hic-sca 0.2.6

HiC-SCA: Hi-C Spectral Compartment Assignment for predicting A-B chromosomal compartments

HiC-SCA: Hi-C Spectral Compartment Assignment

A Python package for assigning A-B compartments from Hi-C (chromosome conformation capture) data using spectral decomposition

Key capabilities:

• Process .hic files at single or multiple resolutions
• Automatic genome-wide Observed/Expected (O/E) normalization with smoothing
• Eigenvector selection using Inter-AB score, which quantifies A/B compartment assignment confidence
• Cross-resolution evaluation to identify suitable resolutions
• Multiple output formats (HDF5, Excel, BED, BedGraph, plots)

Table of Contents

• OS Requirements
• Installation
  • Install from Source
  • Install from PyPI
  • Requirements
  • Dependencies
  • Sample/Test Data
• Demo
• Testing
• Quick Start
  • Command-Line Interface
  • Python API
• Command-Line Interface
  • Arguments
  • Usage Examples
  • Output Files
    • 1. HDF5 Results File (always generated)
    • 2. Excel Files (always generated)
    • 3. BED Files (optional, with --bed)
    • 4. BedGraph Files (optional, with --bedgraph)
    • 5. Compartment Plots (always generated)
    • 6. Cross-Resolution MCC Plot (if multiple resolutions)
  • Troubleshooting CLI
• Python API
  • Core Classes
    • HiCSCA - Main Pipeline Class
  • Results Dictionary Structure
  • Evaluation Tools
    • CrossResolutionAnalyzer
    • CrossDatasetAnalyzer
    • MCCCalculator
• Other Documentation
• Citation
  • Dependencies
  • Sample Data
• Acknowledgments
• License
• Contributing

OS Requirements

Supported platforms:

• Linux (x86_64)
• macOS
• Windows 11 via WSL/WSL2

Tested environments:

OS	Version	Architecture
Ubuntu (WSL2)	22.04	x86_64
macOS	15 (Sequoia)	M2
CentOS	7.9	x86_64

Installation

There are two ways to install HiC-SCA: from source or from PyPI.

Note: Installation of HiC-SCA takes a few minutes but could take a lot longer if running on older Linux distros which requires compiling dependencies. This estimate excludes installing Xcode (macOS) or WSL2 (Windows)

Note: Sample/test Hi-C data must be downloaded separately from figshare. See Sample/Test Data section for details.

Extra instructions for macOS:

If installing on macOS, you will need Xcode. To check if you have Xcode installed, in Terminal, execute xcode-select -p. If it returns an error, you need to install Xcode. There are 3 ways to achieve this:

1. Install Xcode from Apple Developer
2. Install "Command Line Tools" from Apple Developer
3. "Command Line Tools" is installed with brew. Install brew by following the instructions at brew.sh

Extra instructions for Windows 11: HiC-SCA requires the hic-straw package, which contains C++ code that cannot be compiled by the Microsoft MSVC compiler on Windows. To run HiC-SCA on Windows, use WSL2. Instructions for installing WSL 2: https://learn.microsoft.com/en-us/windows/wsl/install

Install from Source

1. Check Miniforge GitHub page for instructions on how to install Miniforge

2. Create a new environment:

mamba create -n hicsca python git cxx-compiler zlib curl hdf5

3. Activate environment:

mamba activate hicsca

4. Clone repositories:

git clone https://github.com/iQLS-MMS/h5typer.git
git clone https://github.com/iQLS-MMS/hic-sca.git

5. Install:

# Install h5typer first (required dependency)
cd h5typer
pip install .

# Install HiC-SCA
cd ../hic-sca
pip install .

Install from PyPI

1. Check Miniforge GitHub page for instructions on how to install Miniforge

2. Create a new environment:

mamba create -n hicsca python cxx-compiler zlib curl hdf5

3. Activate environment:

mamba activate hicsca

4. Install HiC-SCA:

pip install hic-sca

Requirements

• Python >= 3.10
• pip >= 21.0

Dependencies

The package automatically installs:

• hicstraw >= 1.3.0 (reading .hic files)
• numpy >= 1.19.0 (numerical computations)
• scipy >= 1.15.0 (sparse matrices, eigendecomposition)
• h5py >= 3.0.0 (HDF5 I/O)
• pandas >= 1.0.0 (Excel export)
• openpyxl >= 3.0.0 (Excel I/O)
• matplotlib >= 3.0.0 (plotting)
• h5typer >= 0.1.0 (HDF5 type mapping)

Sample/Test Data

The test .hic dataset contains only the intra-chromosomal contacts of ENCFF216ZNY. This dataset is required for running the test suite and can also be used as a sample dataset for trying out HiC-SCA.

