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  3. Epidemiology and Preventive Medicine / 流行病學與預防醫學研究所
  4. Network hub-node prioritization of gene regulation with intra-network association
 
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Network hub-node prioritization of gene regulation with intra-network association

Journal
BMC Bioinformatics
Journal Volume
21
Journal Issue
1
Date Issued
2020
Author(s)
Chang H.-C.
Chu C.-P.
Lin S.-J.
CHUHSING KATE HSIAO  
DOI
10.1186/s12859-020-3444-7
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081901041&doi=10.1186%2fs12859-020-3444-7&partnerID=40&md5=b900877514d5a4a029aa36961de4763e
https://scholars.lib.ntu.edu.tw/handle/123456789/604905
Abstract
BACKGROUND: To identify and prioritize the influential hub genes in a gene-set or biological pathway, most analyses rely on calculation of marginal effects or tests of statistical significance. These procedures may be inappropriate since hub nodes are common connection points and therefore may interact with other nodes more often than non-hub nodes do. Such dependence among gene nodes can be conjectured based on the topology of the pathway network or the correlation between them. RESULTS: Here we develop a pathway activity score incorporating the marginal (local) effects of gene nodes as well as intra-network affinity measures. This score summarizes the expression levels in a gene-set/pathway for each sample, with weights on local and network information, respectively. The score is next used to examine the impact of each node through a leave-one-out evaluation. To illustrate the procedure, two cancer studies, one involving RNA-Seq from breast cancer patients with high-grade ductal carcinoma in situ and one microarray expression data from ovarian cancer patients, are used to assess the performance of the procedure, and to compare with existing methods, both ones that do and do not take into consideration correlation and network information. The hub nodes identified by the proposed procedure in the two cancer studies are known influential genes; some have been included in standard treatments and some are currently considered in clinical trials for target therapy. The results from simulation studies show that when marginal effects are mild or weak, the proposed procedure can still identify causal nodes, whereas methods relying only on marginal effect size cannot. CONCLUSIONS: The NetworkHub procedure proposed in this research can effectively utilize the network information in combination with local effects derived from marker values, and provide a useful and complementary list of recommendations for prioritizing causal hubs.
SDGs

[SDGs]SDG3

Publisher
BioMed Central Ltd.
Type
journal article

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