Interactive Visualization

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

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