Nothing
tests/testthat/test_peakPantheR_applyRTCorrection.R
In peakPantheR: Peak Picking and Annotation of High Resolution Experiments
context('peakPantheR_applyRTCorrection()')
## Input and expected data
test_that('raises error()', {
# Input
tmp_targetFeatTable <- data.frame(matrix(vector(), 2, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_targetFeatTable[1,] <- c("ID-1", "Cpd 1", 11, 1.1)
tmp_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 25, 3.2)
tmp_targetFeatTable[, c(3:4)] <- sapply(tmp_targetFeatTable[, c(3:4)], as.numeric)
tmp_referenceTable <- data.frame(matrix(vector(), 2, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_referenceTable[1,] <- c("Ref-1", "Ref Cpd 1", 10, 1)
tmp_referenceTable[2,] <- c("Ref-2", "Ref Cpd 2", 20, 1.5)
tmp_referenceTable[, c(3:4)] <- sapply(tmp_referenceTable[, c(3:4)], as.numeric)
# Trigger checks on targetFeatTable input
# targetFeatTable is not a data.frame
expect_error(peakPantheR_applyRTCorrection(targetFeatTable='notADataFrame', referenceTable=tmp_referenceTable), 'specified targetFeatTable is not a data.frame')
# targetFeatTable has wrong columns
wrongDF1 <- tmp_targetFeatTable
colnames(wrongDF1) <- c("not cpdID", "cpdName", "rt", "rt_dev_sec")
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=wrongDF1, referenceTable=tmp_referenceTable), "expected columns in targetFeatTable are \"cpdID\", \"cpdName\", \"rt\", \"rt_dev_sec\"")
# targetFeatTable$cpdID not character
wrongDF2 <- tmp_targetFeatTable
wrongDF2$cpdID <- c(5, 5)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=wrongDF2, referenceTable=tmp_referenceTable), 'cpdID must be character')
# targetFeatTable$cpdName not character
wrongDF3 <- tmp_targetFeatTable
wrongDF3$cpdName <- c(5, 5)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=wrongDF3, referenceTable=tmp_referenceTable), 'cpdName must be character')
# targetFeatTable$rt not numeric
wrongDF4 <- tmp_targetFeatTable
wrongDF4$rt <- c('notNumeric', 'notNumeric')
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=wrongDF4, referenceTable=tmp_referenceTable), 'rt must be numeric')
# targetFeatTable$rt_dev_sec not numeric or NA
wrongDF5 <- tmp_targetFeatTable
wrongDF5$rt_dev_sec <- c('notNumeric', 'notNumeric')
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=wrongDF5, referenceTable=tmp_referenceTable), 'rt_dev_sec must be numeric or NA')
# wrong referenceTable
# referenceTable is not a data.frame
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable='notADataFrame'), 'specified referenceTable is not a data.frame')
# referenceTable has wrong columns
wrongDF6 <- tmp_referenceTable
colnames(wrongDF6) <- c("not cpdID", "cpdName", "rt", "rt_dev_sec")
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=wrongDF6), 'expected columns in referenceTable are "cpdID", "cpdName", "rt" and "rt_dev_sec"')
# referenceTable$cpdID not character
wrongDF11 <- tmp_referenceTable
wrongDF11$cpdID <- c(5, 5)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=wrongDF11), 'cpdID must be character')
# referenceTable$cpdName not character
wrongDF12 <- tmp_referenceTable
wrongDF12$cpdName <- c(5, 5)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=wrongDF12), 'cpdName must be character')
# referenceTable$rt not numeric or NA
wrongDF13 <- tmp_referenceTable
wrongDF13$rt <- c('notNumeric', 'notNumeric')
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=wrongDF13), 'rt must be numeric or NA')
# referenceTable$rt_dev_sec not numeric or NA
wrongDF14 <- tmp_referenceTable
wrongDF14$rt_dev_sec <- c('notNumeric', 'notNumeric')
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=wrongDF14), 'rt_dev_sec must be numeric or NA')
# unknown correction method
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='notKnown'), 'Error: \"method\" must be one of: \"polynomial\", \"constant\"', fixed=TRUE)
# params is not a list or character
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, params=5), 'Check input, "params" must be list', fixed=TRUE)
