isv 0.3.3

Automated Interpretation of Structural Copy Number Variants

ISV package

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

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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

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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

For example

from isv import isv


cnvs = [
    ["chr8", 100000, 500000, "DEL"],
    ["chrX", 52000000, 55000000, "DUP"]
] 

results = isv(cnvs, proba=True, shap=True)

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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

For example

python isv_cmd.py -i examples/loss_gain_cnvs.bed -o examples/loss_gain_cnvs_out.bed -p -sv