ExP Heatmap

A powerful Python package and command-line tool for visualizing multidimensional population genetics data through intuitive heatmaps.

ExP Heatmap specializes in displaying cross-population data, including differences, similarities, p-values, and other statistical parameters between multiple groups or populations. This tool enables efficient evaluation of millions of statistical values in a single, comprehensive visualization.

ExP heatmap of the human lactose (LCT) gene showing population differences between 26 populations from the 1000 Genomes Project, displaying empirical rank scores for cross-population extended haplotype homozygosity (XPEHH) selection test. Create your own LCT heatmap with the Quick Start Guide

Developed by the Laboratory of Genomics and Bioinformatics, Institute of Molecular Genetics of the Academy of Sciences of the Czech Republic

Features

Multiple Statistical Tests: Support for XPEHH, XP-NSL, Delta Tajima's D, and Hudson's Fst
Flexible Input Formats: Work with VCF files, pre-computed statistics, or ready-to-plot data
Command-Line Interface: Easy-to-use CLI for standard workflows
Python API: Full programmatic control for custom analyses
Efficient Processing: Zarr-based data storage for fast computation
Customizable Visualization: Multiple color schemes, resolution options, and display settings
Interactive Mode: Plotly-based HTML visualizations with zoom, pan, and hover tooltips

Installation

Requirements

Python ≥ 3.8
vcftools (for genomic data preparation - optional if using preprocessed data)

Python Dependencies

ExP Heatmap requires the following Python packages (automatically installed):

Package	Version	Purpose
scikit-allel	latest	Population genetics computations
zarr	< 3.0.0	Efficient array storage (v3+ not supported)
numpy	latest	Numerical operations
pandas	latest	Data manipulation
matplotlib	latest	Static visualizations
seaborn	latest	Heatmap rendering
click	latest	Command-line interface
plotly	latest	Interactive visualizations
tqdm	latest	Progress bars

Important: ExP Heatmap requires zarr < 3.0.0. The package will automatically install a compatible version, but if you encounter issues, ensure you have the correct version:
pip install 'zarr<3.0.0'

Install from PyPI

pip install exp_heatmap

Install from GitHub (latest version)

pip install git+https://github.com/bioinfocz/exp_heatmap.git

Quick Start

Get started with ExP Heatmap in three simple steps:

Step 1: Download the prepared results of the extended haplotype homozygosity (XPEHH) selection test for the part of human chromosome 2, 1000 Genomes Project data either directly via Zenodo or via command:

wget "https://zenodo.org/records/16364351/files/chr2_output.tar.gz"

Step 2: Decompress the downloaded folder in your working directory:

tar -xzf chr2_output.tar.gz

Step 3: Run the exp_heatmap plot command:

exp_heatmap plot chr2_output/ --start 136108646 --end 137108646 --title "LCT gene" --out LCT_xpehh

The exp_heatmap package will read the files from chr2_output/ folder and create the ExP heatmap and save it as LCT_xpehh.png file.

Usage

Pipeline Overview

ExP Heatmap follows a simple three-step workflow: prepare → compute → plot. Each step can be used independently depending on your data format.

┌─────────────┐      ┌─────────────┐      ┌─────────────┐      ┌─────────────┐
│   VCF File  │ ──── │   prepare   │ ──── │   ZARR Dir  │      │             │
└─────────────┘      └─────────────┘      └──────┬──────┘      │             │
                                                 │             │             │
                                                 ▼             │             │
┌─────────────┐      ┌─────────────┐      ┌─────────────┐      │   Heatmap   │
│ Panel File  │ ──── │   compute   │ ──── │  TSV Files  │ ──── │    (PNG)    │
└─────────────┘      └─────────────┘      └──────┬──────┘      │             │
                                                 │             │             │
                                                 ▼             │             │
                                          ┌─────────────┐      │             │
                                          │    plot     │ ──── │             │
                                          └─────────────┘      └─────────────┘

Starting Points:

From VCF: Use all three steps (prepare → compute → plot)
From ZARR: Skip prepare, use compute → plot
From TSV results: Skip to plot directly

Command-Line Interface

1. Full Pipeline - `full`

Run the complete pipeline (prepare → compute → plot) in a single command.

