Tabular Classification
Scikit-learn
Joblib
genomics
structural-variants
short-tandem-repeats
variant-calling
confidence-calibration
random-forest
Instructions to use khyeom/SVSTR-Score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use khyeom/SVSTR-Score with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("khyeom/SVSTR-Score", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
File size: 784 Bytes
90d0b4b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | """SV-SPR: Short-read SV confidence model (caller-agnostic post-VCF rescoring).
Quick start
-----------
>>> from svspr import score, classify, SVSPR
>>>
>>> # Single SV
>>> classify(chrom='chr1', pos=12345, end=13345,
... svtype='DEL', svlen=1000, total_alt_support=15)
{'CS': 0.87, 'tier': 'high'}
>>>
>>> # Full VCF
>>> df = score(vcf_path='input.vcf', ref_path='GRCh38.fa')
>>> df[['chrom', 'pos', 'svtype', 'CS', 'tier']].head()
"""
from .model import SVSPR, score, classify, load_default
from .features import SVCall, FEATURE_COLS, extract_one, extract_batch, from_vcf
__version__ = '0.1.0'
__all__ = ['SVSPR', 'score', 'classify', 'load_default',
'SVCall', 'FEATURE_COLS', 'extract_one', 'extract_batch', 'from_vcf']
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