Metadata-Version: 2.1
Name: cy
Version: 0.4.11
Summary: Modelling CRISPR dropout data
Home-page: https://github.com/EmanuelGoncalves/crispy
Author: Emanuel Goncalves
Author-email: eg14@sanger.ac.uk
License: UNKNOWN
Description: ![Crispy logo](crispy/data/images/logo.png)
        
        [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![PyPI version](https://badge.fury.io/py/cy.svg)](https://badge.fury.io/py/cy) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2530755.svg)](https://doi.org/10.5281/zenodo.2530755)
        
        
        Module with utility functions to process CRISPR-based screens and method to correct gene independent copy-number effects.
        
        
        Description
        --
        Crispy uses [Sklearn](http://scikit-learn.org/stable/index.html) implementation of [Gaussian Process Regression](http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor), fitting each sample independently.
        
        Install
        --
        
        Install [`pybedtools`](https://daler.github.io/pybedtools/main.html#quick-install-via-conda) and then install `Crispy`
        
        ```
        conda install -c bioconda pybedtools
        
        pip install cy
        ```
        
        Examples
        --
        Support to library imports:
        ```python
        from crispy.CRISPRData import Library
        
        # Master Library, standardised assembly of KosukeYusa V1.1, Avana, Brunello and TKOv3 
        # CRISPR-Cas9 libraries.
        master_lib = Library.load_library("MasterLib_v1.csv.gz")
        
        
        # Genome-wide minimal CRISPR-Cas9 library. 
        minimal_lib = Library.load_library("MinLibCas9.csv.gz")
        
        # Some of the most broadly adopted CRISPR-Cas9 libraries:
        # 'Avana_v1.csv.gz', 'Brunello_v1.csv.gz', 'GeCKO_v2.csv.gz', 'Manjunath_Wu_v1.csv.gz', 
        # 'TKOv3.csv.gz', 'Yusa_v1.1.csv.gz'
        brunello_lib = Library.load_library("Brunello_v1.csv.gz")
        ```
        
        Select sgRNAs (across multiple CRISPR-Cas9 libraries) for a given gene:
        ```python
        from crispy.GuideSelection import GuideSelection
        
        # sgRNA selection class
        gselection = GuideSelection()
        
        # Select 5 optimal sgRNAs for MCL1 across multiple libraries 
        gene_guides = gselection.select_sgrnas(
            "MCL1", n_guides=5, offtarget=[1, 0], jacks_thres=1, ruleset2_thres=.4
        )
        
        # Perform different rounds of sgRNA selection with increasingly relaxed efficiency thresholds 
        gene_guides = gselection.selection_rounds("TRIM49", n_guides=5, do_amber_round=True, do_red_round=True)
        ```
        
        Copy-number correction:
        ```python
        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()
        ```
        ![GPR](crispy/data/images/example_gp_fit.png)
        
        
        Credits and License
        --
        Developed at the [Wellcome Sanger Institue](https://www.sanger.ac.uk/) (2017-2020).
        
        For citation please refer to:
        
        [Gonçalves E, Behan FM, Louzada S, Arnol D, Stronach EA, Yang F, Yusa K, Stegle O, Iorio F, Garnett MJ (2019) Structural 
        rearrangements generate cell-specific, gene-independent CRISPR-Cas9 loss of fitness effects. Genome Biol 20: 27](https://doi.org/10.1186/s13059-019-1637-z)
        
        [Gonçalves E, Thomas M, Behan FM, Picco G, Pacini C, Allen F, Parry-Smith D, Iorio F, Parts L, Yusa K, Garnett MJ (2019) 
        Minimal genome-wide human CRISPR-Cas9 library. bioRxiv](https://www.biorxiv.org/content/10.1101/848895v1)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Description-Content-Type: text/markdown