exp_heatmap full [OPTIONS] <vcf_file> <panel_file>

Argument/Option	Type	Default	Description
`<vcf_file>`	PATH	required	Recoded VCF file (SNPs only recommended)
`<panel_file>`	PATH	required	Population panel file
`-o, --out`	PATH	`exp_heatmap`	Prefix for all output files
`-s, --start`	INT	required	Start position for displayed region
`-e, --end`	INT	required	End position for displayed region
`-t, --test`	choice	`xpehh`	Statistical test: `xpehh`, `xpnsl`, `delta_tajima_d`, `hudson_fst`
`-c, --chunked`	flag	-	Use chunked array to avoid memory exhaustion
`--title`	STR	-	Title of the heatmap
`--cmap`	STR	`Blues`	Colormap for visualization
`--interactive`	flag	-	Generate interactive HTML visualization
`--no-log`	flag	-	Disable logging to file
`--verbose`	flag	-	Show detailed debug output

Example:

exp_heatmap full chr15_snps.recode.vcf genotypes.panel -s 47924019 -e 48924019 -o slc24a5_analysis --title "SLC24A5"

This creates: slc24a5_analysis_zarr/, slc24a5_analysis_compute/, and slc24a5_analysis_plot.png

2. Data Preparation - `prepare`

Convert VCF files to efficient Zarr format for faster computation.

exp_heatmap prepare [OPTIONS] <vcf_file>

Argument/Option	Type	Default	Description
`<vcf_file>`	PATH	required	Recoded VCF file (SNPs only recommended)
`-o, --out`	PATH	`zarr_output`	Directory for ZARR output files
`--no-log`	flag	-	Disable logging to file
`--verbose`	flag	-	Show detailed debug output in console

Example:

exp_heatmap prepare chr15_snps.recode.vcf -o chr15.zarr

3. Statistical Analysis - `compute`

Calculate population genetic statistics across all genomic positions.

exp_heatmap compute [OPTIONS] <zarr_dir> <panel_file>

Argument/Option	Type	Default	Description
`<zarr_dir>`	PATH	required	Directory with ZARR files from `prepare` step
`<panel_file>`	PATH	required	Population panel file (see Input File Formats)
`-o, --out`	PATH	`output`	Directory for output TSV files
`-t, --test`	choice	`xpehh`	Statistical test: `xpehh`, `xpnsl`, `delta_tajima_d`, `hudson_fst`
`-c, --chunked`	flag	-	Use chunked array to avoid memory exhaustion
`--no-log`	flag	-	Disable logging to file
`--verbose`	flag	-	Show detailed debug output in console

Statistical Tests:

xpehh: Cross-population Extended Haplotype Homozygosity - detects recent positive selection
xpnsl: Cross-population Number of Segregating sites by Length - robust to variation in recombination rate
delta_tajima_d: Delta Tajima's D - measures difference in allele frequency spectrum
hudson_fst: Hudson's Fst - genetic differentiation between populations

Note: The -t flag has different meanings for different commands: for compute it specifies the statistical test (--test), while for plot it specifies the heatmap title (--title).

Example:

exp_heatmap compute chr15.zarr genotypes.panel -o chr15_results -t xpehh

4. Visualization - `plot`

Generate heatmap visualizations from computed statistics.

exp_heatmap plot [OPTIONS] <input_dir>

Argument/Option	Type	Default	Description
`<input_dir>`	PATH	required	Directory with TSV files from `compute` step
`-s, --start`	INT	required	Start genomic position
`-e, --end`	INT	required	End genomic position
`-t, --title`	STR	-	Title of the heatmap
`-o, --out`	PATH	`ExP_heatmap`	Output filename (without extension)
`-c, --cmap`	STR	`Blues`	Colormap name (see Colormap Options)
`--interactive`	flag	-	Generate interactive HTML visualization
`--no-log`	flag	-	Disable logging to file
`--verbose`	flag	-	Show detailed debug output in console

Example:

# Basic usage
exp_heatmap plot chr15_results/ --start 47924019 --end 48924019 --title "SLC24A5" --out slc24a5

# Interactive HTML output
exp_heatmap plot chr15_results/ --start 47924019 --end 48924019 --interactive --out slc24a5_interactive

Python Package

The Python API offers more flexibility and customization options. Choose the appropriate scenario based on your data format:

Scenario A: Ready-to-Plot Data

Use when: You have pre-computed rank scores in a TSV file.

