Modelling CRISPR dropout data
Project description
Crispy
Identify associations between genomic alterations (e.g. structural variation, copy-number variation) and CRISPR-Cas9 knockout response.
Tandem duplications lead to loss of fitness effects in CRISPR-Cas9 data
Description
Crispy uses Sklearn implementation of Gaussian Process Regression, fitting by default each chromosome of each sample independently.
Example
import pandas as pd
from crispy.association import CRISPRCorrection
# Import data
data = pd.read_csv('extdata/association_example_data.csv', index_col=0)
# Association analysis
crispy = 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])
Install
python setup.py install
Enrichment module has Cython files, to compile run:
python crispy/enrichment/gsea_setup.py build_ext --inplace
Regression module has Cython files, to compile run:
python crispy/regression/linear_setup.py build_ext --inplace
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