bio-de-visualization

To generate publication-ready plots from DESeq2 or edgeR RNA-seq results, use this Skill to create MA plots, heatmaps, PCA plots, and dispersion plots. It provides an end-to-end workflow using R libraries like DESeq2, ggplot2, and pheatmap.

For RNA-seq differential expression visualizations across multiple conditions, you can create MA plots, heatmaps, PCA plots, dispersion plots, and counts plots. These visuals help identify top DE genes and compare per-sample expression patterns.

Yes, you can visualize top differentially expressed genes in RNA-seq data using heatmaps. The Skill uses the pheatmap R library to generate per-sample expression visuals, helping you quickly understand and communicate DE patterns from your analysis.

Yes, this RNA-seq visualization workflow supports generating PCA plots colored by sample condition. It includes preprocessing steps and plotting functions to compare multiple conditions across samples directly from DESeq2 or edgeR outputs.

To plot RNA-seq differential expression results without manual coding, you need outputs from DESeq2 or edgeR analysis. The Skill handles preprocessing and uses R libraries like ggplot2 and pheatmap internally to produce saved visual outputs automatically.

If your RNA-seq DESeq2 plots are not displaying clearly, this Skill provides guidance on interpreting visuals and troubleshooting plotting issues. It ensures robust data exploration by applying proper preprocessing steps before generating MA plots and heatmaps.