Data format: TSV file with columns: CHROM, POS, followed by pairwise columns for population comparisons.

from exp_heatmap.plot import plot_exp_heatmap
import pandas as pd

# Load your data
data = pd.read_csv("rank_scores.tsv", sep="\t")

# Create heatmap
plot_exp_heatmap(
    data,
    start=135287850,
    end=136287850,
    title="Population Differences in LCT Gene",
    cmap="Blues",
    output="lct_analysis",
    populations="1000Genomes"  # Predefined population set
)

Scenario B: Statistical Results to Rank Scores

Use when: You have computed statistical test results that need conversion to rank scores.

from exp_heatmap.plot import plot_exp_heatmap, create_plot_input

# Convert statistical results to empirical rank scores
data_to_plot = create_plot_input(
    "results_directory/",      # Directory with test results
    start=135287850, 
    end=136287850, 
    populations="1000Genomes",
    rank_scores="2-tailed"    # Options: "2-tailed", "ascending", "descending"
)

# Create heatmap
plot_exp_heatmap(
    data_to_plot,
    start=135287850,
    end=136287850,
    title="XP-NSL Test Results",
    cmap="expheatmap",         # Custom ExP colormap
    output="xpnsl_results"
)

Scenario C: Complete VCF Workflow

Use when: Starting from raw VCF files. Combine CLI commands with Python plotting:

import subprocess
from exp_heatmap.plot import plot_exp_heatmap, create_plot_input

# 1. Prepare data (using CLI)
subprocess.run(["exp_heatmap", "prepare", "data_snps.recode.vcf", "-o", "data.zarr"])

# 2. Compute statistics (using CLI) 
subprocess.run(["exp_heatmap", "compute", "data.zarr", "populations.panel", "-o", "results/"])

# 3. Create custom plots (using Python)
data_to_plot = create_plot_input("results/", start=47000000, end=49000000)
plot_exp_heatmap(data_to_plot, start=47000000, end=49000000, 
                 title="Custom Analysis", output="custom_plot")

Additional Python API Functions

Summarize by Superpopulation:

from exp_heatmap.plot import create_plot_input, summarize_by_superpopulation

# Load data
data = create_plot_input("results/", start=47000000, end=49000000)

# Aggregate to superpopulation level (AFR, EUR, EAS, SAS, AMR)
superpop_data = summarize_by_superpopulation(data, agg_func='mean')
# Result has 20 rows (5×4 superpopulation pairs) instead of 650

Extract Top Regions:

from exp_heatmap.plot import create_plot_input, extract_top_regions

# Load data
data = create_plot_input("results/", start=47000000, end=49000000)

# Find genomic windows with highest selection signals
top_regions = extract_top_regions(data, n_top=50, window_size=10000)
print(top_regions[['center', 'mean_score', 'top_population_pair']])

Custom Colorbar Parameters:

from exp_heatmap.plot import prepare_cbar_params

# Calculate optimal colorbar settings based on data range
cmin, cmax, cbar_ticks = prepare_cbar_params(data_to_plot, n_cbar_ticks=6)

Input File Formats

VCF File

Standard VCF format. For best results:

Filter to SNPs only (remove indels)
Use recoded VCF from vcftools

vcftools --gzvcf input.vcf.gz --remove-indels --recode --recode-INFO-all --out snps_only

Panel File

Tab-separated file defining population membership for each sample. Required columns:

Column	Description
`sample`	Sample identifier (must match VCF sample names exactly)
`pop`	Population code (e.g., "CEU", "YRI")
`super_pop`	Superpopulation code (e.g., "EUR", "AFR")

Example panel file:

sample	pop	super_pop	gender
HG00096	GBR	EUR	male
HG00097	GBR	EUR	female
HG00099	GBR	EUR	female
NA18486	YRI	AFR	male
NA18487	YRI	AFR	female
NA18488	YRI	AFR	male

Important: The sample order in the panel file must match the sample order in the VCF/ZARR file exactly.

1000 Genomes Panel File:

Download the official panel file:

wget "ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/integrated_call_samples_v3.20130502.ALL.panel" -O genotypes.panel

Output File Formats

Compute Output (TSV Files)

The compute step generates one TSV file per population pair, named POP1_POP2.tsv.

