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Significance Weighted Principal Component Analysis
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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 site variables:

import swpca
dataset_rect,weights,A =swpca.swpca(dataset, site)

It will return the rectified dataset, to be used in subsequent analysis.

  1. 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.
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