epiblaster2genos {episcan} | R Documentation |
Calculate the difference of correlation coeficents between cases and controls, conduct Z test for the differences (values) and choose variant pairs with the significance below the given threshold for output.
epiblaster2genos(geno1, geno2, pheno, chunk = 1000, zpthres = 1e-05, outfile = "NONE", suffix = ".txt", ...)
geno1 |
is the first 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. |
geno2 |
is the second 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. |
zpthres |
is the significance threshold to select variant pairs for output. Default is 1e-6. |
outfile |
is the prefix of out filename. |
suffix |
is the suffix of out filename. |
... |
not used. |
null
# 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) geno2 <- matrix(sample(0:2, size = 2000, replace = TRUE, prob = c(0.4, 0.3, 0.3)), ncol = 20) dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), col = paste0("rs", 1:ncol(geno1))) dimnames(geno2) <- list(row = paste0("IND", 1:nrow(geno2)), col = paste0("exm", 1:ncol(geno2))) p1 <- c(rep(0, 60), rep(1, 40)) # normalized data geno1 <- scale(geno1) geno2 <- scale(geno2) # two genotypes with quantitative phenotype epiblaster2genos(geno1 = geno1, geno2 = geno2, pheno = p1, outfile = "episcan_2geno_cc", suffix = ".txt", zpthres = 0.9, chunk = 10) # take a look at the result res <- read.table("episcan_2geno_cc.txt", header = TRUE, stringsAsFactors = FALSE) head(res)