Skip to main content

Evaluation of Predictive CapabilitY

Project description

https://zenodo.org/badge/197271057.svg https://img.shields.io/badge/python-3.11.5-blue.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:

  • python >= 3.11.5

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]
pip install -e .
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-0.2.6.4.tar.gz (2.5 MB view details)

Uploaded Source

Built Distribution

epcy-0.2.6.4-py3-none-any.whl (40.4 kB view details)

Uploaded Python 3

File details

Details for the file epcy-0.2.6.4.tar.gz.

File metadata

  • Download URL: epcy-0.2.6.4.tar.gz
  • Upload date:
  • Size: 2.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.11.9

File hashes

Hashes for epcy-0.2.6.4.tar.gz
Algorithm Hash digest
SHA256 d8bc4a1ef2c33ffb605d4d381f40fd167197bfe236ce95439a666625e5154af3
MD5 70c48da7f4d35eda4f0146442d07402a
BLAKE2b-256 2d1ae473eec1f54304a5cf085708cd9a4867efae38341d1f5606e52406ed92ee

See more details on using hashes here.

File details

Details for the file epcy-0.2.6.4-py3-none-any.whl.

File metadata

  • Download URL: epcy-0.2.6.4-py3-none-any.whl
  • Upload date:
  • Size: 40.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.11.9

File hashes

Hashes for epcy-0.2.6.4-py3-none-any.whl
Algorithm Hash digest
SHA256 8384c513c6f83addfe2f69a129aaec60fb0ba2c058d98d2922a643646673c3a2
MD5 c2b761992d600e809f7328fc54521808
BLAKE2b-256 65b624949c2a4dbae44319fe8228007d5098984c8c9c4bef5b70524cb7744782

See more details on using hashes here.

Supported by

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