Download: The dataset is NOT included in the repository or PyPI package. Download ENCFF216ZNY_Intra_Only.hic (~479 MB) from figshare.

File placement:

• For running tests: Place the downloaded file at tests/test_data/ENCFF216ZNY_Intra_Only.hic (relative to the repository root)
• For general usage: You can place the file anywhere and reference it with the appropriate path in your commands

Demo

The examples/ folder contains sample output files from processing the test dataset:

hic-sca -f tests/test_data/ENCFF216ZNY_Intra_Only.hic -r 100000 -p my_sample

Output files included:

• my_sample_results.h5 - HDF5 file with complete analysis results
• my_sample_100000bp.xlsx - Excel file with compartment predictions and Inter-AB scores
• my_sample_chr{1-22}_100000bp.png - Compartment plots for all autosomal chromosomes at 100kb resolution

Processing takes <5 minutes on an Intel Core i7-9700 CPU on Windows 11 WSL2 - Ubuntu 22.04.

Testing

The test suite is an automated test that compares computed results against reference data to ensure consistency across different environments

Prerequisites:

1. Download the test data file ENCFF216ZNY_Intra_Only.hic from figshare
2. Place it at tests/test_data/ENCFF216ZNY_Intra_Only.hic (relative to the repository root)

To run the test suite, you must install HiC-SCA from source with test dependencies:

# Install from source
pip install ".[tests]"

Then run the tests from the repository root folder with:

pytest tests/

Quick Start

Command-Line Interface

The easiest way to use HiC-SCA is through the command-line interface:

# Process single resolution
hic-sca -f tests/test_data/ENCFF216ZNY_Intra_Only.hic -r 100000 -p my_sample

# Process with BED and BedGraph output
hic-sca -f tests/test_data/ENCFF216ZNY_Intra_Only.hic -r 100000 -p my_sample --bed --bedgraph

# Process multiple resolutions
hic-sca -f tests/test_data/ENCFF216ZNY_Intra_Only.hic -r 100000 50000 25000 -p my_sample

# Process all available resolutions with verbose output
hic-sca -f tests/test_data/ENCFF216ZNY_Intra_Only.hic -p my_sample -v

# Specify output directory
hic-sca -f tests/test_data/ENCFF216ZNY_Intra_Only.hic -r 100000 -p my_sample -o results/

# Process specific chromosomes
# WARNING: The background distribution used to compute the O/E Hi-C
#          contact maps will only include the specific chromosomes
hic-sca -f tests/test_data/ENCFF216ZNY_Intra_Only.hic -r 100000 -c chr1 chr2 chr3 -p my_sample

Output files:

• my_sample_results.h5 - HDF5 file with complete results
• my_sample_100000bp.xlsx - Excel file (mandatory)
• my_sample_100000bp.bed - BED file (if --bed specified)
• my_sample_100000bp.bedgraph - BedGraph file (if --bedgraph specified)
• my_sample_chr1_100000bp.png - Compartment plot for chr1
• my_sample_cross_resolution_mcc.png - Cross-resolution MCC heatmap (if multiple resolutions)

The BED and bedGraph output files can be visualized on the UCSC Genome Browser as Custom Tracks. Ensure the correct genome assembly is selected (the sample data is mapped to the human GRCh38/hg38 assembly).

