JackStraw: Determine statistical significance of PCA scores.

In Seurat: Tools for Single Cell Genomics

Determine statistical significance of PCA scores.

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,
  reduction = "pca",
  assay = NULL,
  dims = 20,
  num.replicate = 100,
  prop.freq = 0.01,
  verbose = TRUE,
  maxit = 1000
)

Arguments

object	Seurat object
reduction	DimReduc to use. ONLY PCA CURRENTLY SUPPORTED.
assay	Assay used to calculate reduction.
dims	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
verbose	Print progress bar showing the number of replicates that have been processed.
maxit	maximum number of iterations to be performed by the irlba function of RunPCA

Value

Returns a Seurat object where JS(object = object[['pca']], slot = 'empirical') represents p-values for each gene in the PCA analysis. If ProjectPCA is subsequently run, JS(object = object[['pca']], slot = 'full') then represents p-values for all genes.

References

Inspired by Chung et al, Bioinformatics (2014)

Examples

## Not run: 
data("pbmc_small")
pbmc_small = suppressWarnings(JackStraw(pbmc_small))
head(JS(object = pbmc_small[['pca']], slot = 'empirical'))

## End(Not run)

Seurat documentation built on May 29, 2024, 4:20 a.m.