Principal network analysis: identification of subnetworks representing major dynamics using gene expression data

Kim, Yongsoo; Kim, Taek‐Kyun; Kim, Yungu; Yoo, Jun-Hyun; You, Sungyong; Lee, Inyoul; Carlson, George A.; Hood, Leroy; Choi, Seungjin; Hwang, Daehee

doi:10.1093/bioinformatics/btq670

Cited by 49 publications

(48 citation statements)

References 20 publications

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“…Finally, the five normalized fold-change matrices were combined by matching the gene IDs in the individual matrices, resulting in a combined fold-change matrix. NMF was then applied to the combined fold-change matrix as described in Kim et al [9]. The number of clusters ( k ) was set to be 30.…”

Section: Methodsmentioning

confidence: 99%

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A Systems Approach to Rheumatoid Arthritis

You

Cho²,

Lee

et al. 2012

PLoS ONE

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Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily attacks synovial joints. Despite the advances in diagnosis and treatment of RA, novel molecular targets are still needed to improve the accuracy of diagnosis and the therapeutic outcomes. Here, we present a systems approach that can effectively 1) identify core RA-associated genes (RAGs), 2) reconstruct RA-perturbed networks, and 3) select potential targets for diagnosis and treatments of RA. By integrating multiple gene expression datasets previously reported, we first identified 983 core RAGs that show RA dominant differential expression, compared to osteoarthritis (OA), in the multiple datasets. Using the core RAGs, we then reconstructed RA-perturbed networks that delineate key RA associated cellular processes and transcriptional regulation. The networks revealed that synovial fibroblasts play major roles in defining RA-perturbed processes, anti-TNF-α therapy restored many RA-perturbed processes, and 19 transcription factors (TFs) have major contribution to deregulation of the core RAGs in the RA-perturbed networks. Finally, we selected a list of potential molecular targets that can act as metrics or modulators of the RA-perturbed networks. Therefore, these network models identify a panel of potential targets that will serve as an important resource for the discovery of therapeutic targets and diagnostic markers, as well as providing novel insights into RA pathogenesis.

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Section: Methodsmentioning

confidence: 99%

“…the gene showing the DEP in cluster i ) were selected as the ones whose adjusted P-values values are less than 0.05. We computed the adjusted P-values as described in Kim et al [9].…”

Section: Methodsmentioning

confidence: 99%

A Systems Approach to Rheumatoid Arthritis

You

Cho²,

Lee

et al. 2012

PLoS ONE

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“…Although conventional differential network analysis [11, 29–31] is limited to indicate the network differences between groups of samples (e.g., normal and disease samples), DEVC-net can further indicate the network differences among individual samples by the personalized dysfunctional gene network, and thus, it can enhance the phenotype identification, e.g., disease diagnosis or prognosis.…”

Section: Resultsmentioning

confidence: 99%

Unravelling personalized dysfunctional gene network of complex diseases based on differential network model

Zeng

Wang

et al. 2015

J Transl Med

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In the conventional analysis of complex diseases, the control and case samples are assumed to be of great purity. However, due to the heterogeneity of disease samples, many disease genes are even not always consistently up-/down-regulated, leading to be under-estimated. This problem will seriously influence effective personalized diagnosis or treatment. The expression variance and expression covariance can address such a problem in a network manner. But, these analyses always require multiple samples rather than one sample, which is generally not available in clinical practice for each individual. To extract the common and specific network characteristics for individual patients in this paper, a novel differential network model, e.g. personalized dysfunctional gene network, is proposed to integrate those genes with different features, such as genes with the differential gene expression (DEG), genes with the differential expression variance (DEVG) and gene-pairs with the differential expression covariance (DECG) simultaneously, to construct personalized dysfunctional networks. This model uses a new statistic-like measurement on differential information, i.e., a differential score (DEVC), to reconstruct the differential expression network between groups of normal and diseased samples; and further quantitatively evaluate different feature genes in the patient-specific network for each individual. This DEVC-based differential expression network (DEVC-net) has been applied to the study of complex diseases for prostate cancer and diabetes. (1) Characterizing the global expression change between normal and diseased samples, the differential gene networks of those diseases were found to have a new bi-coloured topological structure, where their non hub-centred sub-networks are mainly composed of genes/proteins controlling various biological processes. (2) The differential expression variance/covariance rather than differential expression is new informative sources, and can be used to identify genes or gene-pairs with discriminative power, which are ignored by traditional methods. (3) More importantly, DEVC-net is effective to measure the expression state or activity of different feature genes and their network or modules in one sample for an individual. All of these results support that DEVC-net indeed has a clear advantage to effectively extract discriminatively interpretable features of gene/protein network of one sample (i.e. personalized dysfunctional network) even when disease samples are heterogeneous, and thus can provide new features like gene-pairs, in addition to the conventional individual genes, to the analysis of the personalized diagnosis and prognosis, and a better understanding on the underlying biological mechanisms.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-015-0546-5) contains supplementary material, which is available to authorized users.

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“…Differential gene expression Differential gene co-expression Scale of differential network GNEA [60] Yes A set of interactive genes DEGAS [61] Yes Dys-regulated genes enriched on sub-networks DDN [62,63] Yes Local dependency network PNA [64] Yes Yes Major dynamic activation patterns within sub-networks DNE [65] Yes Yes Local change of differential network entropy DEN [66] Yes Yes Global differential expression network PMN [67] Yes Yes Network modules, module network and its re-organizations…”

Section: Methodsmentioning

confidence: 99%

“…Based on a local dependency model, DDN detects significant topological changes in the transcriptional networks between two biological conditions, rather than changes in their expression levels [62,63]. By contrast, principal network analysis (PNA) captures the major dynamic activation patterns and their associated protein and metabolic sub-networks under multiple conditions [64]. Meanwhile, West et al [65] found that cancer cells can be characterized by differential network entropy (DNE).…”

Section: Appendixmentioning

confidence: 99%

Edge biomarkers for classification and prediction of phenotypes

Zeng

Zhang

et al. 2014

Sci. China Life Sci.

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In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed. These biomarkers are classified in terms of their contained information. In particular, we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample. First, we categorize the biomarkers based on the information used in the learning and prediction steps. We then briefly introduce conventional node biomarkers, or molecular biomarkers without network information, and their computational approaches. The main focus of this paper is edge and network biomarkers, which exploit network information to improve the accuracy of diagnosis and prognosis. Moreover, by extracting both network and dynamic information from the data, we can develop dynamical network and edge biomarkers. These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state. The identified critical molecules can be used as drug targets, and the critical state indicates the critical point of disease control. The paper also discusses representative biomarker-based methods.biomarker, edge biomarker, dynamical network biomarker, classification, prediction, phenotype, disease diagnosis, disease prognosis Citation:

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Principal network analysis: identification of subnetworks representing major dynamics using gene expression data

Abstract: The web-based software is available at: http://sbm.postech.ac.kr/pna.

Cited by 49 publications

References 20 publications

A Systems Approach to Rheumatoid Arthritis

A Systems Approach to Rheumatoid Arthritis

Unravelling personalized dysfunctional gene network of complex diseases based on differential network model

Edge biomarkers for classification and prediction of phenotypes

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