https://github.com/biocpy/genomicarrays
https://github.com/biocpy/genomicarrays
ml tiledb
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/biocpy/genomicarrays
- Owner: BiocPy
- License: mit
- Created: 2024-10-28T20:11:30.000Z (7 months ago)
- Default Branch: master
- Last Pushed: 2024-11-18T16:29:46.000Z (6 months ago)
- Last Synced: 2024-11-18T17:52:43.165Z (6 months ago)
- Topics: ml, tiledb
- Language: Python
- Homepage: https://biocpy.github.io/GenomicArrays/
- Size: 271 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Authors: AUTHORS.md
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README
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# Genomic Arrays based on TileDB
GenomicArrays is a Python package for converting genomic data from BigWig format to TileDB arrays.
## Installation
Install the package from [PyPI](https://pypi.org/project/genomicarrays/)
```sh
pip install genomicarrays
```
## Quick Start
### Build a `GenomicArray`
Building a `GenomicArray` generates 3 TileDB files in the specified output directory:
- `feature_annotation`: A TileDB file containing input feature intervals.
- `sample_metadata`: A TileDB file containing sample metadata, each BigWig file is considered a sample.
- A matrix TileDB file named by the `layer_matrix_name` parameter. This allows the package
to store multiple different matrices, e.g. 'coverage', 'some_computed_statistic', for the same interval,
and sample metadata attributes.
The organization is inspired by the [SummarizedExperiment](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html) data structure. The TileDB matrix file is stored in a **features X samples** orientation.
To build a `GenomicArray` from a collection of `BigWig` files:
```python
import numpy as np
import tempfile
import genomicarrays as garr
# Create a temporary directory, this is where the
# output files are created. Pick your location here.
tempdir = tempfile.mkdtemp()
# List BigWig paths
bw_dir = "your/biwig/dir"
files = os.listdir(bw_dir)
bw_files = [f"{bw_dir}/{f}" for f in files]
features = pd.DataFrame({
"seqnames": ["chr1", "chr1"],
"starts": [1000, 2000],
"ends": [1500, 2500]
})
# Build GenomicArray
dataset = garr.build_genomicarray(
files=bw_files,
output_path=tempdir,
features=features,
# Specify a fasta file to extract sequences
# for each region in features
genome_fasta="path/to/genome.fasta",
# agg function to summarize mutiple values
# from bigwig within an input feature interval.
feature_annotation_options=garr.FeatureAnnotationOptions(
aggregate_function = np.nanmean
),
# for parallel processing multiple bigwig files
num_threads=4
)
```
The build process stores missing intervals from a bigwig file as `np.nan`. The
default is to choose an aggregate functions that works with `np.nan`.
### Query a `GenomicArrayDataset`
Users have the option to reuse the `dataset` object retuned when building the arrays or by creating a `GenomicArrayDataset` object by initializing it to the path where the files were created.
```python
# Create a GenomicArrayDataset object from the existing dataset
dataset = GenomicArrayDataset(dataset_path=tempdir)
# Query data for the first 10 regions across all samples
coverage_data = dataset[0:10, :]
print(expression_data.matrix)
print(expression_data.feature_annotation)
```
## output 1
array([[1. , 0.5],
[1. , 0.5],
[1. , 0.5],
[1. , 0.5],
[1. , 0.5],
[1. , 0.5],
[1. , 0.5],
[1. , 0.5],
[1. , 0.5],
[1. , 0.5],
[1. , nan]], dtype=float32)
## output 2
seqnames starts ends genarr_feature_index
0 chr1 300 315 0
1 chr1 320 335 1
2 chr1 340 355 2
3 chr1 360 375 3
4 chr1 380 395 4
5 chr1 400 415 5
6 chr1 420 435 6
7 chr1 440 455 7
8 chr1 460 475 8
9 chr1 480 495 9
10 chr1 500 515 10
## Note
This project has been set up using PyScaffold 4.6. For details and usage
information on PyScaffold see https://pyscaffold.org/.