Bio data visualization specialized omics plots skills 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())
}

Volcano Plot (Python)

import matplotlib.pyplot as plt
import numpy as np

def volcano_plot(df, fdr=0.05, lfc=1, ax=None):
    if ax is None:
        fig, ax = plt.subplots(figsize=(8, 6))

    sig_up = (df['padj'] < fdr) & (df['log2FoldChange'] > lfc)
    sig_down = (df['padj'] < fdr) & (df['log2FoldChange'] < -lfc)
    ns = ~(sig_up | sig_down)

    ax.scatter(df.loc[ns, 'log2FoldChange'], -np.log10(df.loc[ns, 'pvalue']),
               c='grey', alpha=0.5, s=10, label='NS')
    ax.scatter(df.loc[sig_up, 'log2FoldChange'], -np.log10(df.loc[sig_up, 'pvalue']),
               c='#E64B35', alpha=0.7, s=15, label='Up')
    ax.scatter(df.loc[sig_down, 'log2FoldChange'], -np.log10(df.loc[sig_down, 'pvalue']),
               c='#4DBBD5', alpha=0.7, s=15, label='Down')

    ax.axhline(-np.log10(fdr), ls='--', c='grey', lw=0.8)
    ax.axvline(-lfc, ls='--', c='grey', lw=0.8)
    ax.axvline(lfc, ls='--', c='grey', lw=0.8)

    ax.set_xlabel('Log2 Fold Change')
    ax.set_ylabel('-Log10 P-value')
    ax.legend()
    return ax

MA Plot

ma_plot <- function(res, fdr = 0.05) {
    res <- res %>%
        mutate(significant = padj < fdr & !is.na(padj))

    ggplot(res, aes(log10(baseMean), log2FoldChange, color = significant)) +
        geom_point(alpha = 0.5, size = 1) +
        scale_color_manual(values = c('FALSE' = 'grey60', 'TRUE' = '#E64B35')) +
        geom_hline(yintercept = 0, color = 'black', linewidth = 0.5) +
        labs(x = expression(Log[10]~Mean~Expression), y = expression(Log[2]~Fold~Change)) +
        theme_bw() + theme(panel.grid = element_blank(), legend.position = 'none')
}

PCA Plot (R)

Goal: Create a PCA scatter plot from a variance-stabilized expression matrix, colored by experimental condition.

Approach: Select the top most-variable genes, run PCA on transposed assay data, extract variance-explained percentages, and plot PC1 vs PC2 with 95% confidence ellipses per group.

pca_plot <- function(vsd, intgroup = 'condition', ntop = 500) {
    rv <- rowVars(assay(vsd))
    select <- order(rv, decreasing = TRUE)[seq_len(min(ntop, length(rv)))]
    pca <- prcomp(t(assay(vsd)[select, ]))
    percentVar <- round(100 * pca$sdev^2 / sum(pca$sdev^2), 1)

    pca_df <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], colData(vsd))

    ggplot(pca_df, aes(PC1, PC2, color = .data[[intgroup]])) +
        geom_point(size = 3) +
        stat_ellipse(level = 0.95, linetype = 'dashed') +
        labs(x = paste0('PC1 (', percentVar[1], '%)'),
             y = paste0('PC2 (', percentVar[2], '%)')) +
        theme_bw() + theme(panel.grid = element_blank())
}

PCA Plot (Python)

from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

def pca_plot(df, metadata, color_by, ax=None):
    if ax is None:
        fig, ax = plt.subplots(figsize=(8, 6))

    pca = PCA(n_components=2)
    pcs = pca.fit_transform(df.T)

    for group in metadata[color_by].unique():
        mask = metadata[color_by] == group
        ax.scatter(pcs[mask, 0], pcs[mask, 1], label=group, alpha=0.8, s=50)

    ax.set_xlabel(f'PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)')
    ax.set_ylabel(f'PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)')
    ax.legend()
    return ax

Dotplot for Enrichment

Goal: Visualize enrichment analysis results as a dot plot showing gene ratio, count, and significance for top pathways.

Approach: Sort terms by adjusted p-value, compute numeric gene ratios, and plot with dot size proportional to gene count and color mapped to significance on a log scale.

library(ggplot2)

enrichment_dotplot <- function(enrich_result, top_n = 20) {
    df <- enrich_result %>%
        arrange(p.adjust) %>%
        head(top_n) %>%
        mutate(Description = factor(Description, levels = rev(Description)),
               GeneRatio_numeric = sapply(strsplit(GeneRatio, '/'), function(x) as.numeric(x[1])/as.numeric(x[2])))

    ggplot(df, aes(GeneRatio_numeric, Description, size = Count, color = p.adjust)) +
        geom_point() +
        scale_color_gradient(low = '#E64B35', high = '#4DBBD5', trans = 'log10') +
        scale_size_continuous(range = c(3, 10)) +
        labs(x = 'Gene Ratio', y = NULL, color = 'Adj. P-value', size = 'Count') +
        theme_bw() + theme(panel.grid.major.y = element_blank())
}

Boxplot with Statistics

library(ggpubr)

expression_boxplot <- function(df, gene, group_var) {
    ggboxplot(df, x = group_var, y = gene, color = group_var,
              add = 'jitter', palette = 'npg') +
        stat_compare_means(method = 't.test', label = 'p.signif') +
        labs(y = paste0(gene, ' Expression')) +
        theme(legend.position = 'none')
}

UMAP/tSNE Plot

import scanpy as sc
import matplotlib.pyplot as plt

def umap_plot(adata, color, ax=None, **kwargs):
    if ax is None:
        fig, ax = plt.subplots(figsize=(8, 6))

    sc.pl.umap(adata, color=color, ax=ax, show=False, **kwargs)
    return ax

# With custom styling
sc.pl.umap(adata, color='leiden', palette='tab20', frameon=False,
           title='', legend_loc='on data', legend_fontsize=8)

Correlation Plot

library(corrplot)

cor_mat <- cor(t(top_genes_mat), method = 'pearson')
corrplot(cor_mat, method = 'color', type = 'lower', order = 'hclust',
         tl.col = 'black', tl.cex = 0.7, col = colorRampPalette(c('#4DBBD5', 'white', '#E64B35'))(100))

Violin Plot with Split

ggplot(df, aes(cluster, expression, fill = condition)) +
    geom_split_violin(alpha = 0.7) +
    geom_boxplot(width = 0.2, position = position_dodge(0.5), outlier.shape = NA) +
    scale_fill_manual(values = c('#4DBBD5', '#E64B35')) +
    theme_bw()

Survival Curves

library(survival)
library(survminer)

fit <- survfit(Surv(time, status) ~ group, data = df)
ggsurvplot(fit, data = df, risk.table = TRUE, pval = TRUE,
           palette = c('#4DBBD5', '#E64B35'),
           legend.labs = c('Low', 'High'))

Related Skills

• data-visualization/ggplot2-fundamentals - Base plotting
• data-visualization/color-palettes - Color selection
• differential-expression/de-visualization - DE-specific plots
• pathway-analysis/enrichment-visualization - Enrichment plots