bio-data-visualization-specialized-omics-plots

Version Compatibility

Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, ggplot2 3.5+, matplotlib 3.8+, numpy 1.26+, scanpy 1.10+, scikit-learn 1.4+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Specialized Omics Plots

"Create omics-specific plots" → Generate MA plots, PCA biplots, sample correlation heatmaps, and other domain-specific visualizations for genomics data.

• Python: scanpy.pl.pca(), matplotlib custom plots
• R: DESeq2::plotMA(), PCAtools::biplot()

Scope

This skill provides reusable plotting functions for common omics visualizations that can be applied across different analysis types:

• Volcano plots (any DE result)
• MA plots (any log-fold-change data)
• PCA plots (any high-dimensional data)
• Enrichment dotplots (manual, not enrichplot)
• Expression boxplots with statistics
• Survival curves

For DESeq2/edgeR built-in functions (plotMA, plotPCA, plotDispEsts), see differential-expression/de-visualization. For enrichplot-specific functions (dotplot, cnetplot, emapplot, gseaplot2), see pathway-analysis/enrichment-visualization.

Volcano Plot (R)

library(ggplot2)
library(ggrepel)

volcano_plot <- function(res, fdr = 0.05, lfc = 1, top_n = 10) {
    res <- res %>%
        mutate(
            significance = case_when(
                padj < fdr & log2FoldChange > lfc ~ 'Up',
                padj < fdr & log2FoldChange < -lfc ~ 'Down',
                TRUE ~ 'NS'
            ),
            label = ifelse(rank(padj) <= top_n & significance != 'NS', gene, '')
        )

    ggplot(res, aes(log2FoldChange, -log10(pvalue), color = significance)) +
        geom_point(alpha = 0.6, size = 1.5) +
        geom_text_repel(aes(label = label), color = 'black', size = 3, max.overlaps = 20) +
        scale_color_manual(values = c('Up' = '#E64B35', 'Down' = '#4DBBD5', 'NS' = 'grey60')) +
        geom_vline(xintercept = c(-lfc, lfc), linetype = 'dashed', color = 'grey40') +
        geom_hline(yintercept = -log10(fdr), linetype = 'dashed', color = 'grey40') +
        labs(x = expression(Log[2]~Fold~Change), y = expression(-Log[10]~P-value)) +
        theme_bw() + theme(panel.grid = element_blank())
}