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A data mining suite for gene expression data

Candis is an open source data mining suite (released under the GNU General Public License v3) for gene expression data that consists of a wide collection of tools you require, right from Data Extraction to Model Deployment. candis is built on top of the toolkit - CancerDiscover written by the bioinformaticians at HelikarLab.

Citation: If you use candis please cite our work
Mohammed, A., Biegert, G., Adamec, J., & Helikar, T. (2017). Identification of potential tissue-specific cancer biomarkers and development of cancer versus normal genomic classifiers. Oncotarget, 8(49), 85692-85715. https://doi.org/10.18632/oncotarget.21127

Or

Mohammed, A., Biegert, G., Adamec, J., & Helikar, T. (2018). CancerDiscover: An integrative pipeline for cancer biomarker and cancer class prediction from high-throughput sequencing data. Oncotarget, 9(2), 2565-2573. https://doi.org/10.18632/oncotarget.23511

WARNING: candis currently is still in dev mode and not production-ready yet. In case if you run across bugs or errors, raise an issue over here.

Table of Contents

• Installation
• Usage
• Features
• Dependencies
• Team
• License

Installation

Assuming you've installed dependencies, simply

$ pip install candis

TL;DR

$ curl -sL git.io/install-candis | python # with dependencies

... and launch candis's development server:

$ candis

To install candis right from scratch, check out our exhaustive guides:

• A Hitchhiker's Guide to Installing candis on Mac OS X
• A Hitchhiker's Guide to Installing candis on Linux OS (In Progress)
• A Hitchhiker's Guide to Installing candis on Windows OS (Contributors Wanted)

Docker Image

You can also attempt to install candis via Docker as follows:

$ docker pull helikarlab/candis

... and simply run the image optionally mapping the port 5000.

$ docker run -p 8888:5000 helikarlab/candis

OR

After cloning the repository, build from the updated Dockerfile and docker-compose.yml:

For development:

$ ./manage up -d --build

For production:

$ CANDIS_ENVIRONMENT=production ./manage up -d --build

Then go to localhost:5000 in your browser to open the app.

Other Commands:

$ ./manage [service] [command]

$ ./manage db backup			 		# Backup the database
$ ./manage db restore /path/to/backup	# Restore a snapshot
$ ./manage db backups 				 	# List all backups

Usage

Launching the RIA (Rich Internet Application)

via CLI

$ candis

OR

$ python -m candis

via Python

>>> import candis
>>> candis.main()

Using the CLI (Command Line Interface)

$ candis --cdata path/to/data.cdata --config path/to/config.json

Using the Jupyter Notebook from inside the docker container

• Starting the jupyter notebook server inside the candis app container

$ docker-compose exec app jupyter notebook --ip 0.0.0.0 --no-browser --allow-root

Features

• Converting a CDATA to an ARFF file

   >>> import candis
   >>> cdata = candis.cdata.read('path/to/data.cdata')

  Then, simply use the CData.toARFF API:

   >>> cdata.toARFF('path/to/data.arff')

• Running a Pipeline.

   >>> pipe = candis.Pipeline()
   >>> pipe.run(cdata)
   >>> while pipe.status == candis.Pipeline.RUNNING:
   ...     # do something while pipeline is running

Dependencies

• Production Dependencies
  • R
  • WEKA (NOTE: Requires Java)
  • Python 3.6+ and PIP (Python's Package Manager)
  • NumPy
• Development Dependencies
  • Node.js
  • SASS

Team

Dr. Tomas Helikar [email protected] Principal Investigator

Dr. Akram Mohammed [email protected] Author and Maintainer

Achilles Rasquinha [email protected] Author and Maintainer

Rupav Jain [email protected] Author and Maintainer

License

This software has been released under the GNU General Public License v3.