geniml

Geniml is a Python package for building machine learning models on genomic interval data from BED files. It provides unsupervised methods for learning embeddings of genomic regions, single cells, and metadata labels, enabling similarity searches, clustering, and downstream ML tasks.

Install geniml using uv:

```bash

uv uv pip install geniml

```

For ML dependencies (PyTorch, etc.):

```bash

uv uv pip install 'geniml[ml]'

```

Development version from GitHub:

```bash

uv uv pip install git+https://github.com/databio/geniml.git

```

Geniml provides five primary capabilities, each detailed in dedicated reference files:

1. Region2Vec: Genomic Region Embeddings

Train unsupervised embeddings of genomic regions using word2vec-style learning.

Use for: Dimensionality reduction of BED files, region similarity analysis, feature vectors for downstream ML.

Workflow:

1. Tokenize BED files using a universe reference
2. Train Region2Vec model on tokens
3. Generate embeddings for regions

Reference: See references/region2vec.md for detailed workflow, parameters, and examples.

2. BEDspace: Joint Region and Metadata Embeddings

Train shared embeddings for region sets and metadata labels using StarSpace.

Use for: Metadata-aware searches, cross-modal queries (region→label or label→region), joint analysis of genomic content and experimental conditions.

Workflow:

1. Preprocess regions and metadata
2. Train BEDspace model
3. Compute distances
4. Query across regions and labels

Reference: See references/bedspace.md for detailed workflow, search types, and examples.

3. scEmbed: Single-Cell Chromatin Accessibility Embeddings

Train Region2Vec models on single-cell ATAC-seq data for cell-level embeddings.

Use for: scATAC-seq clustering, cell-type annotation, dimensionality reduction of single cells, integration with scanpy workflows.

Workflow:

1. Prepare AnnData with peak coordinates
2. Pre-tokenize cells
3. Train scEmbed model
4. Generate cell embeddings
5. Cluster and visualize with scanpy

Reference: See references/scembed.md for detailed workflow, parameters, and examples.

4. Consensus Peaks: Universe Building

Build reference peak sets (universes) from BED file collections using multiple statistical methods.

Use for: Creating tokenization references, standardizing regions across datasets, defining consensus features with statistical rigor.

Workflow:

1. Combine BED files
2. Generate coverage tracks
3. Build universe using CC, CCF, ML, or HMM method

Methods:

• CC (Coverage Cutoff): Simple threshold-based
• CCF (Coverage Cutoff Flexible): Confidence intervals for boundaries
• ML (Maximum Likelihood): Probabilistic modeling of positions
• HMM (Hidden Markov Model): Complex state modeling

Reference: See references/consensus_peaks.md for method comparison, parameters, and examples.

5. Utilities: Supporting Tools

Additional tools for caching, randomization, evaluation, and search.

Available utilities:

• BBClient: BED file caching for repeated access
• BEDshift: Randomization preserving genomic context
• Evaluation: Metrics for embedding quality (silhouette, Davies-Bouldin, etc.)
• Tokenization: Region tokenization utilities (hard, soft, universe-based)
• Text2BedNN: Neural search backends for genomic queries

Reference: See references/utilities.md for detailed usage of each utility.

Basic Region Embedding Pipeline

```python

from geniml.tokenization import hard_tokenization

from geniml.region2vec import region2vec

from geniml.evaluation import evaluate_embeddings

# Step 1: Tokenize BED files

hard_tokenization(

src_folder='bed_files/',

dst_folder='tokens/',

universe_file='universe.bed',

p_value_threshold=1e-9

)

# Step 2: Train Region2Vec

region2vec(

token_folder='tokens/',

save_dir='model/',

num_shufflings=1000,

embedding_dim=100

)

# Step 3: Evaluate

metrics = evaluate_embeddings(

embeddings_file='model/embeddings.npy',

labels_file='metadata.csv'

)

```

scATAC-seq Analysis Pipeline

```python

import scanpy as sc

from geniml.scembed import ScEmbed

from geniml.io import tokenize_cells

# Step 1: Load data

adata = sc.read_h5ad('scatac_data.h5ad')

# Step 2: Tokenize cells

tokenize_cells(

adata='scatac_data.h5ad',

universe_file='universe.bed',

output='tokens.parquet'

)

# Step 3: Train scEmbed

model = ScEmbed(embedding_dim=100)

model.train(dataset='tokens.parquet', epochs=100)

# Step 4: Generate embeddings

embeddings = model.encode(adata)

adata.obsm['scembed_X'] = embeddings

# Step 5: Cluster with scanpy

sc.pp.neighbors(adata, use_rep='scembed_X')

sc.tl.leiden(adata)

sc.tl.umap(adata)

