Modelling CRISPR dropout data
Project description
Identify associations between genomic alterations (e.g. structural variation, copy-number variation) and CRISPR-Cas9 knockout response.
Description
Crispy uses Sklearn implementation of Gaussian Process Regression, fitting by default each chromosome of each sample independently.
Example
import pandas as pd
import crispy as cy
# Import data
data = cy.get_example_data()
# Association analysis
crispy = cy.CRISPRCorrection()\
.fit_by(by=data['chr'], X=data[['cnv']], y=data['fc'])
# Export
crispy = pd.concat([v.to_dataframe() for k, v in crispy.items()])\
.sort_values(['cnv', 'k_mean'], ascending=[False, True])
print(crispy)
Install
pip install cy
Enrichment and Regression module has Cython files, to compile run:
python crispy/enrichment/gsea_setup.py build_ext --inplace
python crispy/regression/linear_setup.py build_ext --inplace
Credits and License
Developed at the Wellcome Sanger Institue (2017-2018).
For citation please refer to: biorxiv pre-print - Tandem duplications lead to loss of fitness effects in CRISPR-Cas9 data
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