tests/testthat/test_post_alignment.R

In DIAlignR: Dynamic Programming Based Alignment of MS2 Chromatograms

context("Post alignment analysis")

# mzML and osw files are not required.

test_that("test_pickNearestFeature", {
  data(oswFiles_DIAlignR, package="DIAlignR")
  oswFiles <- oswFiles_DIAlignR
  outData <- pickNearestFeature(eXpRT = 5237.8, analyte = 4618L, oswFiles,
                                runname = "run2", adaptiveRT = 77.82315,
                                featureFDR = 0.05)
  expData <- list("leftWidth" = c(5217.361),
                           "rightWidth" = c(5275.395),
                           "RT" = c(5240.79),
                           "intensity" = c(255.496),
                           "peak_group_rank" = c(1L),
                           "m_score" = c(5.692077e-05))
  expect_equal(outData, expData, tolerance = 1e-05)
})

test_that("test_mapIdxToTime", {
  timeVec <- c(1.3,5.6,7.8)
  idx <- c(NA, NA, 1L, 2L, NA, NA, 3L, NA)
  outData <- mapIdxToTime(timeVec, idx)
  expData <- c(NA, NA, 1.3, 5.6, 6.333333, 7.066667, 7.8, NA)
  expect_equal(outData, expData, tolerance = 1e-04)
})

test_that("test_mappedRTfromAlignObj", {
  AlignObj <- testAlignObj()
  data(XIC_QFNNTDIVLLEDFQK_3_DIAlignR, package="DIAlignR")
  XICs <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR
  tVec.ref <-  XICs[["run1"]][["14299_QFNNTDIVLLEDFQK/3"]][[1]][, "time"]
  tVec.eXp <-  XICs[["run2"]][["14299_QFNNTDIVLLEDFQK/3"]][[1]][, "time"]
  expect_equal(mappedRTfromAlignObj(refRT= 5238.35, tVec.ref, tVec.eXp, AlignObj), 5241.3)
})

test_that("test_setAlignmentRank", {
})


test_that("test_calculateIntensity", {

  time <- c( 2.23095,2.239716667,2.248866667,2.25765,2.266416667,
    2.275566667,2.2847,2.293833333,2.304066667,2.315033333,2.325983333,2.336566667,
    2.3468,2.357016667,2.367283333,2.377183333,2.387083333,2.39735,2.40725,2.4175,
    2.4274,2.4373,2.44755,2.45745,2.4677,2.477966667,2.488216667,2.498516667,2.5084,
    2.5183,2.5282,2.538466667,2.548366667,2.558266667,2.568516667,2.578783333,
    2.588683333,2.59895,2.6092,2.619466667,2.630066667,2.64065,2.65125,2.662116667,
    2.672716667,2.6833,2.6939,2.7045,2.715083333,2.725683333,2.736266667,2.746866667,
    2.757833333,2.768416667,2.779016667,2.789616667,2.8002,2.810116667,2.820033333,
    2.830316667,2.840216667,2.849766667,2.859316667,2.868866667,2.878783333,2.888683333,
    2.898233333,2.907783333,2.916033333,2.924266667,2.93215,2.940383333,2.947933333,
    2.955816667,2.964066667,2.97195,2.979833333,2.987716667,2.995616667,3.003516667,
    3.011416667,3.01895,3.026833333,3.034366667,3.042266667,3.0498,3.05735,3.065233333,
    3.073133333,3.080666667,3.0882,3.095733333,3.103633333,3.111533333,3.119066667,
    3.126966667,3.134866667,3.14275,3.15065,3.15855,3.166433333,3.174333333,3.182233333,
    3.190133333,3.198016667,3.205916667,3.213166667
  )

  intensity <- c(
    1447,2139,1699,755,1258,1070,944,1258,1573,1636,
    1762,1447,1133,1321,1762,1133,1447,2391,692,1636,2957,1321,1573,1196,1258,881,
    1384,2076,1133,1699,1384,692,1636,1133,1573,1825,1510,2391,4342,10382,17618,
    51093,153970,368094,632114,869730,962547,966489,845055,558746,417676,270942,
    184865,101619,59776,44863,31587,24036,20450,20324,11074,9879,10508,7928,7110,
    6733,6481,5726,6921,6670,5537,4971,4719,4782,5097,5789,4279,5411,4530,3524,
    2139,3335,3083,4342,4279,3083,3649,4216,4216,3964,2957,2202,2391,2643,3524,
    2328,2202,3649,2706,3020,3335,2580,2328,2894,3146,2769,2517
  )
  df <- data.frame(time, intensity)
  left <- 2.472833334
  right <- 3.022891666
  outData <- calculateIntensity(list(df), left, right, integrationType = "intensity_sum",
                     baselineType = "base_to_base", fitEMG = FALSE)
  expect_equal(outData, 6645331.33866)

  outData <- calculateIntensity(list(df, df), left, right, integrationType = "trapezoid",
                                baselineType = "vertical_division_min", fitEMG = FALSE)
  expect_equal(outData, 2*71063.59368, tolerance = 0.01)
})