Python API

from hicsca import HiCSCA

# Initialize pipeline with Hi-C file
hicsca = HiCSCA(
    hic_file_path="tests/test_data/ENCFF216ZNY_Intra_Only.hic",
    resolutions=[100000],  # or None for auto-detect all
    chr_names=None  # None = all autosomal chromosomes
)

# Process all chromosomes
hicsca.process_all_chromosomes(verbose=True)

# Access results
result = hicsca.results[100000]['chr1']
if result['Success']:
    compartment = result['assigned_AB_compartment']
    eig_idx = result['selected_eig_idx']
    score = result['modified_inter_eigval_score']
    print(f"chr1: Selected Eig{eig_idx}, Score: {score:.4f}")

# Export results using convenience methods
hicsca.to_bed(100000, "compartments_100kb.bed")
hicsca.to_bedgraph(100000, "compartments_100kb.bedgraph")
hicsca.to_excel(100000, "compartments_100kb.xlsx")
hicsca.to_hdf5("saved_analysis.h5")

# Plot compartments (saves to files)
hicsca.plot_compartments(100000, output_dir="plots", output_prefix="sample")

# Plot cross-resolution MCC correlation
hicsca.plot_cross_resolution_mcc(save_path="cross_res_mcc.png")

Command-Line Interface

Arguments

Required (one of):

• -f, --hic-file PATH - Path to input .hic file
• --load-hdf5 PATH - Load existing HDF5 results file

Optional:

• -r, --resolutions BP [BP ...] - Space-separated resolutions in bp (default: auto-detect all)
• -p, --output-prefix PREFIX - Prefix for output files (required)
• -o, --output-dir DIR - Output directory (default: current directory)
• -c, --chromosomes CHR [CHR ...] - Space-separated chromosome names (default: chr1-chr22)
• -t, --data-type TYPE - Data type: "observed" or "oe" (default: "observed")
• -v, --verbose - Enable verbose output
• --bed - Generate BED files for each resolution
• --bedgraph - Generate BedGraph files for each resolution

Usage Examples

# Basic usage
hic-sca -f data.hic -r 100000 -p my_sample

# With BED and BedGraph output
hic-sca -f data.hic -r 100000 -p my_sample --bed --bedgraph

# Multiple resolutions
hic-sca -f data.hic -r 500000 250000 100000 50000 -p my_sample

# All available resolutions
hic-sca -f data.hic -p my_sample -v

# Custom output directory
hic-sca -f data.hic -r 100000 -p my_sample -o results/

# Specific chromosomes
hic-sca -f data.hic -r 100000 -c chr1 chr2 chr3 -p my_sample

# Use pre-normalized O/E data from the .hic file (skip background normalization)
hic-sca -f data.hic -r 100000 -p my_sample -t oe

# Load existing HDF5 and export to BED/BedGraph
hic-sca --load-hdf5 results.h5 -p output --bed --bedgraph

# Load HDF5 with .hic file (enables processing additional data)
hic-sca --load-hdf5 results.h5 -f data.hic -p output

# Load HDF5 and export filtered data (specific resolutions/chromosomes)
hic-sca --load-hdf5 results.h5 -r 100000 -c chr1 chr2 -p output --bed

Output Files

The CLI generates the following output files:

1. HDF5 Results File (always generated)

Filename: {prefix}_results.h5

Complete analysis results for all resolutions and chromosomes:

• All compartment predictions
• Pre-computed background normalizations (for fast reloading)
• Eigendecomposition results
• Inter-AB score
• Self-contained: stores chromosome lengths, no .hic file path stored
• Can be loaded with or without .hic file for export or further processing

Loading HDF5 files:

from hicsca import HiCSCA

# Load for export only
hicsca = HiCSCA.from_hdf5("results.h5")
hicsca.to_bed(100000, "compartments.bed")

# Load with .hic file to enable processing additional data
hicsca = HiCSCA.from_hdf5("results.h5", hic_file_path="data.hic")
hicsca.process_chromosome("chr1")  # Can process more chromosomes

2. Excel Files (always generated)

Filename: {prefix}_{resolution}bp.xlsx

One file per resolution with:

• Per-chromosome worksheets containing:
  • Start: Bin start position (1-indexed, in bp)
  • End: Bin end position (in bp)
  • Value: Compartment eigenvector value
  • Compartment: "A" (positive values), "B" (negative values), or "" (excluded bins)
• Summary worksheet "Inter-AB Scores" containing:
  • Chromosome: Chromosome name
  • Inter-AB Score: Quality metric (numeric value or "N/A" if processing failed)
  • Confidence: "High" if 1.75 ≤ score ≤ 3.20, else "Low"