# Robust is not logical
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, robust=3, params=list(polynomialOrder=1)), 'robust must be either TRUE or FALSE', fixed=TRUE)
# Method <-> params SPECIFIC CHECKS
# method = polynomial but no polynomial order
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(a=5), robust=TRUE), 'polynomialOrder must be provided in params', fixed=TRUE)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(a=5), robust=FALSE), 'polynomialOrder must be provided in params', fixed=TRUE)
# method = polynomial but polynomial order is not valid
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder='NA'), robust=FALSE), 'polynomialOrder must be an integer and equal or greater than 1', fixed=TRUE)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=1.3), robust=FALSE), 'polynomialOrder must be an integer and equal or greater than 1', fixed=TRUE)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=1.3), robust=TRUE), 'polynomialOrder must be an integer and equal or greater than 1', fixed=TRUE)
# method = polynomial, valid polynomial order but exceeds number of references
expect_warning(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=4), robust=FALSE), '`polynomialOrder` is larger than the number of references passed. `polynomialOrder` will be set equal to number of reference compounds - 1', fixed=TRUE)
expect_warning(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=4), robust=TRUE), '`polynomialOrder` is larger than the number of references passed. `polynomialOrder` will be set equal to number of reference compounds - 1', fixed=TRUE)
# test sugestion of method=constant if ref = 1
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable[1, ], method='polynomial', params=list(polynomialOrder=1), robust=FALSE), 'No function can be fitted with a single reference. Use method=\`constant\` instead.', fixed=TRUE)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable[1, ], method='polynomial', params=list(polynomialOrder=1), robust=TRUE), 'No function can be fitted with a single reference. Use method=\`constant\` instead.', fixed=TRUE)
# test constant method throws error with multiple references
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='constant', params=list(polynomialOrder=1), robust=TRUE), "`constant` Rt correction can only use a single reference", fixed=TRUE)
})
test_that('polynomial method rt correction', {
# Input
tmp_targetFeatTable <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_targetFeatTable[1,] <- c("ID-1", "Cpd 1", 11, 1.1)
tmp_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 25, 1.5)
tmp_targetFeatTable[3,] <- c("ID-3", "Cpd 3", 35, 3.5)
tmp_targetFeatTable[4,] <- c("ID-4", "Cpd 4", 45, 5.5)
tmp_targetFeatTable[5,] <- c("ID-5", "Cpd 5", 55, 7)
tmp_targetFeatTable[, c(3:4)] <- sapply(tmp_targetFeatTable[, c(3:4)], as.numeric)
tmp_referenceTable <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_referenceTable[1,] <- c("Ref-1", "Ref Cpd 1", 11, 1.1)
tmp_referenceTable[2,] <- c("Ref-2", "Ref Cpd 2", 25, 1.5)
tmp_referenceTable[3,] <- c("Ref-3", "Ref Cpd 3", 35, 3.5)
tmp_referenceTable[4,] <- c("Ref-4", "Ref Cpd 4", 45, 5.5)
tmp_referenceTable[5,] <- c("Ref-5", "Ref Cpd 5", 55, 7)
tmp_referenceTable[, c(3:4)] <- sapply(tmp_referenceTable[, c(3:4)], as.numeric)
# Expected model and results
expected_corrected <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=10.61282, predictedRtDrift=0.387176)
expected_corrected[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=1.5, isReference="External set", correctedRt=22.60164, predictedRtDrift=2.398363)
expected_corrected[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt= 31.16508, predictedRtDrift=3.834925)
expected_corrected[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.72851, predictedRtDrift=5.271487)
expected_corrected[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.29195, predictedRtDrift=6.708049)
expected_corrected_dg3 <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected_dg3[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=9.907072, predictedRtDrift=1.092928)
