episcan (version 0.0.1)

epiblaster1geno: Parallelized calculation of the difference of correlation coefficients and compute \(Z\) test with one genotype input

Description

Calculate the difference of correlation coefficents between cases and controls, conduct \(Z\) test for the differences (values) and choose variant pairs with the significance below the given threshold for output.

Usage

epiblaster1geno(geno, pheno, chunk = 1000, zpthres = 1e-05,
  outfile = "NONE", suffix = ".txt", ...)

Arguments

geno

is the normalized genotype data. It can be a matrix or a dataframe, or a big.matrix object (from bigmemory. The columns contain the information of variables and the rows contain the information of samples.

pheno

a vector containing the binary phenotype information (case/control). The values are either 0 (control) or 1 (case).

chunk

is the number of variants in each chunk. Default: 1000.

zpthres

is the significance threshold to select variant pairs for output. Default is 1e-6.

outfile

is the base of out filename. Default: 'NONE'.

suffix

is the suffix of out filename. Default: '.txt'.

...

not used.

Value

null

Examples

# NOT RUN {
# simulate some data
set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), ncol = 10)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), col = paste0("rs", 1:ncol(geno1)))
p1 <- c(rep(0, 60), rep(1, 40))

# normalized data
geno1 <- scale(geno1)

# one genotype with case-control phenotype
epiblaster1geno(geno = geno1, 
pheno = p1,
outfile = "episcan_1geno_cc", 
suffix = ".txt", 
zpthres = 0.9, 
chunk = 10)

# take a look at the result
res <- read.table("episcan_1geno_cc.txt", 
header = TRUE, 
stringsAsFactors = FALSE)
head(res)
# }