Skip to main content

Infercnv is a scalable python library to infer copy number variation (CNV) events from single cell transcriptomics data. It is heavliy inspired by InferCNV, but plays nicely with scanpy and is much more scalable.

Project description

infercnvpy: Scanpy plugin to infer copy number variation (CNV) from single-cell transcriptomics data

Tests Documentation PyPI

Infercnv is a scalable python library to infer copy number variation (CNV) events from single cell transcriptomics data. It is heavliy inspired by InferCNV, but plays nicely with scanpy and is much more scalable.

The main result of infercnv

WARNING:

This package is still experimental. The results have not been validated, except in that they look similar, but not identical, to the results of InferCNV.

We are happy about feedback and welcome contributions!

Getting started

Please refer to the documentation. In particular, the

Installation

You need to have Python 3.8 or newer installed on your system. If you don't have Python installed, we recommend installing Mambaforge.

There are several alternative options to install infercnvpy:

  1. Install the latest release of infercnvpy from PyPI <https://pypi.org/project/infercnvpy/>_:
pip install infercnvpy
  1. Install the latest development version:
pip install git+https://github.com/icbi-lab/infercnvpy.git@main

To (optionally) run the copyKAT algorithm, you need a working R installation and the copykat package installed. Usually, if R is in your PATH, rpy2 automatically detects your R installation. If you get an error message while importing infercnvpy, try setting the R_HOME environment variable before importing infercnvpy:

import os

os.environ["R_HOME"] = "/usr/lib/R"
import infercnvpy

Release notes

See the changelog.

Contact

For questions and help requests, you can reach out in the scverse discourse. If you found a bug, please use the issue tracker.

Citation

n/a

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

infercnvpy-0.4.2.tar.gz (50.5 MB view details)

Uploaded Source

Built Distribution

infercnvpy-0.4.2-py3-none-any.whl (3.8 MB view details)

Uploaded Python 3

File details

Details for the file infercnvpy-0.4.2.tar.gz.

File metadata

  • Download URL: infercnvpy-0.4.2.tar.gz
  • Upload date:
  • Size: 50.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.9

File hashes

Hashes for infercnvpy-0.4.2.tar.gz
Algorithm Hash digest
SHA256 3cfd23afc2dbbca3508a305032c032440d2af292d037d43a7b5046c7bd36ed22
MD5 183746e2bd662a4b37bfbee8faaa1302
BLAKE2b-256 f73736f22ccb623b712119392f768a0277cfb0560e75f47b4492fb22e84f9f79

See more details on using hashes here.

File details

Details for the file infercnvpy-0.4.2-py3-none-any.whl.

File metadata

  • Download URL: infercnvpy-0.4.2-py3-none-any.whl
  • Upload date:
  • Size: 3.8 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.9

File hashes

Hashes for infercnvpy-0.4.2-py3-none-any.whl
Algorithm Hash digest
SHA256 f8355fd3e1c60ec9750ae3c388fae15c00fd4feeeb0d856c0e18bbf8ce7c1ea7
MD5 f389d5a55bdbe2cc338f5a1280d7e8a3
BLAKE2b-256 90b4743e4b46428eb39d7cbd8f911c19ea159cf4e87f6bcc5dc1aa42a19598f7

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page