Skip to content

visualization

Directory actions

More options

Directory actions

More options

Latest commit

 

History

History

visualization

README.md

Visualization Module

70+ plot types for biological data -- basic charts, statistical plots, genomic visualizations, expression heatmaps, interactive dashboards, and configurable themes.

Overview

70+ plot types for biological data -- basic charts, statistical plots, genomic visualizations, expression heatmaps, interactive dashboards, and configurable themes.

Table of Contents

Architecture

graph TD
    subgraph "Visualization Module"
        P[plots/] --> |basic.py| BA[Line, Scatter, Bar, Heatmap]
        P --> |general.py| GN[Volcano, Manhattan, PCA, QQ]
        P --> |specialized.py| SP[Domain-Specific Plots]
        P --> |multidim.py| MD[Multi-Dimensional Plots]
        P --> |animations.py| AN[Animated Plots]

        G[genomics/] --> |expression.py| EX[Expression Heatmaps]
        G --> |genomics.py| GM[Manhattan, Volcano, Ideogram]
        G --> |networks.py| NW[Network Graphs]
        G --> |trees.py| TR[Phylogenetic Trees]

        A[analysis/] --> |statistical.py| ST[Histogram, Boxplot, ROC]
        A --> |dimred.py| DR[PCA, UMAP, t-SNE Plots]
        A --> |quality.py| QA[Quality Assessment Plots]

        D[dashboards/] --> |composite.py| CM[Multi-Panel Layouts]
        D --> |interactive.py| IN[Interactive Plotly Plots]

        CF[config/] --> |themes.py| TH[Theme Management]
        CF --> |palettes.py| PL[Color Palettes]
    end

    CF --> P
    CF --> G
    P --> D
    G --> D
Loading

Key Capabilities

Basic & Statistical Plots

from metainformant.visualization.plots.basic import lineplot, scatter_plot, heatmap
from metainformant.visualization.analysis.statistical import histogram
import numpy as np

ax = lineplot(np.arange(100), np.random.randn(100), output_path="output/viz/line.png")
ax = histogram(np.random.randn(500), bins=30, output_path="output/viz/hist.png")
Function Module Description
lineplot, scatter_plot, heatmap, bar_plot plots.basic Core chart types
volcano_plot, manhattan_plot, pca_plot, qq_plot plots.general Genomic/statistical composites
histogram, correlation_heatmap analysis.statistical Statistical distribution plots
plot_pca analysis.dimred PCA scatter with variance explained

Genomic Visualizations

from metainformant.visualization.genomics.expression import plot_expression_heatmap
from metainformant.visualization.genomics.genomics import manhattan_plot, volcano_plot

ax = plot_expression_heatmap(expression_df, output_path="output/viz/heatmap.png")
ax = manhattan_plot(positions, pvalues, chromosomes, output_path="output/viz/manhattan.png")

Themes, Palettes & Dashboards

from metainformant.visualization.config.themes import apply_theme
from metainformant.visualization.config.palettes import categorical, expression_gradient
from metainformant.visualization.dashboards.composite import multi_panel

apply_theme("publication")
colors = categorical(5, palette="wong")  # Colorblind-safe
fig = multi_panel(plot_functions=[plot_a, plot_b], layout=(1, 2))

Submodules

Module Purpose
plots/ Core chart types: lineplot, scatter_plot, heatmap, bar_plot, volcano_plot, manhattan_plot
genomics/ Expression heatmaps, Manhattan plots, phylogenetic trees, network graphs
analysis/ Statistical plots (histogram, plot_pca), quality assessment, time series
dashboards/ Multi-panel composites (multi_panel, genomic_overview) and interactive Plotly plots
config/ Themes (apply_theme, theme) and color palettes (categorical, expression_gradient)
interactive_dashboards/ Extended interactive dashboard utilities

Quick Start

from metainformant.visualization.plots.general import volcano_plot
import numpy as np
import pandas as pd

log2fc = np.random.randn(1000)
pvalues = np.random.uniform(0, 1, 1000)
ax = volcano_plot(log2fc, pvalues, output_path="output/viz/volcano.png")

Related