tests/testthat.R

In HaidYi/DASC: Detecting hidden batch factors through data adaptive adjustment for biological effects

library(testthat)
library(DASC)
library(NMF)
library(Biobase)
library(cvxclustr)

context("DASC functionality")

test_that("adj2vector", {
    W <- matrix(c(0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 4)
    w <- adj2vector(W, 4)
})

# test_that("DASC", {
#     dat <- data.frame(matrix(rnbinom(n = 200, mu = 100, size = 1 / 0.5),
#                                 ncol = 4))
#     pdat <- data.frame(sample = colnames(dat), type = c(rep("A", 2),
#                                                             rep("B", 2)))
#     rownames(pdat) <- colnames(dat)
#     if (.Platform$OS.type == "windows") {
#         res <- DASC(edata = dat, pdata = pdat, factor = pdat$type,
#                      method = "ama", type = 3, lambda = 1, rank = 2,
#                      nrun = 1, annotation = "simulated dataset")
#     }else{
#         res <- DASC(edata = dat, pdata = pdat, factor = pdat$type,
#                      method = "ama", type = 3, lambda = 1, rank = 2,
#                      nrun = 50, annotation = "simulated dataset")
#     }
# })

test_that("trans_Laplace", {
    factors <- data.frame(Sample = c("Sample1", "Sample2", "Sample3",
                                      "Sample4"), type = c(rep("A", 2),
                                                            rep("B", 2)))
    trans_Laplace(as.factor(factors$type))
})

test_that("Sptree", {
    W <- matrix(c(0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0), nrow = 4)
    Sptree(W)
})