Columns:

Column	Description
`name`	Dataset identifier (derived from input filename)
`variant_pos`	Genomic position of the variant
`{test}`	Raw test statistic value (e.g., `xpehh`, `xpnsl`)
`-log10_p_value_ascending`	Empirical rank score (ascending sort)
`-log10_p_value_descending`	Empirical rank score (descending sort)

Example output (CEU_YRI.tsv):

name	variant_pos	xpehh	-log10_p_value_ascending	-log10_p_value_descending
chr15_snps	48120990	0.523	1.234	2.456
chr15_snps	48120995	-0.891	2.891	1.123
chr15_snps	48121003	1.245	0.891	3.012

Note: Column names contain "p_value" for backward compatibility, but these are empirical rank scores, not classical p-values. See Statistical Methodology for details.

Advanced Features

Interactive Visualizations

Generate HTML-based interactive heatmaps with zoom, pan, and hover tooltips:

from exp_heatmap.interactive import plot_interactive

# Create interactive HTML visualization
plot_interactive(
    "results_directory/",
    start=135287850,
    end=136287850,
    title="Interactive LCT Analysis",
    output="lct_interactive"  # Saves as lct_interactive.html
)

Or via CLI:

exp_heatmap plot results/ --start 135287850 --end 136287850 --interactive --out lct_interactive

Additional Interactive Functions:

from exp_heatmap.interactive import create_comparison_view, create_population_focus_view
from exp_heatmap.plot import create_plot_input

data = create_plot_input("results/", start=40000000, end=60000000)

# Compare two genomic regions side-by-side
create_comparison_view(
    data,
    region1=(47000000, 49000000),
    region2=(55000000, 57000000),
    title="Region Comparison",
    output="comparison"
)

# Focus on comparisons involving a specific population
create_population_focus_view(
    data,
    focus_population="CEU",
    start=47000000,
    end=49000000,
    title="CEU Selection Signals",
    output="ceu_focus"
)

Advanced Customization

Fine-tune your visualizations with advanced options:

from exp_heatmap.plot import plot_exp_heatmap, prepare_cbar_params, superpopulations

# Custom colorbar settings
cmin, cmax, cbar_ticks = prepare_cbar_params(data_to_plot, n_cbar_ticks=6)

# Advanced plot with multiple customizations
plot_exp_heatmap(
    data_to_plot,
    start=135000000,
    end=137000000,
    title="Selection Signals in African Populations",
    
    # Population filtering
    populations=superpopulations["AFR"],  # Focus on African populations
    # Available: ["AFR", "AMR", "EAS", "EUR", "SAS"] or custom list
    
    # Visual customizations
    cmap="expheatmap",                    # Custom ExP colormap
    display_limit=1.60,                   # Filter noise (values below limit = white)
    display_values="higher",              # Show values above display_limit
    
    # Annotations
    vertical_line=[                       # Mark important SNPs
        [135851073, "rs41525747"],        # [position, label]
        [135851081, "rs41380347"]
    ],
    
    # Colorbar customization
    cbar_vmin=cmin,
    cbar_vmax=cmax,
    cbar_ticks=cbar_ticks,
    
    # Output
    output="african_populations_analysis",
    xlabel="Custom region description"
)

Colormap Options

ExP Heatmap supports all matplotlib colormaps plus a custom colormap:

Colormap	Description	Best For
`Blues`	Sequential blue gradient (default)	General use
`expheatmap`	Custom colormap based on `gist_ncar_r` with white background	Highlighting strong signals
`gist_heat`	Heat-style gradient	High contrast visualization
`Reds`	Sequential red gradient	Alternative sequential
`viridis`	Perceptually uniform	Color-blind friendly
`RdBu`	Diverging red-blue	Bidirectional data

See the full list of matplotlib colormaps.

Custom expheatmap colormap:

Based on gist_ncar_r
White background for low values (better noise filtering)
Dark blue for highest values
Optimized for selection signal visualization

Workflow Examples

Complete Analysis: SLC24A5 Gene

This example demonstrates a full workflow analyzing the SLC24A5 gene, known for its role in human skin pigmentation using 1000 Genomes Project data. SLC24A5 is also known to show strong selection signals, which makes it a suitable example.

