gsnca_p: Gene Sets Net Correlation Analysis (GSNCA) including...

In hui-sheen/MetaGSCA: Meta-analysis of Gene Set differential Co-expression Analysis

Gene Sets Net Correlation Analysis (GSNCA) including permutation

Description

Gene Sets Net Correlation Analysis (GSNCA) including permutation

Usage

gsnca_p(
  object,
  group,
  perm.list,
  cor.method = "pearson",
  max.skip = 50,
  min.sd = 0.001
)

Arguments

object	Gene expression matrix. Genes in rows.
group	original groupping of samples, vector of 1's and 2's.
perm.list	list of permutation specs. Each component gives permutated sample indices
cor.method	correlation method
max.skip	maximum number of repeated permutation/bootstrap times to avoid zero STD
min.sd	a valid data matrix per group must have at least this much per-feature STD

Value

list of p-value and d-statistics of GSNCA

See Also

[gsnca_stat()] for the internal algorithm that returns the coexpression distance statistics.

Examples

data(meta)
BRCA <- datasets[['BRCA']]
smpCode <- substr(colnames(BRCA),14,15)
grp1 <- which(smpCode=='01')
grp2 <- which(smpCode=='11')
object <- BRCA[1:25,c(grp1,grp2)]
group <- c(rep(1,length(grp1)),rep(2,length(grp1)))
perm.list <- vector('list',500)
for (i in seq_len(500)) {perm.list[[i]] <- sample(ncol(object))}
gsnca.p.res <- gsnca_p(object,group,perm.list)

hui-sheen/MetaGSCA documentation built on April 9, 2022, 7:24 p.m.