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Modelling CRISPR dropout data

Project description

Crispy logo

License PyPI version

Method to correct gene independent copy-number effects on CRISPR-Cas9 screens.


Crispy uses Sklearn implementation of Gaussian Process Regression, fitting each sample independently.


Install pybedtools and then install Crispy

conda install -c bioconda pybedtools

pip install cy


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')

# Gaussian Process Regression is stored
crispy.gpr.plot(x_feature='ratio', y_feature='fold_change')


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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Filename, size & hash SHA256 hash help File type Python version Upload date
cy-0.2.7-py3-none-any.whl (4.3 MB) Copy SHA256 hash SHA256 Wheel py3
cy-0.2.7.tar.gz (77.6 kB) Copy SHA256 hash SHA256 Source None

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