applyPermutationWPCA: Applies pemutation method to return the most significant...

View source: R/Projections.R

applyPermutationWPCAR Documentation

Applies pemutation method to return the most significant components of PCA data

Description

Applies pemutation method to return the most significant components of PCA data

Usage

applyPermutationWPCA(expr, components = 50, p_threshold = 0.05)

Arguments

expr

Expression data

components

Maximum components to calculate. Default is 50.

p_threshold

P Value to cutoff components at. Default is .05.

Details

Based on the method proposed by Buja and Eyuboglu (1992), PCA is performed on the data then a permutation procedure is used to assess the significance of components

Value

(list):

  • wPCA: weighted PCA data

  • eval: the proortinal variance of each component

  • evec: the eigenvectors of the covariance matrix

  • permuteMatrices: the permuted matrices generated as the null distrbution


YosefLab/VISION documentation built on June 14, 2024, 5:27 p.m.