#!/bin/bash

# Download 1000 Genomes data
wget "ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ALL.chr15.phase3_shapeit2_mvncall_integrated_v5b.20130502.genotypes.vcf.gz" -O chr15.vcf.gz
wget "ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/integrated_call_samples_v3.20130502.ALL.panel" -O genotypes.panel

# Filter to SNPs only
vcftools --gzvcf chr15.vcf.gz \
    --remove-indels \
    --recode \
    --recode-INFO-all \
    --out chr15_snps

# Prepare data
exp_heatmap prepare chr15_snps.recode.vcf -o chr15_snps.recode.zarr

# Compute statistics
exp_heatmap compute chr15_snps.recode.zarr genotypes.panel -o chr15_snps_output

# Generate heatmap for SLC24A5 region
exp_heatmap plot chr15_snps_output \
    --start 47924019 \
    --end 48924019 \
    --title "SLC24A5" \
    --cmap gist_heat \
    --out SLC24A5_heatmap

Gallery

Different Rank Score Computations

The same XP-EHH test data for the ADM2 gene region, showing different rank score calculation methods:

Two-tailed rank scores:

Ascending rank scores:

Descending rank scores:

Noise Filtering

Using display_limit and display_values parameters to filter noisy data and highlight significant regions:

Same data as above, but with display_limit=1.60 to filter noise and highlight significant signals.

Statistical Methodology

Empirical Rank Scores (Not P-Values)

ExP Heatmap uses empirical rank scores to visualize selection signals. Despite column names containing "p_value" (kept for backward compatibility), these are not classical statistical p-values.

How rank scores are computed:

Rank all variants: For each population pair, sort all genome-wide test statistics
Calculate percentile: rank_score = rank / total_variants
Transform to -log10 scale: display_value = -log10(rank_score)

Interpretation:

Higher values indicate more extreme (potentially selected) variants
A value of 3.0 means the variant is in the top 0.1% genome-wide
A value of 2.0 means the variant is in the top 1% genome-wide

Rank Score Options

The rank_scores parameter in create_plot_input() controls how rank scores are calculated:

Option	Description	Use Case
`"2-tailed"`	For POP1_POP2: use descending; for POP2_POP1: use ascending	Default - Captures selection in either direction
`"ascending"`	Lowest test values ranked first	Detect negative selection signals
`"descending"`	Highest test values ranked first	Detect positive selection signals

Example:

# Two-tailed (recommended for most analyses)
data = create_plot_input("results/", start=47000000, end=49000000, rank_scores="2-tailed")

# One-tailed for specific hypothesis
data = create_plot_input("results/", start=47000000, end=49000000, rank_scores="descending")

Statistical Tests Explained

Test	Detects	Interpretation
XP-EHH	Recent positive selection	Positive values: selection in pop1; Negative: selection in pop2
XP-nSL	Selection (robust to recombination rate variation)	Similar to XP-EHH but more robust
Delta Tajima's D	Difference in allele frequency spectrum	Positive: excess rare variants in pop1
Hudson's Fst	Population differentiation	Higher values: greater genetic distance

1000 Genomes Population Reference

ExP Heatmap is optimized for the 1000 Genomes Project Phase 3 data (26 populations).

Population Codes

Code	Population	Superpopulation
ACB	African Caribbean in Barbados	AFR
ASW	African Ancestry in SW USA	AFR
ESN	Esan in Nigeria	AFR
GWD	Gambian in Western Division	AFR
LWK	Luhya in Webuye, Kenya	AFR
MSL	Mende in Sierra Leone	AFR
YRI	Yoruba in Ibadan, Nigeria	AFR
BEB	Bengali in Bangladesh	SAS
GIH	Gujarati Indians in Houston	SAS
ITU	Indian Telugu in the UK	SAS
PJL	Punjabi in Lahore, Pakistan	SAS
STU	Sri Lankan Tamil in the UK	SAS
CDX	Chinese Dai in Xishuangbanna	EAS
CHB	Han Chinese in Beijing	EAS
CHS	Han Chinese South	EAS
JPT	Japanese in Tokyo	EAS
KHV	Kinh in Ho Chi Minh City, Vietnam	EAS
CEU	Utah residents (CEPH) with European ancestry	EUR
FIN	Finnish in Finland	EUR
GBR	British in England and Scotland	EUR
IBS	Iberian populations in Spain	EUR
TSI	Toscani in Italy	EUR
CLM	Colombian in Medellín	AMR
MXL	Mexican Ancestry in Los Angeles	AMR
PEL	Peruvian in Lima	AMR
PUR	Puerto Rican in Puerto Rico	AMR