expected_corrected_dg3[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=1.5, isReference="External set", correctedRt=23.457678, predictedRtDrift=1.542322)
expected_corrected_dg3[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt= 31.574063, predictedRtDrift=3.425937)
expected_corrected_dg3[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.447720, predictedRtDrift=5.552280)
expected_corrected_dg3[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.013466, predictedRtDrift=6.986534)
# results (output, warnings and messages)
polynomial_regression_dg1 <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=1), robust=FALSE))
polynomial_regression_dg3 <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable,
method='polynomial', params=list(polynomialOrder=3), robust=FALSE))
# Check results
expect_equal(polynomial_regression_dg1$result, expected_corrected, tolerance=1e-5)
expect_equal(polynomial_regression_dg3$result, expected_corrected_dg3, tolerance=1e-5)
# test isReference is assigned correctely
tmp_referenceTable <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_referenceTable[1,] <- c("Ref-1", "Ref Cpd 1", 11, 1.1)
tmp_referenceTable[2,] <- c("ID-2", "Cpd 2", 25, 1.5)
tmp_referenceTable[3,] <- c("Ref-3", "Ref Cpd 3", 35, 3.5)
tmp_referenceTable[4,] <- c("Ref-4", "Ref Cpd 4", 45, 5.5)
tmp_referenceTable[5,] <- c("Ref-5", "Ref Cpd 5", 55, 7)
tmp_referenceTable[, c(3:4)] <- sapply(tmp_referenceTable[, c(3:4)], as.numeric)
expected_corrected_isRef <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected_isRef[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=10.61282, predictedRtDrift=0.387176)
expected_corrected_isRef[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=1.5, isReference="Reference set", correctedRt=22.60164, predictedRtDrift=2.398363)
expected_corrected_isRef[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt= 31.16508, predictedRtDrift=3.834925)
expected_corrected_isRef[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.72851, predictedRtDrift=5.271487)
expected_corrected_isRef[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.29195, predictedRtDrift=6.708049)
polynomial_regression_ref <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=1), robust=FALSE))
expect_equal(polynomial_regression_ref$result, expected_corrected_isRef, tolerance=1e-5)
# Check messages (cannot check time on message)
#expect_equal(length(result_integrateFIR$messages), 5)
#expect_equal(result_integrateFIR$messages[c(1,2,4)], expected_messages)
})
test_that('RANSAC rt correction function', {
# Input
tmp_targetFeatTable <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_targetFeatTable[1,] <- c("ID-1", "Cpd 1", 11, 1.1)
tmp_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 25, 1.5)
tmp_targetFeatTable[3,] <- c("ID-3", "Cpd 3", 35, 3.5)
tmp_targetFeatTable[4,] <- c("ID-4", "Cpd 4", 45, 5.5)
tmp_targetFeatTable[5,] <- c("ID-5", "Cpd 5", 55, 7)
tmp_targetFeatTable[, c(3:4)] <- sapply(tmp_targetFeatTable[, c(3:4)], as.numeric)
tmp_referenceTable <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_referenceTable[1,] <- c("Ref-1", "Ref Cpd 1", 11, 1.1)
tmp_referenceTable[2,] <- c("Ref-2", "Ref Cpd 2", 25, 1.5)
tmp_referenceTable[3,] <- c("Ref-3", "Ref Cpd 3", 35, 3.5)
tmp_referenceTable[4,] <- c("Ref-4", "Ref Cpd 4", 45, 5.5)
tmp_referenceTable[5,] <- c("Ref-5", "Ref Cpd 5", 55, 7)
tmp_referenceTable[, c(3:4)] <- sapply(tmp_referenceTable[, c(3:4)], as.numeric)
# Expected model and results
expected_corrected <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=10.61282, predictedRtDrift=0.387176)
expected_corrected[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=1.5, isReference="External set", correctedRt=22.60164, predictedRtDrift=2.398363)
expected_corrected[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt= 31.16508, predictedRtDrift=3.834925)
expected_corrected[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.72851, predictedRtDrift=5.271487)
