Statistical Power Analysis tool

Version: 1.0

Short Description

Estimation of statistical power and sample size in metabolic phenotyping.

Description

Statistical power analysis tool

This is a Python tool to help you design your experiment in order to gain your expected power with combination of effect and sample size for multivariate data sets.

This tool can only run in Python 2.x.

Key features

• Statistical Power Analysis
• Experiment Design

Functionality

• Statistical Power Analysis
• Random Permutation
• False Discovery Rate (by Benjamini & Hochberg)

Approaches

• Metabolomics / Targeted

Instrument Data Types

• ALL

Screenshots

Tool Authors

• Goncalo Correia (Imperial College London)
• Jianliang Gao (Imperial College London)

Container Contributors

• Jianliang Gao (Imperial College London)

Website

• http://pubs.acs.org/doi/abs/10.1021/acs.analchem.6b00188

Git Repository

• https://github.com/jianlianggao/papy

Installation

For local individual installation:

docker pull docker-registry.phenomenal-h2020.eu/phnmnl/papy:latest

Usage Instructions

For direct docker usage:

docker run --rm -t -v <path/to/data/dir>:/data docker-registry.phenomenal-h2020.eu/phnmnl/papy /data/<testdata_input>.csv <exp_cols_from_input_data> <sample_size> <effect_size> <number of repeats> <outcome_type> <CPU number>

Help and Documentation

Please check the docs for detail at https://jianlianggao.github.io/papy/pa.html

Publications

• Benjamin, J. Blaise, Goncalo Correia, et al., Power Analysis and Sample Size Determination in Metabolic Phenotyping. Bioinformatics, 2016. 88(10): p. 5179-5188. DOI: 10.1021/acs.analchem.6b00188