Gene set correlation analysis and visualization using gene expression data
Journal
Current Bioinformatics
Journal Volume
16
Journal Issue
3
Pages
406-421
Date Issued
2021
Author(s)
Chen J.J.
Abstract
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.
Subjects
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
SDGs
Type
journal article