JackStraw: 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, num.pc = 20, num.replicate = 100, prop.freq = 0.01,
do.print = FALSE)
Arguments
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.
Value
Returns a Seurat object where object@dr$pca@jackstraw@emperical.p.value represents
p-values for each gene in the PCA analysis. If ProjectPCA is subsequently
run, object@dr$pca@jackstraw@emperical.p.value.full then represents p-values for all genes.
References
Inspired by Chung et al, Bioinformatics (2014)
Examples
Run this code# NOT RUN {
pbmc_small = suppressWarnings(JackStraw(pbmc_small))
head(pbmc_small@dr$pca@jackstraw@emperical.p.value)
# }
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