3. BED Files (optional, with --bed)

Filename: {prefix}_{resolution}bp.bed

BED9 format with RGB colors:

• Consecutive bins of same compartment are merged
• A compartments: red (255,0,0)
• B compartments: blue (0,0,255)
• Zero values (excluded regions) are skipped
• Track header included for genome browser compatibility

Format:

track name=AB_Compartments_100000bp description="..." itemRgb="On"
chr1    0       300000  A   0   .   0       300000  255,0,0
chr1    400000  800000  B   0   .   400000  800000  0,0,255

4. BedGraph Files (optional, with --bedgraph)

Filename: {prefix}_{resolution}bp.bedgraph

Continuous compartment scores for genome browser visualization:

• One value per bin (not merged)
• Zero values (excluded regions) are skipped
• Track header included

Format:

track type=bedGraph name="AB_Compartments_100000bp"
chr1    0       100000  0.123456
chr1    100000  200000  -0.098765

5. Compartment Plots (always generated)

Filename: {prefix}_{chr_name}_{resolution}bp.png

Publication-quality plots (300 DPI) for each chromosome:

• Red line: A compartment (positive values)
• Blue line: B compartment (negative values)

6. Cross-Resolution MCC Plot (if multiple resolutions)

Filename: {prefix}_cross_resolution_mcc.png and {prefix}_cross_resolution_mcc_colorbar.png

Heatmap showing Matthews Correlation Coefficient between resolutions:

• Assesses consistency across different resolutions
• Main heatmap and separate colorbar figure
• Gray cells indicate incompatible resolution pairs (not round multiples)
• Red-white-blue colormap for MCC values (0-1)

Troubleshooting CLI

"Error: Must provide either --load-hdf5 or -f/--hic-file"

• Provide at least one input source: -f for new .hic file, or --load-hdf5 for existing results

"Error: Hi-C file not found"

• Verify path to .hic file
• Use absolute paths if relative paths cause issues

"Error: HDF5 file not found"

• Verify path to HDF5 results file
• Ensure correct file name with .h5 extension

"No resolutions available"

• Check that .hic file contains data at specified resolutions
• Use auto-detection (omit -r flag) to see available resolutions

Memory errors

• Process fewer resolutions at once
• Use higher resolutions (e.g., 100kb instead of 10kb)
• Close other applications to free up RAM

Python API

Core Classes

HiCSCA - Main Pipeline Class

Complete pipeline for A-B compartment prediction from Hi-C data.

Initialization:

from hicsca import HiCSCA

hicsca = HiCSCA(
    hic_file_path="data/sample.hic",
    chr_names=None,  # None = all autosomal chromosomes
    resolutions=None,  # None = all available resolutions
    data_type="observed",  # "observed" or "oe"
    norm_type="NONE",
    smoothing_cutoff=400
)

Parameters:

• hic_file_path (str): Path to .hic file
• chr_names (list or None): Chromosome names to process (default: all autosomal)
• resolutions (list or None): Resolutions in bp (default: auto-detect all)
• data_type (str): "observed" (raw contacts with O/E normalization) or "oe" (pre-normalized, skip O/E)
• norm_type (str): Normalization type for hicstraw (default: "NONE")
• smoothing_cutoff (int): Smoothing parameter for O/E normalization (default: 400, only used when data_type="observed")

Key Methods:

# Compute background normalization (automatic when processing, can be called explicitly)
hicsca.compute_background_normalization(resolutions=None)

# Process single chromosome at specified resolutions
hicsca.process_chromosome(chr_name, resolutions=None, verbose=True)

# Process all chromosomes at specified resolutions
hicsca.process_all_chromosomes(resolutions=None, verbose=True)

# Load saved HiCSCA instance
hicsca = HiCSCA.from_hdf5(hdf5_path, hic_file_path=None)

Export Methods:

# Save complete instance to HDF5
hicsca.to_hdf5(output_path, update=False)

# Generate BED file
hicsca.to_bed(resolution, output_path, chr_names=None, dataset_id=None)

# Generate BedGraph file
hicsca.to_bedgraph(resolution, output_path, chr_names=None, dataset_id=None, track_name=None)

# Generate Excel file
hicsca.to_excel(resolution, output_path, chr_names=None, dataset_id=None)

# Plot compartments (saves to files and/or displays in Jupyter)
hicsca.plot_compartments(
    resolution,
    chr_names=None,
    output_dir=None,  # Specify to save files
    output_prefix=None,
    display=True,  # Set False to disable Jupyter display
    dpi=300,
    figsize=(3.595, 2)
)

