https://github.com/gagneurlab/abexp-utils
https://github.com/gagneurlab/abexp-utils
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/gagneurlab/abexp-utils
- Owner: gagneurlab
- License: mit
- Created: 2024-09-03T15:20:38.000Z (9 months ago)
- Default Branch: master
- Last Pushed: 2025-02-20T11:28:23.000Z (3 months ago)
- Last Synced: 2025-03-28T07:35:57.745Z (about 2 months ago)
- Language: Python
- Size: 52.9 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
from conftest import output_dir
# AbExp-utils
This repository contains python packages for the [AbExp](https://github.com/gagneurlab/abexp) project.
## AbExp-utils
This package contains utility functions for the [AbExp](https://github.com/gagneurlab/abexp) project.
It contains functions to manipulates polars and spark dataframes, as well as scikit-learn models used by AbExp.
### Installation
The package can be installed using pip.
```bash
# spark
pip install packages/abexp_utils[spark]
# polars
pip install packages/abexp_utils[polars]
# both
pip install packages/abexp_utils[all]
```
## Kipoi-enformer
Variant effect prediction of promoter variants using the [Enformer](https://github.com/google-deepmind/deepmind-research/tree/master/enformer) model.
### Installation
The package can be installed using pip.
```bash
# without cuda support
pip install packages/kipoi_enformer
# with cuda support
pip install packages/kipoi_enformer[gpu]
```
### Usage
```python
from kipoi_enformer.dataloader import RefTSSDataloader, VCFTSSDataloader
from kipoi_enformer.enformer import Enformer, EnformerAggregator, EnformerTissueMapper, EnformerVeff
from pathlib import Path
from sklearn import linear_model
# define output dirs
output_dir = Path('output')
(output_dir / 'raw/ref.parquet/chrom=chr22').mkdir(exist_ok=True, parents=True)
(output_dir / 'raw/alt.parquet').mkdir(exist_ok=True, parents=True)
(output_dir / 'aggregated/ref.parquet/chrom=chr22').mkdir(exist_ok=True, parents=True)
(output_dir / 'aggregated/alt.parquet').mkdir(exist_ok=True, parents=True)
(output_dir / 'tissue/ref.parquet/chrom=chr22').mkdir(exist_ok=True, parents=True)
(output_dir / 'tissue/alt.parquet').mkdir(exist_ok=True, parents=True)
# define enformer objects
enformer = Enformer()
enformer_aggregator = EnformerAggregator()
enformer_tissue_mapper = EnformerTissueMapper(tracks_path='assets/enformer_tracks/human_cage_enformer_tracks.yaml')
enformer_veff = EnformerVeff(gtf='example_files/annotation.gtf.gz')
# Reference sequences
# define reference dataloader
ref_dl = RefTSSDataloader(fasta_file='example_files/seq.fa', gtf='example_files/annotation.gtf.gz', chromosome='chr22',
canonical_only=False, protein_coding_only=True)
# run enformer on reference genome
enformer.predict(ref_dl, batch_size=2, filepath=output_dir / 'raw/ref.parquet/chrom=chr22/data.parquet',
num_output_bins=11)
# aggregate reference enformer scores
enformer_aggregator.aggregate(output_dir / 'raw/ref.parquet/chrom=chr22/data.parquet',
output_dir / 'aggregated/ref.parquet/chrom=chr22/data.parquet')
# train tissue mapper using reference genome
enformer_tissue_mapper.train([output_dir / 'aggregated/ref.parquet/chrom=chr22/data.parquet'],
output_path=output_dir / 'tissue_mapper.pkl',
expression_path="example_files/isoform_proportions.tsv",
model=linear_model.ElasticNetCV(cv=2))
# map reference to tissues
enformer_tissue_mapper.predict(output_dir / 'aggregated/ref.parquet/chrom=chr22/data.parquet',
output_dir / 'tissue/ref.parquet/chrom=chr22/data.parquet')
# Alternative sequences
# define alternative dataloader
alt_dl = VCFTSSDataloader(fasta_file='example_files/seq.fa', gtf='example_files/annotation.gtf.gz',
vcf_file='example_files/vcf/chr22_var.vcf.gz', variant_upstream_tss=50,
variant_downstream_tss=200, canonical_only=False, protein_coding_only=True)
# run enformer on alternative genome
enformer.predict(alt_dl, batch_size=2, filepath=output_dir / 'raw/alt.parquet/chr22_var.vcf.gz.parquet',
num_output_bins=11)
# aggregate alternative enformer scores
enformer_aggregator.aggregate(output_dir / 'raw/alt.parquet/chr22_var.vcf.gz.parquet',
output_dir / 'aggregated/alt.parquet/chr22_var.vcf.gz.parquet')
# map alternative to tissues
enformer_tissue_mapper.predict(output_dir / 'aggregated/alt.parquet/chr22_var.vcf.gz.parquet',
output_dir / 'tissue/alt.parquet/chr22_var.vcf.gz.parquet')
# Variant effect prediction
enformer_veff.run(ref_paths=[output_dir / 'tissue/ref.parquet/chrom=chr22/data.parquet'],
alt_path=output_dir / 'tissue/alt.parquet/chr22_var.vcf.gz.parquet',
output_path=output_dir / 'veff.parquet', aggregation_mode='canonical')
```