Significance Weighted Principal Component Analysis (SWPCA)
SWPCA is a technique (1) developed to parse out the influence of a categorical variable that introduces variability in a certain dataset. This was originally intended to remove acquisition site variance in neuroimaging databases.
To use the script to remove, navigate to the download dir, load the library (
import swpca) into your environment and execute this command using the current
dataset and acquisition
import swpca dataset_rect,weights,A =swpca.swpca(dataset, site)
It will return the rectified dataset, to be used in subsequent analysis.
- Francisco Jesús Martinez-Murcia et al. On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis Human Brain Mapping, Access online. 2016. http://dx.doi.org/10.1002/hbm.23449