expected_corrected[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.29195, predictedRtDrift=6.708049)
expected_corrected_dg3 <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected_dg3[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=9.907072, predictedRtDrift=1.092928)
expected_corrected_dg3[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=1.5, isReference="External set", correctedRt=23.457678, predictedRtDrift=1.542322)
expected_corrected_dg3[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt= 31.574063, predictedRtDrift=3.425937)
expected_corrected_dg3[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.447720, predictedRtDrift=5.552280)
expected_corrected_dg3[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.013466, predictedRtDrift=6.986534)
# results (output, warnings and messages)
set.seed(19472)
polynomial_ransac_dg1 <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=1), robust=TRUE))
set.seed(390753)
polynomial_ransac_dg3 <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=3), robust=TRUE))
# Check results
expect_equal(polynomial_ransac_dg1$result, expected_corrected, tolerance=1e-5)
expect_equal(polynomial_ransac_dg3$result, expected_corrected_dg3, tolerance=1e-5)
# Add outlier
tmp_referenceTable_outlier <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_referenceTable_outlier[1,] <- c("Ref-1", "Ref Cpd 1", 11, 1.1)
tmp_referenceTable_outlier[2,] <- c("ID-2", "Cpd 2", 25, 15)
tmp_referenceTable_outlier[3,] <- c("Ref-3", "Ref Cpd 3", 35, 3.5)
tmp_referenceTable_outlier[4,] <- c("Ref-4", "Ref Cpd 4", 45, 5.5)
tmp_referenceTable_outlier[5,] <- c("Ref-5", "Ref Cpd 5", 55, 7)
tmp_referenceTable_outlier[, c(3:4)] <- sapply(tmp_referenceTable_outlier[, c(3:4)], as.numeric)
# Modify outlier in targetFeatTable
tmp_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 25, 15)
tmp_targetFeatTable[, c(3:4)] <- sapply(tmp_targetFeatTable[, c(3:4)], as.numeric)
expected_corrected_outlier <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected_outlier[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=10.14133, predictedRtDrift=0.8586692)
expected_corrected_outlier[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=15.0, isReference="Reference set outlier", correctedRt=22.26570, predictedRtDrift=2.7343018)
expected_corrected_outlier[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt=30.92596, predictedRtDrift=4.0740394)
expected_corrected_outlier[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.58622, predictedRtDrift=5.4137769)
expected_corrected_outlier[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.24649, predictedRtDrift=6.7535145)
expected_corrected_outlier_dg3 <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected_outlier_dg3[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt= 9.90000, predictedRtDrift=1.100000)
expected_corrected_outlier_dg3[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=15.0, isReference="Reference set outlier", correctedRt=23.25802, predictedRtDrift=1.741979)
expected_corrected_outlier_dg3[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt=31.50000, predictedRtDrift=3.500000)
expected_corrected_outlier_dg3[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.50000, predictedRtDrift=5.500000)
expected_corrected_outlier_dg3[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.00000, predictedRtDrift=7.000000)
# Case where outlier is not detected
expected_corrected_outlier_nd <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected_outlier_nd[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt= 9.90000, predictedRtDrift=1.100000)
expected_corrected_outlier_nd[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=15.0, isReference="Reference set", correctedRt=10.00000, predictedRtDrift=15.000000)
expected_corrected_outlier_nd[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt=31.50000 , predictedRtDrift=3.500000)
expected_corrected_outlier_nd[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=50.23268, predictedRtDrift=-5.232684)
