r-bioinformatics/edgePy

design testing procedures

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#10 opened on May 16, 2018

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help wanted

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Description

Design procedures for testing the analysis workflow and comparing it to the edgeR/limma/Glimma R implementation so that we can ensure reproducibility, test performance, features, parity, upgrades, etc.

This could possibly include things such as:

  • reproducing DGE analysis from published studies in both the R workflows and the python workflows.
  • performing DGE analysis on publicly available datasets such as human cell line data, public data from GEO, etc, as long as the datasets are openly available to use.

Contributor guide

Research direction
Design test procedures to compare edgePy outputs with edgeR/limma/Glimma using public datasets. Focus on reproducing published DGE analyses and ensure feature parity.
Tech stack
python
Domain
testing
Issue type
Test
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonRDifferential Expression Analysis
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
40