DEA
DEA Help
Differential Expression Analysis Overview
Differential Expression Analysis (DEA) identifies genes that are significantly up- or down-regulated between different cell clusters, helping to characterize cell types and states within your single-cell dataset.
Parameter Configuration
To begin your DEA, configure the following parameters:
- (1) Number of Genes: Select the number of genes to be plotted per cluster (maximum of 8)
- (2) Statistical Method: Choose the statistical method to be employed (Wilcoxon or t-test)
- (3) Custom Gene List (Optional): Define specific genes you want to visualize
Once your selections are made, click Run (4) to start the analysis.
By default, the analysis filters results to display only genes with an adjusted p-value < 0.05. For each gene and cluster, the following are computed:
- Mean expression: Average expression among expressing cells
- Log fold change (logFC): Comparing expression in the target cluster vs. all other clusters
Only genes with a logFC ≥ 0.25 and mean expression ≥ 0.1 are considered for visualization, unless a custom list is provided.
Results and Visualization
After running the analysis, you will see:
- (5) Download Link: A link titled "Differential Expression Analysis" will appear above, allowing you to download the complete DEA results per cluster
- (6) Dot Plot Visualizations: Two dot plot visualizations will be generated below
Understanding Dot Plots:
- Dot color: Indicates whether the gene is up- or down-regulated
- Dot size: Reflects the percentage of cells within each cluster that express the gene
Finally, click Continue to Visualization (7) to plot selected genes in UMAP or PCA space for spatial insight.
