tests/testthat/test_optim.R

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

context("optim")
test_that("optim gives same results", {
  set.seed(1)
  dds <- makeExampleDESeqDataSet(n=100,interceptMean=10,interceptSD=3)
  dds <- estimateSizeFactors(dds)
  dds <- estimateDispersions(dds)
  # make a large predictor to test scaling
  colData(dds)$condition <- rnorm(ncol(dds),0,1000)
  modelMatrix <- model.matrix(~ condition, as.data.frame(colData(dds)))
  fit <- DESeq2:::fitNbinomGLMs(dds, modelMatrix=modelMatrix, 
                                modelFormula = ~ condition,
                                alpha_hat = dispersions(dds),
                                lambda = c(2,2),
                                renameCols=TRUE, betaTol=1e-8,
                                maxit=100, useOptim=TRUE,
                                useQR=TRUE, forceOptim=FALSE)
  fitOptim <- DESeq2:::fitNbinomGLMs(dds, modelMatrix=modelMatrix, 
                                     modelFormula = ~ condition,
                                     alpha_hat = dispersions(dds),
                                     lambda = c(2,2),
                                     renameCols=TRUE, betaTol=1e-8,
                                     maxit=100, useOptim=TRUE,
                                     useQR=TRUE, forceOptim=TRUE)
  #plot(fit$betaMatrix[,2], fitOptim$betaMatrix[,2])
  #abline(0,1,col="red")
  expect_equal(fit$betaMatrix, fitOptim$betaMatrix,tolerance=1e-6)
  expect_equal(fit$betaSE, fitOptim$betaSE,tolerance=1e-6)

  # test optim gives same lfcSE
  set.seed(1)
  dds <- makeExampleDESeqDataSet(n=100, m=10)
  counts(dds)[1,] <- c(rep(0L,5),c(1000L,1000L,0L,0L,0L))
  dds <- DESeq(dds, betaPrior=FALSE)
  # beta iter = 100 implies optim used for fitting
  expect_equal(mcols(dds)$betaIter[1], 100)
  res1 <- results(dds, contrast=c("condition","B","A"))
  res2 <- results(dds, contrast=c(0,1))
  expect_true(all.equal(res1$lfcSE, res2$lfcSE))
  expect_true(all.equal(res1$pvalue, res2$pvalue))
})