bio-data-visualization-specialized-omics-plots

To create a PCA plot from omics data, this Skill utilizes ggplot2 to visualize sample relationships and identify expression patterns. It helps you explore dimensionality reduction to assess sample clustering across experimental conditions.

Yes, you can generate heatmaps for sample distance matrices and gene expression patterns. This Skill provides specialized visualization tools to map sample relationships and highlight significant gene expression variations across your experimental conditions.

Yes, the toolkit supports built-in DESeq2 and edgeR plotting functions alongside custom ggplot2 implementations. You can generate standard dispersion plots and MA plots directly from your differential expression analysis outputs.

The best way to visualize enrichment analysis results is by generating dotplots through this Skill's enrichment visualization capabilities. It processes omics enrichment data to create clear, publication-quality representations of your pathway analysis.

Using custom ggplot2 implementations for MA plots ensures your differential expression visualizations are publication-ready with refined aesthetics. This approach provides better customization for highlighting significant gene expression changes compared to base R graphics.