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
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