# Plot cross-resolution MCC correlation heatmap
hicsca.plot_cross_resolution_mcc(
    chr_name='all',  # 'all' or specific chromosome
    resolutions=None,  # None = all resolutions
    chr_names=None,  # For genome-wide ('all') calculation
    figsize=(2.8, 2.8),
    dpi=300,
    background_alpha=1,  # 0=transparent, 1=opaque
    plot_colorbar=True,  # Generate separate colorbar figure
    save_path=None  # Path to save main figure
)

Usage:

# Example 1: Using observed data (default - with O/E normalization)
hicsca = HiCSCA(
    "data/sample.hic",
    resolutions=[100000, 50000],
    data_type="observed",
    norm_type="NONE"
)

# Process all chromosomes (results stored in hicsca.results)
hicsca.process_all_chromosomes(verbose=True)

# Access results
result = hicsca.results[100000]["chr1"]
if result['Success']:
    compartment = result['assigned_AB_compartment']
    eig_idx = result['selected_eig_idx']
    score = result['modified_inter_eigval_score']

# Export results
hicsca.to_bed(100000, "compartments_100kb.bed")
hicsca.to_excel(100000, "compartments_100kb.xlsx")
hicsca.to_hdf5("saved_analysis.h5")

# Example 2: Using pre-normalized O/E data (skips background normalization)
hicsca_oe = HiCSCA(
    "data/sample.hic",
    resolutions=[100000],
    data_type="oe",  # Data already O/E normalized
    norm_type="KR"   # Can use normalized data if available
)
hicsca_oe.process_all_chromosomes(verbose=True)

# Example 3: Load previously saved HiCSCA instance
hicsca_loaded = HiCSCA.from_hdf5("saved_analysis.h5")
# All results and normalizations already loaded - no processing needed
result = hicsca_loaded.results[100000]['chr1']

Results Dictionary Structure

Results are stored in: hicsca.results[resolution][chr_name]

Each result dictionary contains:

Core Fields (always present):

• Success (bool): Whether processing succeeded
• Eig Converged (bool): Whether eigendecomposition converged

Compartment Prediction (when Success=True):

• assigned_AB_compartment (ndarray): Normalized compartment eigenvector (positive=A, negative=B, zero=excluded)
• selected_eig_idx (int): Index of selected eigenvector (1-10)
• modified_inter_eigval_score (float): Eigenvalue-weighted eigenvector selection score
• unmodified_inter_AB_score (float): Raw inter-compartment contact score

Eigendecomposition Results:

• eigvals (ndarray): All eigenvalues (11 values: trivial + 10 non-trivial)
• eigenvects (ndarray): All eigenvectors (11 × N matrix, row-major format)

Quality Control:

• cutoff (float): Low-coverage filter cutoff value
• include_bool (ndarray): Boolean array indicating bins included in analysis
• non_zero_not_included_bool (ndarray): Boolean array for excluded non-zero bins
• deg (ndarray): Degree (column sums) of filtered O/E matrix
• OE_normed_diag (ndarray): Diagonal of O/E matrix for included bins

See RESULTS_STRUCTURE.md for complete documentation.

Evaluation Tools

CrossResolutionAnalyzer

Analyzes agreement between different resolutions within the same Hi-C dataset.

from hicsca.evals import CrossResolutionAnalyzer

# Initialize with HiCSCA results (auto-detects resolutions and chromosomes)
analyzer = CrossResolutionAnalyzer(hicsca.results)

# Or specify custom resolutions/chromosomes
analyzer = CrossResolutionAnalyzer(
    hicsca.results,
    resolutions=[500000, 250000, 100000, 50000],
    chr_names=['chr1', 'chr2', ..., 'chr22']
)

