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Automated Interpretation of Structural Copy Number Variants

Project description

ISV package

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


Install

Install with pip install isv

This will also automatically install all required additional packages. Thus it is recommended to install the package in a separate environment (e.g. virtualenv, conda, ...)

Package url: https://pypi.org/project/isv/

Module reference available at https://tsladecek.github.io/isv_package/


Modules

The package contains a wrapper 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 a Wrapper class (which is recommended):

isv.ISV(cnvs)

with methods:

  • ISV.predict(proba)
  • ISV.shap(data=None)
    • where the data argument is optional
  • ISV.waterfall(cnv_index)
    • for creating an interactive waterfall plot for a CNV at index cnv_index

The main subfunctions of the package are:

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, proba)

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

3. isv.shap_values(annotated_cnvs)

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

For example

  1. using the simple wrapper
from isv import isv


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

results = isv(cnvs, proba=True, shap=True)
  1. using the ISV class
from isv import ISV


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

cnv_isv = ISV(cnvs)
predictions = cnv_isv.predict(proba=True)
shap_vals = cnv_isv.shap()
cnv_isv.waterfall(cnv_index=1)

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

Results will be saved in a tab separated file at path specified by user

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

Project details


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