two related regulatory networks is often small and sparse. As of now, this problem has only been addressed in the undirected setting, namely by KLIEP , DPM and others Lichtblau et al, 2017) that estimate differences between undirected graphs; for a recent review see Shojaie (2020).…”

Section: Introductionmentioning

confidence: 99%

DCI: learning causal differences between gene regulatory networks

2021

View full text Add to dashboard Cite

Summary Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale expression datasets from different conditions, cell types, disease states and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e., edges that appeared, disappeared or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions. Availability and implementation Python package freely available at http://uhlerlab.github.io/causaldag/dci Supplementary information Supplementary information is available at Bioinformatics online.

show abstract

Assisted differential network analysis for gene expression data

2021

Genetic Epidemiology

View full text Add to dashboard Cite

In the analysis of gene expression data, when there are two or more disease conditions/groups (e.g., diseased and normal, responder and nonresponder, and multiple stages/subtypes), differential analysis has been extensively conducted to identify key differences and has important implications. Network analysis takes a system perspective and can be more informative than that limited to simple statistics such as mean and variance. In differential network analysis, a common practice is to first estimate a gene expression network for each condition/group, and then spectral clustering can be applied to the network difference(s) to identify key genes and biological mechanisms that lead to the differences. Compared to “simple” analysis such as regression, differential network analysis can be more challenging with the significantly larger number of parameters. In this study, taking advantage of the increasing popularity of multidimensional profiling data, we develop an assisted analysis strategy and propose incorporating regulator information to improve the identification of key genes (that lead to the differences in gene expression networks). An effective computational algorithm is developed. Comprehensive simulation is conducted, showing that the proposed approach can outperform the benchmark alternatives in identification accuracy. With the The Cancer Genome Atlas lung adenocarcinoma data, we analyze the expressions of genes in the KEGG cell cycle pathway, assisted by copy number variation data. The proposed assisted analysis leads to identification results similar to the alternatives but different estimations. Overall, this study can deliver an efficient and cost‐effective way of improving differential network analysis.

show abstract

Differential network analysis: A statistical perspective

Cited by 46 publications

References 126 publications

Comparing Statistical Tests for Differential Network Analysis of Gene Modules

Comparing Statistical Tests for Differential Network Analysis of Gene Modules

DCI: learning causal differences between gene regulatory networks

Assisted differential network analysis for gene expression data

Contact Info

Product

Resources

About