Modelling CRISPR dropout data
Project description
Method to correct gene independent copy-number effects on CRISPR-Cas9 screens.
Description
Crispy uses Sklearn implementation of Gaussian Process Regression, fitting each sample independently.
Install
pip install cy
Example
import crispy as cy
import matplotlib.pyplot as plt
# Import data
rawcounts, copynumber = cy.Utils.get_example_data()
# Import CRISPR-Cas9 library
lib = cy.Utils.get_crispr_lib()
# Instantiate Crispy
crispy = cy.Crispy(
raw_counts=rawcounts, copy_number=copynumber, library=lib
)
# Fold-changes and correction integrated funciton.
# Output is a modified/expanded BED formated data-frame with sgRNA and segments information
bed_df = crispy.correct(x_features='ratio', y_feature='fold_change')
print(bed_df.head())
# Gaussian Process Regression is stored
crispy.gpr.plot(x_feature='ratio', y_feature='fold_change')
plt.show()
Credits and License
Developed at the Wellcome Sanger Institue (2017-2018).
For citation please refer to: biorxiv pre-print
Project details
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