tests/testthat/test-norm.R

In metagenomeSeq: Statistical analysis for sparse high-throughput sequencing

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# metagenomeSeq plot functions unit tests
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context("Testing norm factor calculation")
library("metagenomeSeq"); library("testthat")


test_that("`calcNormFactors` function provides expected values", {
  # uses the lung data and pre-calculated normalization factors 
  # for various values of p
  data(lungData)
  point25 = c(29,2475,2198,836,722,1820,79,1171,1985,710,145,742,848,89,1981)
  point = c(43,2475,2198,836,722,1820,119,1171,1985,710,145,742,848,89,1981)
  point100=as.numeric(unlist(libSize(lungData[,1:15])))
  expect_equal(as.numeric(unlist(calcNormFactors(lungData[,1:15]))),point)
  expect_equal(as.numeric(unlist(calcNormFactors(lungData[,1:15],p=.25))),point25)
  expect_equal(as.numeric(unlist(calcNormFactors(lungData[,1:15],p=1))),point100)
})

test_that("`cumNorm` returns the same object as defined in the package", {
  data(lungData); data(mouseData)
  expect_equal(cumNorm(mouseData,p=.5), mouseData)
  expect_equal(cumNorm(lungData), lungData)
})

test_that("`cumNormStat` returns the correct value", {
  data(lungData); data(mouseData);
  expect_equal(cumNormStat(lungData),0.7014946)
  expect_equal(cumNormStat(mouseData),0.5)
})

test_that("`cumNormStatFast` returns the correct value", {
  data(lungData); data(mouseData);
  expect_equal(cumNormStatFast(lungData),0.7014946)
  expect_equal(cumNormStatFast(mouseData),0.5)
})