Bio Data Visualization Interactive Visualization

Version Compatibility

Reference examples tested with: ggplot2 3.5+, pandas 2.2+

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.

Interactive Visualization

"Create interactive plots" → Build zoomable, hoverable visualizations for exploring large datasets in notebooks or HTML reports.

• Python: plotly.express, bokeh, altair
• R: plotly::ggplotly(), htmlwidgets

plotly (Python)

import plotly.express as px
import plotly.graph_objects as go
import pandas as pd

# Scatter plot
fig = px.scatter(df, x='PC1', y='PC2', color='condition', hover_data=['sample'],
                 title='PCA Plot')
fig.write_html('pca_interactive.html')
fig.show()

Interactive Volcano Plot

Goal: Create a zoomable, hoverable volcano plot for exploring differential expression results.

Approach: Compute significance categories, build a plotly scatter with gene names on hover and metadata tooltips, add threshold reference lines, and export as standalone HTML.

import plotly.express as px

df['neg_log_pval'] = -np.log10(df['pvalue'])
df['significant'] = (df['padj'] < 0.05) & (abs(df['log2FoldChange']) > 1)

fig = px.scatter(df, x='log2FoldChange', y='neg_log_pval',
                 color='significant', hover_name='gene',
                 hover_data=['baseMean', 'padj'],
                 color_discrete_map={True: 'red', False: 'grey'},
                 title='Interactive Volcano Plot')

fig.add_hline(y=-np.log10(0.05), line_dash='dash', line_color='grey')
fig.add_vline(x=-1, line_dash='dash', line_color='grey')
fig.add_vline(x=1, line_dash='dash', line_color='grey')

fig.update_layout(xaxis_title='Log2 Fold Change', yaxis_title='-Log10 P-value')
fig.write_html('volcano_interactive.html')

Interactive Heatmap

import plotly.express as px

fig = px.imshow(df, color_continuous_scale='RdBu_r', aspect='auto',
                labels=dict(x='Samples', y='Genes', color='Expression'))
fig.update_xaxes(tickangle=45)
fig.write_html('heatmap_interactive.html')

plotly with Subplots

from plotly.subplots import make_subplots
import plotly.graph_objects as go

fig = make_subplots(rows=1, cols=2, subplot_titles=('PCA', 'Volcano'))

fig.add_trace(go.Scatter(x=df['PC1'], y=df['PC2'], mode='markers',
                          marker=dict(color=df['condition'].map({'Control': 'blue', 'Treatment': 'red'})),
                          text=df['sample'], name='PCA'), row=1, col=1)

fig.add_trace(go.Scatter(x=de['log2FC'], y=-np.log10(de['pvalue']), mode='markers',
                          marker=dict(color=de['significant'].map({True: 'red', False: 'grey'})),
                          text=de['gene'], name='Volcano'), row=1, col=2)

fig.update_layout(height=500, width=1000, showlegend=False)
fig.write_html('combined_interactive.html')

plotly (R)

library(plotly)

# From ggplot2
p <- ggplot(df, aes(PC1, PC2, color = condition, text = sample)) +
    geom_point()
ggplotly(p)

# Native plotly
plot_ly(df, x = ~PC1, y = ~PC2, color = ~condition, text = ~sample,
        type = 'scatter', mode = 'markers') %>%
    layout(title = 'PCA Plot')

Interactive MA Plot

library(plotly)

de_results$text <- paste0('Gene: ', de_results$gene, '<br>',
                           'baseMean: ', round(de_results$baseMean, 2), '<br>',
                           'log2FC: ', round(de_results$log2FoldChange, 2), '<br>',
                           'padj: ', formatC(de_results$padj, format = 'e', digits = 2))

plot_ly(de_results, x = ~log10(baseMean), y = ~log2FoldChange,
        color = ~(padj < 0.05), colors = c('grey', 'red'),
        text = ~text, hoverinfo = 'text',
        type = 'scatter', mode = 'markers', marker = list(size = 5, opacity = 0.6)) %>%
    layout(title = 'MA Plot',
           xaxis = list(title = 'Log10 Mean Expression'),
           yaxis = list(title = 'Log2 Fold Change'))

Linked Brushing

import plotly.express as px
from plotly.subplots import make_subplots

fig = px.scatter_matrix(df, dimensions=['PC1', 'PC2', 'PC3'], color='condition')
fig.write_html('scatter_matrix.html')

bokeh (Python)

from bokeh.plotting import figure, output_file, save
from bokeh.models import ColumnDataSource, HoverTool

output_file('pca_bokeh.html')

source = ColumnDataSource(df)

p = figure(title='PCA Plot', x_axis_label='PC1', y_axis_label='PC2',
           tools='pan,wheel_zoom,box_zoom,reset,hover,save')

p.circle('PC1', 'PC2', source=source, size=10, alpha=0.6,
         color='color', legend_field='condition')

hover = p.select(dict(type=HoverTool))
hover.tooltips = [('Sample', '@sample'), ('Condition', '@condition')]

save(p)

bokeh with Widgets

from bokeh.layouts import column
from bokeh.models import Select
from bokeh.io import curdoc

select = Select(title='Color by:', value='condition',
                options=['condition', 'batch', 'cluster'])

def update(attr, old, new):
    p.circle.glyph.fill_color = new

select.on_change('value', update)
curdoc().add_root(column(select, p))

Save Interactive Plots

# plotly
fig.write_html('plot.html')
fig.write_json('plot.json')

# bokeh
from bokeh.io import save, export_png
save(p, filename='plot.html')
export_png(p, filename='plot.png')  # requires selenium

Embed in Jupyter

# plotly - works automatically in Jupyter
fig.show()

# bokeh
from bokeh.io import output_notebook, show
output_notebook()
show(p)

Related Skills

• data-visualization/ggplot2-fundamentals - Static plots
• data-visualization/specialized-omics-plots - Omics-specific plots
• reporting/quarto-reports - Embed in reports