```

Universe Building and Evaluation

```bash

# Generate coverage

cat bed_files/*.bed > combined.bed

uniwig -m 25 combined.bed chrom.sizes coverage/

# Build universe with coverage cutoff

geniml universe build cc \

--coverage-folder coverage/ \

--output-file universe.bed \

--cutoff 5 \

--merge 100 \

--filter-size 50

# Evaluate universe quality

geniml universe evaluate \

--universe universe.bed \

--coverage-folder coverage/ \

--bed-folder bed_files/

```

Geniml provides command-line interfaces for major operations:

```bash

# Region2Vec training

geniml region2vec --token-folder tokens/ --save-dir model/ --num-shuffle 1000

# BEDspace preprocessing

geniml bedspace preprocess --input regions/ --metadata labels.csv --universe universe.bed

# BEDspace training

geniml bedspace train --input preprocessed.txt --output model/ --dim 100

# BEDspace search

geniml bedspace search -t r2l -d distances.pkl -q query.bed -n 10

# Universe building

geniml universe build cc --coverage-folder coverage/ --output universe.bed --cutoff 5

# BEDshift randomization

geniml bedshift --input peaks.bed --genome hg38 --preserve-chrom --iterations 100

```

Use Region2Vec when:

• Working with bulk genomic data (ChIP-seq, ATAC-seq, etc.)
• Need unsupervised embeddings without metadata
• Comparing region sets across experiments
• Building features for downstream supervised learning

Use BEDspace when:

• Metadata labels available (cell types, tissues, conditions)
• Need to query regions by metadata or vice versa
• Want joint embedding space for regions and labels
• Building searchable genomic databases

Use scEmbed when:

• Analyzing single-cell ATAC-seq data
• Clustering cells by chromatin accessibility
• Annotating cell types from scATAC-seq
• Integration with scanpy is desired

Use Universe Building when:

• Need reference peak sets for tokenization
• Combining multiple experiments into consensus
• Want statistically rigorous region definitions
• Building standard references for a project

Use Utilities when:

• Need to cache remote BED files (BBClient)
• Generating null models for statistics (BEDshift)
• Evaluating embedding quality (Evaluation)
• Building search interfaces (Text2BedNN)

General Guidelines

• Universe quality is critical: Invest time in building comprehensive, well-constructed universes
• Tokenization validation: Check coverage (>80% ideal) before training
• Parameter tuning: Experiment with embedding dimensions, learning rates, and training epochs
• Evaluation: Always validate embeddings with multiple metrics and visualizations
• Documentation: Record parameters and random seeds for reproducibility

Performance Considerations

• Pre-tokenization: For scEmbed, always pre-tokenize cells for faster training
• Memory management: Large datasets may require batch processing or downsampling
• Computational resources: ML/HMM universe methods are computationally intensive
• Model caching: Use BBClient to avoid repeated downloads

Integration Patterns

• With scanpy: scEmbed embeddings integrate seamlessly as adata.obsm entries
• With BEDbase: Use BBClient for accessing remote BED repositories
• With Hugging Face: Export trained models for sharing and reproducibility
• With R: Use reticulate for R integration (see utilities reference)

Geniml is part of the BEDbase ecosystem:

• BEDbase: Unified platform for genomic regions
• BEDboss: Processing pipeline for BED files
• Gtars: Genomic tools and utilities
• BBClient: Client for BEDbase repositories

• Documentation: https://docs.bedbase.org/geniml/
• GitHub: https://github.com/databio/geniml
• Pre-trained models: Available on Hugging Face (databio organization)
• Publications: Cited in documentation for methodological details

"Tokenization coverage too low":

• Check universe quality and completeness
• Adjust p-value threshold (try 1e-6 instead of 1e-9)
• Ensure universe matches genome assembly

"Training not converging":

• Adjust learning rate (try 0.01-0.05 range)
• Increase training epochs
• Check data quality and preprocessing

"Out of memory errors":

• Reduce batch size for scEmbed
• Process data in chunks
• Use pre-tokenization for single-cell data

"StarSpace not found" (BEDspace):

• Install StarSpace separately: https://github.com/facebookresearch/StarSpace
• Set --path-to-starspace parameter correctly

For detailed troubleshooting and method-specific issues, consult the appropriate reference file.