Skip to content
Significance Weighted Principal Component Analysis
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
README.md
swpca.py

README.md

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.

Use

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. http://dx.doi.org/10.1002/hbm.23449
You can’t perform that action at this time.