tests/testthat/test_model_matrix.R

In mikelove/DESeq2: Differential gene expression analysis based on the negative binomial distribution

context("model_matrix")
test_that("supplying custom model matrix works", {
  dds <- makeExampleDESeqDataSet(n=100, m=18)
  dds$group <- factor(rep(1:3,each=6))
  dds$condition <- factor(rep(rep(c("A","B","C"),each=2),3))
  # note: design is not used
  design(dds) <- ~ 1
  dds <- dds[,-c(17,18)]

  m1 <- model.matrix(~ group*condition, colData(dds))
  m1 <- m1[,-9]
  m0 <- model.matrix(~ group + condition, colData(dds))

  dds <- DESeq(dds, full=m1, reduced=m0, test="LRT")
  results(dds)[1,]
  results(dds, name="group2.conditionC", test="Wald")[1,]
  dds <- removeResults(dds)
  dds <- DESeq(dds, full=m1, test="Wald", betaPrior=FALSE)
  results(dds)[1,]

  # test better error than "error: inv(): matrix seems singular"
  coldata <- data.frame(group=factor(rep(1:3,each=6)),
                        group2=factor(rep(1:3,each=6)),
                        condition=factor(rep(1:6,3)))
  counts <- matrix(rpois(180, 100), ncol=18)
  m1 <- model.matrix(~ group + group2, coldata)
  m2 <- model.matrix(~ condition + group, coldata)
  dds <- DESeqDataSetFromMatrix(counts, coldata, ~group)
  expect_error(dds <- DESeq(dds, full=m1, fitType="mean"), "full rank")
  expect_error(dds <- DESeq(dds, full=m2, reduced=m1, test="LRT", fitType="mean"), "full rank")
})