tutorial

Geneview Tutorials

This directory contains Jupyter notebook tutorials for geneview — a Python package for genomics data visualization.

Available Tutorials

Tutorial	Description	Notebook
GWAS Plots	Manhattan plots and Q-Q plots for genome-wide association studies	gwas_plot.ipynb
Admixture	Population structure visualization from ADMIXTURE output	admixture.ipynb
Venn Diagrams	Set intersection diagrams for 2–6 datasets	venn.ipynb
Color Palettes	Color schemes and palette customization for genomics figures	palettes.ipynb
Plot Styles	Journal-compliant figure styles (Nature, Science, Cell) with a single function call	plotstyle.ipynb
Genome Tracks	Gviz-style genome track visualization (axis, annotations, gene models, data tracks, highlights)	genome_tracks.ipynb
Mutation Tracks	Lollipop- and dandelion-style visualization of mutations, variants, and methylation sites (LolliplotTrack, DandelionTrack)	mutation_tracks.ipynb

Additional Documentation

Document	Description
User Guide	Comprehensive guide covering all geneview modules (GWAS, Venn, Admixture, Karyotype, Genome Tracks, CLI, and more)
Genome Tracks Guide	Detailed Gviz-style guide for the geneview.genometracks module — track types, display parameters, file I/O, and complete examples
mtDNA Guide	Detailed guide for the geneview.mtdna module — MitoQuest-style readers, circular genome map, heteroplasmy landscape/heatmap, coverage, and copy number

Example Scripts

Runnable Python scripts demonstrating genome tracks features are available in examples/scripts/:

Script	Description
genome_tracks_basic.py	Basic GenomeAxisTrack + AnnotationTrack from BED
genome_tracks_gene_region.py	Gene models from GTF with collapsing modes
genome_tracks_data.py	DataTrack plot types: line, histogram, polygon, heatmap, points, mountain, gradient
genome_tracks_highlight.py	Cross-track highlight regions
genome_tracks_comprehensive.py	Full showcase with all track types combined
mtdna.py	Mitochondrial DNA figures: circular genome map, heteroplasmy scatter/heatmap, coverage, copy number

Run any script with: python examples/scripts/<script_name>.py

Synthetic example data can be regenerated with: python examples/scripts/generate_genome_tracks_data.py

Set Up the Environment for Jupyter Notebook

$ pip install jupyter        # Skip if already installed
$ pip install ipykernel
$ pip install geneview       # Install geneview and its dependencies
$ python -m ipykernel install --user --name venv --display-name "Python3(geneview)"

Launch a Tutorial

$ jupyter notebook

Then navigate to any .ipynb file above and select the Python3(geneview) kernel to get started.

Dataset

The tutorials use example datasets hosted in geneview-data. You can load them programmatically:

import geneview as gv

# List available datasets
names = gv.get_dataset_names()

# Load a dataset by name
df = gv.load_dataset("gwas")