A powerful score-based statistical test for group difference in weighted biological networks

Ji, Jiadong; Yuan, Zhongshang; Zhang, Xiaoshuai; Xue, Fuzhong

doi:10.1186/s12859-016-0916-x

Cited by 14 publications

(9 citation statements)

References 38 publications

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“…Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis. The identified differential interaction patterns between two group-specific biological networks can be taken as candidate biomarkers, and have extensive biomedical and clinical applications (Ji et al, 2015(Ji et al, , 2016Laenen et al, 2013;Yang et al, 2013). Although numerous differential network analysis methods (Fukushima, 2013;Ha et al, 2015;Watson, 2006;Yates and Mukhopadhyay, 2013;Zhao et al, 2014) have been proposed, most of the methods rely on marginal or partial correlation to measure the strength of connection between pairs of nodes in a network.…”

Section: Discussionmentioning

confidence: 99%

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

Feng

et al. 2017

Preprint

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Motivation: A complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application. Results: We propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.

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Section: Discussionmentioning

confidence: 99%

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

Feng

et al. 2017

Preprint

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“…The proposed statistic can be treated as the extension for directed network of our recent study 40 . Little attention has been paid on the biological network structure learning problem.…”

Section: Discussionmentioning

confidence: 99%

A powerful weighted statistic for detecting group differences of directed biological networks

Yuan

Zhang

et al. 2016

Sci Rep

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Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. Different physiological conditions such as cases and controls may manifest as different networks. Statistical comparison between biological networks can provide not only new insight into the disease mechanism but statistical guidance for drug development. However, the methods developed in previous studies are inadequate to capture the changes in both the nodes and edges, and often ignore the network structure. In this study, we present a powerful weighted statistical test for group differences of directed biological networks, which is independent of the network attributes and can capture the changes in both the nodes and edges, as well as simultaneously accounting for the network structure through putting more weights on the difference of nodes locating on relatively more important position. Simulation studies illustrate that this method had better performance than previous ones under various sample sizes and network structures. One application to GWAS of leprosy successfully identifies the specific gene interaction network contributing to leprosy. Another real data analysis significantly identifies a new biological network, which is related to acute myeloid leukemia. One potential network responsible for lung cancer has also been significantly detected. The source R code is available on our website.

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“…It is particularly challenging to quantify inter-node connection strength precisely with a unified metric, especially when involving group (e.g., patients versus healthy controls) differences in biological networks (Gambardella et al, 2013;Yates and Mukhopadhyay, 2013;Ruan et al, 2015). In an attempt to accommodate changes in nodes and edges which lead to network differences, we previously developed statistics to test the group difference for weighted biological networks (Ji et al, 2016), for pathways with chain structure (Ji et al, 2015;Yuan et al, 2016a) and for directed biological networks (Yuan et al, 2016b). Nevertheless, these methods have little capacity to adjust for potential confounding factors and covariates (e.g., age, sex, batch effect), which served as a motivation for the current investigation into network regression techniques to infer the effect of a biological network as a whole (i.e., treating the whole network as the independent variables), accounting for the potential confounders through a regression model.…”

Section: Introductionmentioning

confidence: 99%

PMINR: Pointwise Mutual Information-Based Network Regression – With Application to Studies of Lung Cancer and Alzheimer’s Disease

Lin

Zhu

et al. 2020

Front. Genet.

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Complex diseases are believed to be the consequence of intracellular network(s) involving a range of factors. An improved understanding of a disease-predisposing biological network could lead to better identification of genes and pathways that confer disease risk and therefore inform drug development. The group difference in biological networks, as is often characterized by graphs of nodes and edges, is attributable to effects of these nodes and edges. Here we introduced pointwise mutual information (PMI) as a measure of the connection between a pair of nodes with either a linear relationship or nonlinear dependence. We then proposed a PMI-based network regression (PMINR) model to differentiate patterns of network changes (in node or edge) linking a disease outcome. Through simulation studies with various sample sizes and inter-node correlation structures, we showed that PMINR can accurately identify these changes with higher power than current methods and be robust to the network topology. Finally, we illustrated, with publicly available data on lung cancer and gene methylation data on aging and Alzheimer's disease, an evaluation of the practical performance of PMINR. We concluded that PMI is able to capture the generic inter-node correlation pattern in biological networks, and PMINR is a powerful and efficient approach for biological network analysis.

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A powerful score-based statistical test for group difference in weighted biological networks

Cited by 14 publications

References 38 publications

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

A powerful weighted statistic for detecting group differences of directed biological networks

PMINR: Pointwise Mutual Information-Based Network Regression – With Application to Studies of Lung Cancer and Alzheimer’s Disease

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