JackStraw: Determine statistical significance of PCA scores.

In nukappa/seurat_v2: Seurat : R toolkit for single cell genomics

Description

Randomly permutes a subset of data, and calculates projected PCA scores for these 'random' genes. Then compares the PCA scores for the 'random' genes with the observed PCA scores to determine statistical signifance. End result is a p-value for each gene's association with each principal component.

Usage

JackStraw(object, num.pc = 30, num.replicate = 100, prop.freq = 0.01,
  do.print = FALSE, rev.pca = FALSE, do.fast = FALSE)

Arguments

object	Seurat object
num.pc	Number of PCs to compute significance for
num.replicate	Number of replicate samplings to perform
prop.freq	Proportion of the data to randomly permute for each replicate
do.print	Print the number of replicates that have been processed.
rev.pca	By default computes the PCA on the cell x gene matrix. Setting to true will compute it on gene x cell matrix. This should match what was set when the intial PCA was run.
do.fast	Compute the PCA using the fast approximate calculation from the IRLBA package. Values stored with object must also have been computed using the PCAFast() function.

Value

Returns a Seurat object where [e-mail] represents p-values for each gene in the PCA analysis. If ProjectPCA is subsequently run, [e-mail] then represents p-values for all genes.

References

Inspired by Chung et al, Bioinformatics (2014)

nukappa/seurat_v2 documentation built on May 24, 2019, 9:57 a.m.