GEO (Gene Expression Omnibus) Dataset Analysis involves examining gene expression data to identify significant genes linked to diseases. This process leverages online tools and bioinformatics software to retrieve, filter, normalize, compare, annotate, and visualize gene expression profiles. The goal is to find meaningful gene sets that could help understand disease mechanisms or identify potential biomarkers.
GEO2R is a user-friendly web tool for quick gene expression analysis of GEO datasets.
Steps
1. Load your GEO dataset into GEO2R.
2. The tool automatically generates visualizations such as volcano plots and UMAP plots.
3. You can download these plots for further examination.
4. GEO2R also allows the download of processed data tables in Excel format for detailed analysis.
5. If GEO2R results seem non-significant, proceed to use an RStudio script for more advanced analysis.
When GEO2R does not yield significant gene results, RStudio provides more flexibility and control by allowing you to run customized scripts.
Steps
1. Go to the GEO2R page of your dataset and copy the analysis script provided.
2. Open RStudio and paste the copied script into the console or script editor.
3. Run the script to generate your own results.
4. Start with a significance threshold (p-value) of 0.05. If necessary, gradually lower it (e.g., 0.04, 0.03) to obtain a sufficient number of significant genes for further study.
After identifying significant genes, various tools help you compare datasets and explore biological meanings.
1. Compare Gene Sets:
Use online tools like Venny to find overlaps between upregulated and downregulated gene lists from different datasets.
2. Visualize Common Genes:
Create heat maps to illustrate expression patterns of commonly identified genes.
3. Annotate Gene Functions:
Conduct Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) using tools that provide information about molecular function (MF), biological function (BF), cellular components (CC), and KEGG pathways.
4. Explore Protein Interactions:
Input your significant genes into the STRING database to assess possible protein-protein interactions.
Adjust confidence thresholds if no interactions are initially found to increase detection sensitivity.
5. Drug-Gene Interaction Analysis:
Use DGIdb to identify drugs that target your significant genes.
Download results for virtual screening or molecular docking studies.
6. Validate Disease Relevance:
Perform text mining via MalaCards to link the common genes with relevant diseases, helping prioritize targets for further research.