Relevant literature
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- R
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Description
Check this [paper: Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4072626/) (PMC4072626) that measures how bad batch effects are. Perhaps the same simulations could be used to show how to avoid them. [code](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4072626/bin/pone.0100335.s002.html#TOC)
> The bias in the cross-validation performance estimates is not eliminated by the batch effect removal, and consequently the cross-validation performance estimates obtained after batch effect elimination are not more reliable measures of the true performance than those obtained without batch effect elimination.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the linked paper, PMC4072626, and its supplied simulation code. Compare the paper's batch-effect and cross-validation simulations with the scope of experDesign, then clarify whether reproducing them is wanted and what results would count as done; no repository file or test is named.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- r
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100