eclust (version 0.1.0)

s_modules: Simulate Covariates With Exposure Dependent Correlations

Description

This is a wrapper of the simulateDatExpr function which simulates data in a modular structure (i.e. in blocks). This function simulates data in 5 blocks referred to as Turquoise, Blue, Red, Green and Yellow, separately for exposed (E=1) and unexposed (E=0) observations.

Usage

s_modules(n, p, rho, exposed, ...)

Arguments

n

number of observations

p

total number of predictors to simulate

rho

numeric value representing the expected correlation between green module and red module

exposed

binary numeric vector of length n with 0 for unexposed and 1 for exposed

...

arguments passed to the simulateDatExpr function

Value

n x p matrix of simulated data

Examples

library(magrittr)
p <- 1000
n <- 200
d0 <- s_modules(n = 100, p = 1000, rho = 0, exposed = FALSE,
                modProportions = c(0.15,0.15,0.15,0.15,0.15,0.25),
                minCor = 0.01,
                maxCor = 1,
                corPower = 1,
                propNegativeCor = 0.3,
                backgroundNoise = 0.5,
                signed = FALSE,
                leaveOut = 1:4)

d1 <- s_modules(n = 100, p = 1000, rho = 0.90, exposed = TRUE,
                modProportions = c(0.15,0.15,0.15,0.15,0.15,0.25),
                minCor = 0.4,
                maxCor = 1,
                corPower = 0.3,
                propNegativeCor = 0.3,
                backgroundNoise = 0.5,
                signed = FALSE)

X <- rbind(d0$datExpr, d1$datExpr) %>%
 magrittr::set_colnames(paste0("Gene", 1:p)) %>%
 magrittr::set_rownames(paste0("Subject",1:n))
dim(X)