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Automated Interpretation of Structural Copy Number Variants

Project description

ISV package

Python package for easy prediction of pathogenicity Copy Number Variants (CNVs)


Requirements

xgboost>=1.4.2
shap>=0.39.0
sklearn-json>=0.1.0
numba>=0.53.1
numpy>=1.20.3
pandas>=1.2.4
numba>=0.53.1

The package might work with older versions, however above specified versions are recommended. Make sure to install these packages before installing the isv package


The main function of the package is the function

isv.isv(cnvs, proba, shap)

which automatically annotates and predicts cnvs provided in a list, np.array or pandas DataFrame format represented in 4 columns: chromosome, start (grch38), end (grch38) and cnv_type

  • The proba parameter controls whether probabilities should be calculated
  • The shap parameter controls whether shap values should be calculated

And three main subfunction functions:

1. isv.annotate(cnvs)

  • annotates cnvs provided in a list, np.array or pandas DataFrame format represented in 4 columns: chromosome, start (grch38), end (grch38) and cnv_type
  • Returns an annotated dataframe which can be used as an input to following two functions

2. isv.predict(annotated_cnvs, cnv_type, proba)

  • returns an array of isv predictions. annotated_cnvs represents annotated cnvs returned by the annotate function

3. isv.shap_values(annotated_cnvs, cnv_type)

  • calculates shap values for given CNVs. annotated_cnvs represents annotated cnvs returned by the annotate function

Can be also used as a command line tool. Make sure to:

1. clone the repository (https://github.com/tsladecek/isv_package)

2. install requirements, e.g.

virtualenv venv
source venv/bin/activate
pip install -r requirements.txt

3. Use ISV!

python isv_cmd.py -i <input_cnvs>.bed -o <outputpath> [-p] [-sv]

where the input should be a list of CNVs in a bed format, with columns: chromosome, start (grch38), end (grch38) and cnv_type

File where results should be saved (as a tab-separated table)

Optionally, use following flags:

  • -p: whether probabilities should be returned
  • -sv: whether shap values should be calculated

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