# Analyze (results stored internally, cached)
analyzer.analyze()

# Plot genome-wide cross-resolution MCC with colorbar
fig, ax, cbar_fig, cbar_ax = analyzer.plot_cross_resolution_mcc()

# Plot specific chromosome with transparent background
fig, ax, cbar_fig, cbar_ax = analyzer.plot_cross_resolution_mcc(
    chr_name='chr1',
    save_path='chr1_mcc.png',
    background_alpha=0
)
# Saves: chr1_mcc.png and chr1_mcc_colorbar.png

# Access MCC matrices directly
genome_wide_mcc = analyzer.mcc_matrices['all']
chr1_mcc = analyzer.mcc_matrices['chr1']

CrossDatasetAnalyzer

Analyzes agreement between different Hi-C datasets at the same resolution(s).

from hicsca.evals import CrossDatasetAnalyzer

# Create dataset dictionary (dataset_id -> HiCSCA results)
dataset_dict = {
    'dataset1': hicsca_inst1.results,
    'dataset2': hicsca_inst2.results,
    'dataset3': hicsca_inst3.results
}

# Initialize (auto-detects resolutions, dataset_ids, and chromosomes)
analyzer = CrossDatasetAnalyzer(dataset_dict)

# Analyze all resolutions
analyzer.analyze()

# Plot genome-wide MCC correlation at 100kb with colorbar
fig1, ax1, cbar_fig1, cbar_ax1 = analyzer.plot_mcc_correlation(
    100000,
    tick_labels=['A', 'B', 'C'],
    save_path='mcc_100kb.png'
)
# Saves: mcc_100kb.png and mcc_100kb_colorbar.png

# Plot chr1 orientation agreement
fig2, ax2, cbar_fig2, cbar_ax2 = analyzer.plot_orientation_agreement(
    100000,
    chr_name='chr1',
    save_path='orient_chr1_100kb.png'
)

# Access matrices directly
mcc_genome_wide = analyzer.mcc_matrices[100000]['all']

MCCCalculator

Compute Matthews Correlation Coefficient between compartment predictions.

from hicsca.evals import MCCCalculator

mcc, tp, fp, tn, fn, zeroed, reversed = MCCCalculator.compute_AB_MCC(
    reference_compartments,
    predicted_compartments,
    auto_flip=True  # Automatically handle orientation
)

print(f"MCC: {mcc:.4f}")

Other Documentation

• RESULTS_STRUCTURE.md: Complete documentation of results dictionary structure

Citation

If you use this software in your research, please cite:

Chan, J. & Kono, H. HiC-SCA: A Spectral Clustering Method for Reliable A/B Compartment Assignment From Hi-C Data. Preprint at https://doi.org/10.1101/2025.09.22.677711 (2025).

Dependencies

Our package uses hicstraw:

Durand, N.C., Robinson, J.T., Shamim, M.S., Machol, I., Mesirov, J.P., Lander, E.S., and Aiden, E.L. (2016). Juicebox Provides a Visualization System for Hi-C Contact Maps with Unlimited Zoom. Cell Systems 3, 99–101. https://doi.org/10.1016/j.cels.2015.07.012.

LOBPCG algorithm (SciPy) for efficient eigen-decomposition:

Knyazev, A.V., Argentati, M.E., Lashuk, I., and Ovtchinnikov, E.E. (2007). Block Locally Optimal Preconditioned Eigenvalue Xolvers (BLOPEX) in hypre and PETSc. https://doi.org/10.48550/ARXIV.0705.2626.

Sample Data

Sample data from:

Rao, S.S.P., Huntley, M.H., Durand, N.C., Stamenova, E.K., Bochkov, I.D., Robinson, J.T., Sanborn, A.L., Machol, I., Omer, A.D., Lander, E.S., et al. (2014). A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell 159, 1665–1680. https://doi.org/10.1016/j.cell.2014.11.021.

Obtained from the ENCODE database:

Luo, Y., Hitz, B.C., Gabdank, I., Hilton, J.A., Kagda, M.S., Lam, B., Myers, Z., Sud, P., Jou, J., Lin, K., et al. (2020). New developments on the Encyclopedia of DNA Elements (ENCODE) data portal. Nucleic Acids Research 48, D882–D889. https://doi.org/10.1093/nar/gkz1062.

Acknowledgments

This package uses:

• NumPy and SciPy for numerical computations and sparse matrix operations
• matplotlib for visualization
• h5py for HDF5 file I/O

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.