The PLSDAbatch package includes both a new method for
batch effect correction, along with a comprehensive standardised
framework for batch effect management including the application of
existing methods ranging from accounting for batch effects (e.g. with
linear models) to correcting for batch effects (e.g removeBatchEffect
from the limma package, and ComBat from sva) and a
proposed method for microbiome data.
If you see mistakes or want to suggest changes, please create an issue on the source repository.
For attribution, please cite this work as
Wang (2022, March 23). Di Cook Award: PLSDAbatch R-package . Retrieved from https://statsocaus.github.io/dicook-award/tutorials/2022-03-23-yiwen-eva-wang/
BibTeX citation
@misc{wang2022plsdabatch,
author = {Wang, Yiwen (Eva)},
title = {Di Cook Award: PLSDAbatch R-package },
url = {https://statsocaus.github.io/dicook-award/tutorials/2022-03-23-yiwen-eva-wang/},
year = {2022}
}