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

In YosefLab/VISION: Functional interpretation of single cell RNA-seq latent manifolds

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.