Skip to main content

Modelling CRISPR dropout data

Project description

Crispy logo

License PyPI version DOI

Module with utility functions to process CRISPR-based screens and method to correct gene independent copy-number effects.


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


Support to library imports:

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:

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:

import crispy as cy
import matplotlib.pyplot as plt
from crispy.CRISPRData import ReadCounts, Library

Import sample data
rawcounts, copynumber = cy.Utils.get_example_data()

Import CRISPR-Cas9 library

      Library has to have the following columns: "Chr", "Start", "End", "Approved_Symbol"
      Library and segments have to have consistent "Chr" formating: "Chr1" or "chr1" or "1"
      Gurantee that "Start" and "End" columns are int
lib = Library.load_library("Yusa_v1.1.csv.gz")

lib = lib.rename(
    columns=dict(start="Start", end="End", chr="Chr", Gene="Approved_Symbol")
).dropna(subset=["Chr", "Start", "End"])

lib["Chr"] = "chr" + lib["Chr"]

lib["Start"] = lib["Start"].astype(int)
lib["End"] = lib["End"].astype(int)

Calculate fold-change
plasmids = ["ERS717283"]
rawcounts = ReadCounts(rawcounts).remove_low_counts(plasmids)
sgrna_fc = rawcounts.norm_rpm().foldchange(plasmids)

Correct CRISPR-Cas9 sgRNA fold changes
crispy = cy.Crispy(
    sgrna_fc=sgrna_fc.mean(1), copy_number=copynumber, library=lib.loc[sgrna_fc.index]

# Fold-changes and correction integrated funciton.
# Output is a modified/expanded BED formated data-frame with sgRNA and segments information
#   n_sgrna: represents the minimum number of sgRNAs required per segment to consider in the fit.
#            Recomended default values range between 4-10.
bed_df = crispy.correct(n_sgrna=10)

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


Credits and License

Developed at the Wellcome Sanger Institue (2017-2020).

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

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

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cy-0.5.8.tar.gz (56.0 kB view hashes)

Uploaded source

Built Distribution

cy-0.5.8-py3-none-any.whl (52.8 MB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page