scholarly journals Discovering tightly regulated and differentially expressed gene sets in whole genome expression data

2007 ◽  
Vol 23 (2) ◽  
pp. e84-e90 ◽  
Author(s):  
C. Ye ◽  
E. Eskin
Keyword(s):  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Whole Genome ◽  
Expression Data ◽  
Genome Expression ◽  
Gene Sets ◽  
Whole Genome Expression

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Gene Sets ◽  
Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

A computational analysis of whole-genome expression data reveals chromosomal domains of gene expression

10.1038/79896 ◽  
2000 ◽  
Vol 26 (2) ◽  
pp. 183-186 ◽  
Author(s):  
Barak A. Cohen ◽  
Robi D. Mitra ◽  
Jason D. Hughes ◽  
George M. Church
Keyword(s):  
Gene Expression ◽  
Computational Analysis ◽  
Whole Genome ◽  
Expression Data ◽  
Genome Expression ◽  
Whole Genome Expression

scholarly journals What can whole genome expression data tell us about the ecology and evolution of personality?

2010 ◽  
Vol 365 (1560) ◽  
pp. 4001-4012 ◽  
Author(s):  
Alison M. Bell ◽  
Nadia Aubin-Horth
Keyword(s):  
Molecular Mechanisms ◽  
Behavioural Plasticity ◽  
Whole Genome ◽  
Expression Data ◽  
Proximate Mechanisms ◽  
Genome Expression ◽  
Individual Consistency ◽  
Evolutionary Consequences ◽  
Whole Genome Expression ◽  
Behavioural Correlations

Consistent individual differences in behaviour, aka personality, pose several evolutionary questions. For example, it is difficult to explain within-individual consistency in behaviour because behavioural plasticity is often advantageous. In addition, selection erodes heritable behavioural variation that is related to fitness, therefore we wish to know the mechanisms that can maintain between-individual variation in behaviour. In this paper, we argue that whole genome expression data can reveal new insights into the proximate mechanisms underlying personality, as well as its evolutionary consequences. After introducing the basics of whole genome expression analysis, we show how whole genome expression data can be used to understand whether behaviours in different contexts are affected by the same molecular mechanisms. We suggest strategies for using the power of genomics to understand what maintains behavioural variation, to study the evolution of behavioural correlations and to compare personality traits across diverse organisms.

scholarly journals Whole Genome Expression Profiling and Signal Pathway Screening of MSCs in Ankylosing Spondylitis

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yuxi Li ◽  
Peng Wang ◽  
Zhongyu Xie ◽  
Lin Huang ◽  
Rui Yang ◽  
...  
Keyword(s):  
Ankylosing Spondylitis ◽  
Expression Profiling ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Whole Genome ◽  
Tlr Signaling ◽  
Genome Expression ◽  
Kegg Pathway Analysis ◽  
Key Genes ◽  
Whole Genome Expression

The pathogenesis of dysfunctional immunoregulation of mesenchymal stem cells (MSCs) in ankylosing spondylitis (AS) is thought to be a complex process that involves multiple genetic alterations. In this study, MSCs derived from both healthy donors and AS patients were cultured in normal media or media mimicking an inflammatory environment. Whole genome expression profiling analysis of 33,351 genes was performed and differentially expressed genes related to AS were analyzed by GO term analysis and KEGG pathway analysis. Our results showed that in normal media 676 genes were differentially expressed in AS, 354 upregulated and 322 downregulated, while in an inflammatory environment 1767 genes were differentially expressed in AS, 1230 upregulated and 537 downregulated. GO analysis showed that these genes were mainly related to cellular processes, physiological processes, biological regulation, regulation of biological processes, and binding. In addition, by KEGG pathway analysis, 14 key genes from the MAPK signaling and 8 key genes from the TLR signaling pathway were identified as differentially regulated. The results of qRT-PCR verified the expression variation of the 9 genes mentioned above. Our study found that in an inflammatory environment ankylosing spondylitis pathogenesis may be related to activation of the MAPK and TLR signaling pathways.

scholarly journals Whole Genome Expression Profiles of Yeast RNA Polymerase II Core Subunit, Rpb4, in Stress and Nonstress Conditions

2002 ◽  
Vol 278 (5) ◽  
pp. 3339-3346 ◽  
Author(s):  
Beena Pillai ◽  
Jiyoti Verma ◽  
Anju Abraham ◽  
Princy Francis ◽  
Yadunanda Kumar ◽  
...  
Keyword(s):  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Expression Profiles ◽  
Whole Genome ◽  
Genome Expression ◽  
Whole Genome Expression
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