Skip to main content

Wishbone algorithm for identifying bifurcating trajectories from single-cell data

Project description

Wishbone

Wishbone is an algorithm to align single cells from differentiation systems with bifurcating branches. Wishbone has been designed to work with multidimensional single cell data from diverse technologies such as Mass cytometry and single cell RNA-seq.

Installation and dependencies

  1. Wishbone has been implemented in Python3 and can be installed using

     $> pip install wishbone_dev
    
  2. Wishbone depends on a number of python3 packages available on pypi and these dependencies are listed in setup.py All the dependencies will be automatically installed using the above commands

Usage

Command line

A tutorial on Wishbone usage and results visualization for single cell RNA-seq data can be found in this notebook: http://nbviewer.jupyter.org/github/ManuSetty/wishbone/blob/master/notebooks/Wishbone_for_single_cell_RNAseq.ipynb

A tutorial on Wishbone usage and results visualization for mass cytometry data can be found in this notebook: http://nbviewer.jupyter.org/github/ManuSetty/wishbone/blob/master/notebooks/Wishbone_for_mass_cytometry.ipynb

GUI

A python GUI is now available for Wishbone. After following the installation steps listed below, the GUI can be invoked using

    $> wishbone_gui.py

A tutorial on using the interface is available in the Wishbone tutorial.

Citation

Wishbone manuscript is available from Nature Biotechnology. If you use Wishbone for your work, please cite our paper.

    @article{Wishbone_2016,
            title = {Wishbone identifies bifurcating developmental trajectories from single-cell data},
            author = {Manu Setty and Michelle D Tadmor and Shlomit Reich-Zeliger and Omer Angel and Tomer Meir Salame and Pooja Kathail and Kristy Choi and Sean Bendall and Nir Friedman and Dana Pe'er},
            journal = {Nature Biotechnology},
            year = {2016},
            month = {may},
            url = {http://dx.doi.org/10.1038/nbt.3569},
            doi = {doi:10.1038/nbt.3569}
    }

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

wishbone_dev-0.5.2.tar.gz (26.2 MB view details)

Uploaded Source

Built Distribution

wishbone_dev-0.5.2-py3-none-any.whl (43.2 kB view details)

Uploaded Python 3

File details

Details for the file wishbone_dev-0.5.2.tar.gz.

File metadata

  • Download URL: wishbone_dev-0.5.2.tar.gz
  • Upload date:
  • Size: 26.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.0.0 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.6.5

File hashes

Hashes for wishbone_dev-0.5.2.tar.gz
Algorithm Hash digest
SHA256 ecc731931b6995c311bf990051a6228b0719da0101ee7def8f66f2a0ac30ac70
MD5 e816eedc31e6a135d823aaca80a5f05c
BLAKE2b-256 7a3a4f9ccf46e624d35b4c08fda6e23c8e06a6c91909dd92acafb05268d5dee0

See more details on using hashes here.

File details

Details for the file wishbone_dev-0.5.2-py3-none-any.whl.

File metadata

  • Download URL: wishbone_dev-0.5.2-py3-none-any.whl
  • Upload date:
  • Size: 43.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.0.0 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.6.5

File hashes

Hashes for wishbone_dev-0.5.2-py3-none-any.whl
Algorithm Hash digest
SHA256 5ed7bbbb9ec130225ee8a4de14f2e690ce8c9720aaa14fb75f30431be67ff616
MD5 db660ca33a3f46618b50a7f57021a2c2
BLAKE2b-256 31a76b28b4b3c58a8c60438a8b9d753cfba7aad6bb3bed98bb1406d41220bc10

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