# Calculate epistasis using HSIC with two genotype inputs 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. ```r epiHSIC2genos(geno1 = NULL, geno2 = NULL, pheno = NULL, chunk = 1000, zpthres = 1e-05, outfile = "NONE", suffix = ".txt", ...) ``` ## Arguments - `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`: is a vector containing the normalized phenotype information. - `chunk`: is the number of variants in each chunk. - `zpthres`: is the significance threshold for cut-off output of the variant pairs. - `outfile`: is the basename of out filename. - `suffix`: is the suffix of out filename. - `...`: not used ## Returns null ## Examples ```r # simulate some data set.seed(123) n1 <- 10; n2 <- 15; rows <- 10 geno1 <- matrix(sample(0:2, size = n1*rows, replace = TRUE, prob = c(0.5, 0.3, 0.2)), ncol = n1) geno2 <- matrix(sample(0:2, size = n2*rows, replace = TRUE, prob = c(0.4, 0.3, 0.3)), ncol = n2) 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))) p2 <- rnorm(rows, mean = 5, sd = 10) # normalized data geno1 <- scale(geno1) geno2 <- scale(geno2) p2 <- as.vector(unlist(scale(p2))) # two genotypes with quantitative phenotype epiHSIC2genos(geno1 = geno1, geno2 = geno2, pheno = p2, outfile = "episcan_2geno_quant", suffix = ".txt", zpthres = 0.9, chunk = 10) # take a look at the result res <- read.table("episcan_2geno_quant.txt", header = TRUE, stringsAsFactors = FALSE) head(res) ```

epiHSIC2genos function