Specialized Omics Plots

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 (R)

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)

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 (R)

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 (R)

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 (Python)

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 (R)

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 (R)

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 (R)

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