Superpopulations

Code	Name	Populations
AFR	African	ACB, ASW, ESN, GWD, LWK, MSL, YRI
SAS	South Asian	BEB, GIH, ITU, PJL, STU
EAS	East Asian	CDX, CHB, CHS, JPT, KHV
EUR	European	CEU, FIN, GBR, IBS, TSI
AMR	American (Admixed)	CLM, MXL, PEL, PUR

Reference: 1000 Genomes Project

Troubleshooting

Common Errors and Solutions

Zarr Version Error

Error:

Unsupported zarr version: 3.x.x
Please downgrade to zarr version < 3.0.0

Solution:

pip install 'zarr<3.0.0'

No Data Found in Genomic Region

Error:

ValueError: No data found in the requested genomic region (X - Y). 
The data contains positions from A to B.

Cause: The specified --start/--end coordinates don't overlap with the data.

Solution:

Check that your coordinates match the chromosome in your data
Use coordinates within the available range shown in the error message
Verify you're using the correct output directory

Sample Order Mismatch

Error:

Sample order differs! Found X mismatches

Cause: The sample order in the panel file doesn't match the VCF/ZARR file.

Solution:

Ensure you're using the correct panel file for your VCF
Check that both files are from the same data release/phase
Verify sample IDs match exactly (case-sensitive)

Memory Exhaustion During Compute

Symptoms: Process killed, system becomes unresponsive, or "MemoryError"

Solution: Use the --chunked flag to process data in smaller chunks:

exp_heatmap compute data.zarr panel.tsv -o output/ --chunked

Empty or All-NaN Results

Error:

All positions have NaN results. No output will be generated.

Cause: The statistical test produced no valid results, often due to:

Too few variants
Insufficient allele frequency variation
For delta_tajima_d: window size too large

Solution:

Check your data has sufficient variants
For delta_tajima_d, the default window size is 13 SNPs; ensure your data has enough variants

Getting Help

Check the GitHub Issues for known problems
Enable verbose logging with --verbose flag for detailed debug output
Log files are saved automatically (disable with --no-log)

Contributing

We welcome contributions! Feel free to contact us or submit issues or pull requests.

Development Setup

git clone https://github.com/bioinfocz/exp_heatmap.git
cd exp_heatmap
pip install -e .

License

This project is licensed under a Custom Non-Commercial License based on the MIT License - see the LICENSE file for details.

Note: This is NOT the standard MIT License. Commercial use requires explicit written permission from the authors.

For commercial licensing under different terms, please contact: edvard.ehler@img.cas.cz

Contributors

Edvard Ehler (@EdaEhler) - Lead Developer
Adam Nógell (@AdamNogell) - Developer
Jan Pačes (@hpaces) - Developer
Mariana Šatrová (@satrovam) - Developer
Ondřej Moravčík (@ondra-m) - Developer

Acknowledgments

If you use ExP Heatmap in your research, please cite our paper [citation details will be added upon publication].

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ExP Heatmap

Features

Table of Contents

Installation

Requirements

Python Dependencies

Install from PyPI

Install from GitHub (latest version)

Quick Start

Usage

Pipeline Overview

Command-Line Interface

1. Full Pipeline - full

2. Data Preparation - prepare

3. Statistical Analysis - compute

4. Visualization - plot

Python Package

Scenario A: Ready-to-Plot Data

Scenario B: Statistical Results to Rank Scores

Scenario C: Complete VCF Workflow

Additional Python API Functions

Input File Formats

VCF File

Panel File

Output File Formats

Compute Output (TSV Files)

Advanced Features

Interactive Visualizations

Advanced Customization

Colormap Options

Workflow Examples

Complete Analysis: SLC24A5 Gene

Gallery

Different Rank Score Computations

Noise Filtering

Statistical Methodology

Empirical Rank Scores (Not P-Values)

Rank Score Options

Statistical Tests Explained

1000 Genomes Population Reference

Population Codes

Superpopulations

Troubleshooting

Common Errors and Solutions

Zarr Version Error

No Data Found in Genomic Region

Sample Order Mismatch

Memory Exhaustion During Compute

Empty or All-NaN Results

Getting Help

Contributing

Development Setup

License

Contributors

Acknowledgments

About

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Resources

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1. Full Pipeline - `full`

2. Data Preparation - `prepare`

3. Statistical Analysis - `compute`

4. Visualization - `plot`