CD Genomics is able to use gene set enrichment analysis to help customers efficiently and accurately detect gene expression levels from the vast amount of gene expression information, identify potential gene functions, and uncover their roles in the life process.
Introduction of Gene Set Enrichment Analysis
A gene set is a group of genes on a gene chip that have the same biological function or are located in the same biological channel. Gene set enrichment analysis is an enrichment analysis method based on gene sets. The principle is to select a functional gene set or a set of functional genes for the purpose of gene expression data analysis, and then rank the correlation between gene expression data and phenotype to determine the effect of synergistic changes of genes within the gene set on phenotype changes.
Figure 1. A GSEA overview illustrating the method. (Subramanian A, et al., 2005)
Deliverables
- Enrichment score folding line chart.
- Location map of functionally annotated gene sets.
- Gene set enrichment analysis ridge map.
- Advanced Venn diagram for gene set enrichment analysis.
- Network diagram for gene set enrichment analysis.
- Gene set enrichment analysis bar chart.
Advantages of Gene Set Enrichment Analysis
Gene Set Enrichment Analysis is not limited to differential genes and does not require specified thresholds to screen for differential genes, allowing analysis of gene sets of interest without threshold restrictions and avoiding the omission of biologically important genes with insignificant differential expression.
CD Genomics Gene Set Enrichment Analysis Pipeline
Gene Set Enrichment Analysis Content
The purpose of gene set enrichment analysis is to screen out gene sets with differential expression levels between two or more groups. Currently, the commonly used methods for gene set enrichment analysis include bottom-up and top-down methods.
| Calculation of statistics at the single gene level |
Fold change |
| Signal-to-noise ratio value |
| Correlation coefficient |
| Coefficient of ANOVA or linear regression |
| Regularized t-statistics |
| Statistical transformations |
No conversion |
| Absolute values |
| Square values |
| Binary transformation |
| P-values or Bayesian posterior probabilities |
| False discovery rate |
| Selection of the original hypothesis |
Competitive original hypothesis |
| Self-limiting original hypothesis |
| Calculate the statistics of the gene set |
The sum or the mean of the transformed single gene statistics |
| The median of the transformed single gene statistics |
| Kolmogorov-Smirnov statistics |
| The max mean statistic |
| Wilcoxon rank sum test statistic |
| Significance evaluation |
Genes as replicate sampling units |
| Sample as a repeat sampling unit |
| Both genes and samples are used as replicate sampling units |
We are able to use gene set enrichment analysis services to help our customers achieve high throughput data analysis in multi-omics. For questions about analysis content, project cycle, and pricing, please click online inquiry.
How It Works
CD Genomics is a professional bioinformatics service provider with years of experience in NGS and long-read sequencing (PacBio SMRT and Oxford Nanopore platforms) data, proteomics and metabolomics data analysis, integrated analysis services, database construction, and other bioinformatics solutions.
CD Genomics has professional bioinformatics experts who have successfully provided gene set enrichment analysis to researchers in many different fields. Our professional skills and enthusiasm will provide you with high-quality analysis services. If you are interested in our services, please contact us for more details.
Reference
- Subramanian A, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005 Oct 25; 102(43): 15545-50.
