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 axMA 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 axDotplot 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