Network Analysis of Microarray Data

2021 ◽  
pp. 161-186
Author(s):  
Alisa Pavel ◽  
Angela Serra ◽  
Luca Cattelani ◽  
Antonio Federico ◽  
Dario Greco
Keyword(s):  
Network Analysis ◽  
Microarray Data

Expression and network analysis of Illumina microarray data

2009 ◽  
Author(s):  
Giovanni Coppola ◽  
Kellen Winden ◽  
Genevieve Konopka ◽  
Fuying Gao ◽  
Daniel Geschwind
Keyword(s):  
Network Analysis ◽  
Microarray Data

scholarly journals Identification of Candidate Genes and MicroRNAs for Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Li ◽  
Xiao_nan He ◽  
Chao Li ◽  
Ling Gong ◽  
Min Liu
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Differentially Expressed ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Background. Identification of potential molecular targets of acute myocardial infarction is crucial to our comprehensive understanding of the disease mechanism. However, studies of gene coexpression analysis via jointing multiple microarray data of acute myocardial infarction still remain restricted. Methods. Microarray data of acute myocardial infarction (GSE48060, GSE66360, GSE97320, and GSE19339) were downloaded from Gene Expression Omnibus database. Three data sets without heterogeneity (GSE48060, GSE66360, and GSE97320) were subjected to differential expression analysis using MetaDE package. Differentially expressed genes having upper 25% variation across samples were imported in weighted gene coexpression network analysis. Functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID. The predicted microRNAs to regulate target genes in the most significant module were identified using TargetScan. Moreover, subpathway analyses using iSubpathwayMiner package and GenCLiP 2.0 were performed on hub genes with high connective weight in the most significant module. Results. A total of 1027 differentially expressed genes and 33 specific modules were screened out between acute myocardial infarction patients and control samples. Ficolin (collagen/fibrinogen domain containing) 1 (FCN1), CD14 molecule (CD14), S100 calcium binding protein A9 (S100A9), and mitochondrial aldehyde dehydrogenase 2 (ALDH2) were identified as critical target molecules; hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential regulators of the expression of the key genes in the two biggest modules. Conclusions. FCN1, CD14, S100A9, ALDH2, hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential candidate regulators in acute myocardial infarction. These findings might provide new comprehension into the underlying molecular mechanism of disease.

scholarly journals Elucidating functional context within microarray data by integrated transcription factor-focused gene-interaction and regulatory network analysis

2013 ◽  
Vol 24 (2) ◽  
pp. 75-90 ◽  
Author(s):  
Thomas Werner ◽  
Susan M. Dombrowski ◽  
Carlos Zgheib ◽  
Fouad A. Zouein ◽  
Henry L. Keen ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Network Analysis ◽  
Microarray Data ◽  
Regulatory Network ◽  
Gene Interaction ◽  
Functional Context

scholarly journals Co-expression Network Analysis of Human lncRNAs and Cancer Genes

2014 ◽  
Vol 13s5 ◽  
pp. CIN.S14070 ◽  
Author(s):  
Steven B. Cogill ◽  
Liangjiang Wang
Keyword(s):  
Network Analysis ◽  
Microarray Data ◽  
Blood Cells ◽  
Somatic Mutations ◽  
Expression Profiles ◽  
Noncoding Rnas ◽  
Cancer Genes ◽  
Data Set ◽  
The Third ◽  
Potential Cancer

We used gene co-expression network analysis to functionally annotate long noncoding RNAs (lncRNAs) and identify their potential cancer associations. The integrated microarray data set from our previous study was used to extract the expression profiles of 1,865 lncRNAs. Known cancer genes were compiled from the Catalogue of Somatic Mutations in Cancer and UniProt databases. Co-expression analysis identified a list of previously uncharacterized lncRNAs that showed significant correlation in expression with core cancer genes. To further annotate the lncRNAs, we performed a weighted gene co-expression network analysis, which resulted in 37 co-expression modules. Three biologically interesting modules were analyzed in depth. Two of the modules showed relatively high expression in blood and brain tissues, whereas the third module was found to be downregulated in blood cells. Hub lncRNA genes and enriched functional annotation terms were identified within the modules. The results suggest the utility of this approach as well as potential roles of uncharacterized lncRNAs in leukemia and neuroblastoma.

scholarly journals Co-expression network analysis of Down's syndrome based on microarray data

2016 ◽  
Vol 12 (3) ◽  
pp. 1503-1508 ◽  
Author(s):  
Jianping Zhao ◽  
Zhengguo Zhang ◽  
Shumin Ren ◽  
Yanan Zong ◽  
Xiangdong Kong
Keyword(s):  
Network Analysis ◽  
Microarray Data ◽  
Down's Syndrome ◽  
Down’S Syndrome
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