Skip to main content

Evaluattion of Predictive CapabilitY for ranking biomarker candidates.

Project description

https://img.shields.io/badge/python-3.6-blue.svg https://travis-ci.org/iric-soft/epcy.svg?branch=master https://codecov.io/gh/iric-soft/epcy/branch/master/graph/badge.svg

Citing:

Introduction:

This tool was developed to Evaluate Predictive CapabilitY of each gene (feature) to become a predictive (bio)marker candidates. Documentation is available via Read the Docs.

Requirements:

  • python3

  • (Optional) virtualenv

Install:

Using pypi:

pip install epcy

From source:

python3 -m venv $HOME/.virtualenvs/epcy
source $HOME/.virtualenvs/epcy/bin/activate
pip install pip setuptools --upgrade
pip install wheel
cd [your_epcy_folder]
# If need it
# CFLAGS=-std=c99 pip3 install numpy==1.17.0
python3 setup.py install
epcy -h

Usage:

General:

After install:

epcy -h

From source:

cd [your_epcy_folder]
python3 -m epcy -h

Generic case:

  • EPCY is design to work on any quantitative data, provided that values of each feature are comparable between each samples (normalized).

  • To run a comparative analysis, epcy pred need two tabulated files:

    • A matrix of quantitative normalized data for each samples (column) with an “ID” column to identify each feature.

    • A design table which describe the comparison.

# Run epcy on any normalized quantification data
epcy pred -d ./data/small_for_test/design.tsv -m ./data/small_for_test/log_normalized_matrix.tsv -o ./data/small_for_test/EPCY_output

# If your data are normalized, but require a log2 transforamtion, add --log
epcy pred --log -d ./data/small_for_test/design.tsv -m ./data/small_for_test/normalized_matrix.tsv -o ./data/small_for_test/EPCY_output

# If your data are not normalized and require a log2 transforamtion, add --norm --log
epcy pred --norm --log -d ./data/small_for_test/design.tsv -m ./data/small_for_test/matrix.tsv -o ./data/small_for_test/EPCY_output

# Different runs might show small variations.
# To ensure reproducibility set a random seed, using --randomseed
epcy pred -d ./data/small_for_test/design.tsv -m ./data/small_for_test/normalized_matrix.tsv -o ./data/small_for_test/EPCY_output --randomseed 42
epcy pred -d ./data/small_for_test/design.tsv -m ./data/small_for_test/normalized_matrix.tsv -o ./data/small_for_test/EPCY_output2 --randomseed 42
diff ./data/small_for_test/EPCY_output/predictive_capability.tsv ./data/small_for_test/EPCY_output2/predictive_capability.tsv

More documentation is available via Read the Docs.

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

epcy-0.2.5.tar.gz (30.8 kB view hashes)

Uploaded Source

Built Distribution

epcy-0.2.5-py3-none-any.whl (40.3 kB view hashes)

Uploaded Python 3

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