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Gene Set Enrichment Analysis

Gene Set Enrichment Analysis Online Inquiry

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.

A GSEA overview illustrating the method.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

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.

How It Works

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

  1. 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.
* For Research Use Only. Not for use in diagnostic procedures.
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