epiHSIC1geno {episcan} | R Documentation |
Calculate the significance of epistasis according the definition of HSIC, conduct Z test for HSIC values and choose variant pairs with the significance below the given threshold for output.
epiHSIC1geno(geno = NULL, pheno, chunk = 1000, zpthres = 1e-05, outfile = "NONE", suffix = ".txt", ...)
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 |
is a vector containing the normalized phenotype information. |
chunk |
is the number of variants in each chunk. |
zpthres |
is is the significance threshold to select variant pairs for output. Default is 1e-6. |
outfile |
is the basename of out filename. |
suffix |
is the suffix of out filename. |
... |
not used. |
null
Beibei Jiang beibei_jiang@psych.mpg.de
# 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))) p2 <- rnorm(100, mean = 5, sd = 10) # normalized data geno1 <- scale(geno1) p2 <- as.vector(unlist(scale(p2))) # one genotypes with quantitative phenotype epiHSIC1geno(geno = geno1, pheno = p2, outfile = "episcan_1geno_quant", suffix = ".txt", zpthres = 0.9, chunk = 10) # take a look at the result res <- read.table("episcan_1geno_quant.txt", header = TRUE, stringsAsFactors = FALSE) head(res)