https://scholars.lib.ntu.edu.tw/handle/123456789/605779
標題: | Gene set correlation analysis and visualization using gene expression data | 作者: | CHEN-AN TSAI Chen J.J. |
關鍵字: | Co-inertia analysis;Covariance;Gene set analysis;Gene set correlation analysis;Gene set enrichment analyses;P53 gene expression data;protein p53;Article;comparative study;gene construct;gene expression;gene expression profiling;gene identification;gene mapping;gene mutation;gene repression;gene set correlation analysis;gene structure;genetic analysis;genetic association;genetic cross;genetic transcription;genetic variation;hierarchical clustering;human;lymphoblastoid cell line;overlapping gene;phenotype;phylogenetic tree;RNA sequencing;simulation;X chromosome inactivation | 公開日期: | 2021 | 卷: | 16 | 期: | 3 | 起(迄)頁: | 406-421 | 來源出版物: | Current Bioinformatics | 摘要: | 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 the 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 co-structure 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 co-relationships 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 between and within gene sets and their interaction and network. We then demonstrate the 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 the identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis. ? 2021 Bentham Science Publishers. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107596644&doi=10.2174%2f1574893615999200629124444&partnerID=40&md5=d63a59befe2997c486e97f86e5cde8cf https://scholars.lib.ntu.edu.tw/handle/123456789/605779 |
ISSN: | 15748936 | DOI: | 10.2174/1574893615999200629124444 |
顯示於: | 農藝學系 |
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