Figure 1. Demo result of GSEA analysis.
Demo result interpretation
The map shows the ES value of GO obtained after GSEA analysis of the gene set. The curve in the upper part of the graph represents the dynamic ES value, and the highest point represents the ES value of this GO. The more significant the ES, the greater the impact on the gene set.
Introduction
GSEA (Gene Set Enrichment Analysis) is a computational method used to determine whether a pre-defined gene set can show significant consistency differences in two biological states.
Pre-defined gene set: A gene set contains genes of interest, such as a certain pathway, a certain GO term, or hall marker gene set. Two biological states: the experimental group and the control group. It can be cancer and normal, male and female. Consistency differences: the genes in the pre-defined gene set show similar differences in two biological states; that is, the gene set in a certain pathway/GO entry is in the experimental group. It shows a consistent upward or downward trend with the control group.
Overall solutions
In the method that is typically referred to as standard GSEA, there are three steps involved in the analytical process.The general steps are summarized below:
- Calculate the enrichment score (ES) that represents the amount to which the genes in the set are over-represented at either the top or bottom of the list. This score is a Kolmogorov–Smirnov-like statistic.
- Estimate the statistical significance of the ES. This calculation is done by a phenotypic-based permutation test in order to produce a null distribution for the ES. The P value is determined by comparison to the null distribution.
- Calculating significance this way tests for the dependence of the gene set on the diagnostic/phenotypic labels.
- Adjust for multiple hypothesis testing for when a large number of gene sets are being analyzed at one time. The enrichment scores for each set are normalized and a false discovery rate is calculated.
Features
- Routine enrichment analysis must first perform differential screening, and use the screened genes (no matter how many) to perform functional enrichment. This method may miss some key information due to unreasonable screening parameters.
- No other analysis is needed when using the GSEA analysis. The advantage is that the key information can be retained without screening the differences, and then find those functional gene sets that are not very different but have consistent genetic differences.
GSEA analysis
| Project name |
GSEA analysis |
| Samples requirement |
Our network analysis services in biology require you to provide specific requirements. |
| Timeline |
Decide according to your needs. |
| Deliverables |
We provide you with raw data and modeling results. |
| Price |
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Analysis requirements
Two groups of mRNA expression data (differential gene or gene of interest).
The results we provide:
- Image files (Network diagrams), including png, PDF formats.
- Text file: ES, NES, pvalue, FDR of each gene set (Excel, html format)
Applications
- Genome-wide association studies
- Spontaneous preterm birth
- Cancer cell profiling
- Schizophrenia
- Depression
CD ComputaBio provides corresponding GSEA analysis as proven to be very useful for understanding the biochemical basis of physiological events at different stages of drug development (even in different fields such as materials science). CD ComputaBio team has been working in this field for more than ten years and has published his findings in top scientific journals. If you have a need for network analysis services, please feel free to contact us.
We provide a variety of modeling services, but not limited to:
* For Research Use Only.
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