expected_corrected_outlier_nd[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.00000, predictedRtDrift=7.000000)
set.seed(22142)
polynomial_ransac_outlier <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable_outlier, method='polynomial', params=list(polynomialOrder=1), robust=TRUE))
set.seed(12149)
polynomial_ransac_outlier_dg3 <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable_outlier,
method='polynomial', params=list(polynomialOrder=3), robust=TRUE))
set.seed(4871414)
polynomial_ransac_outlier_nd <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable_outlier,
method='polynomial', params=list(polynomialOrder=3), robust=TRUE))
expect_equal(polynomial_ransac_outlier$result, expected_corrected_outlier, tolerance=1e-5)
expect_equal(polynomial_ransac_outlier_dg3$result, expected_corrected_outlier_dg3, tolerance=1e-5)
expect_equal(polynomial_ransac_outlier_nd$result, expected_corrected_outlier_nd, tolerance=1e-5)
})
test_that('constant rt correction', {
# Input
tmp_targetFeatTable <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_targetFeatTable[1,] <- c("ID-1", "Cpd 1", 11, 1.1)
tmp_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 25, 1.5)
tmp_targetFeatTable[3,] <- c("ID-3", "Cpd 3", 35, 3.5)
tmp_targetFeatTable[4,] <- c("ID-4", "Cpd 4", 45, 5.5)
tmp_targetFeatTable[5,] <- c("ID-5", "Cpd 5", 55, 7)
tmp_targetFeatTable[, c(3:4)] <- sapply(tmp_targetFeatTable[, c(3:4)], as.numeric)
tmp_referenceTable <- data.frame(matrix(vector(), 1, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_referenceTable[1,] <- c("Ref-1", "Ref Cpd 1", 11, 1.1)
tmp_referenceTable[, c(3:4)] <- sapply(tmp_referenceTable[, c(3:4)], as.numeric)
# Expected model and results
expected_corrected <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=9.9, predictedRtDrift=1.1)
expected_corrected[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=1.5, isReference="External set", correctedRt=23.9, predictedRtDrift=1.1)
expected_corrected[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt=33.9, predictedRtDrift=1.1)
expected_corrected[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=43.9, predictedRtDrift=1.1)
expected_corrected[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=53.9, predictedRtDrift=1.1)
constant_correction <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='constant', params=list(polynomialOrder=1), robust=FALSE))
expect_equal(constant_correction$result, expected_corrected, tolerance=1e-5)
})
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context('peakPantheR_applyRTCorrection()')
## Input and expected data
test_that('raises error()', {
# Input
tmp_targetFeatTable <- data.frame(matrix(vector(), 2, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_targetFeatTable[1,] <- c("ID-1", "Cpd 1", 11, 1.1)
tmp_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 25, 3.2)
tmp_targetFeatTable[, c(3:4)] <- sapply(tmp_targetFeatTable[, c(3:4)], as.numeric)
tmp_referenceTable <- data.frame(matrix(vector(), 2, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_referenceTable[1,] <- c("Ref-1", "Ref Cpd 1", 10, 1)
tmp_referenceTable[2,] <- c("Ref-2", "Ref Cpd 2", 20, 1.5)
tmp_referenceTable[, c(3:4)] <- sapply(tmp_referenceTable[, c(3:4)], as.numeric)
# Trigger checks on targetFeatTable input
# targetFeatTable is not a data.frame
expect_error(peakPantheR_applyRTCorrection(targetFeatTable='notADataFrame', referenceTable=tmp_referenceTable), 'specified targetFeatTable is not a data.frame')
# targetFeatTable has wrong columns
wrongDF1 <- tmp_targetFeatTable
colnames(wrongDF1) <- c("not cpdID", "cpdName", "rt", "rt_dev_sec")
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=wrongDF1, referenceTable=tmp_referenceTable), "expected columns in targetFeatTable are \"cpdID\", \"cpdName\", \"rt\", \"rt_dev_sec\"")
# targetFeatTable$cpdID not character
wrongDF2 <- tmp_targetFeatTable
wrongDF2$cpdID <- c(5, 5)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=wrongDF2, referenceTable=tmp_referenceTable), 'cpdID must be character')
# targetFeatTable$cpdName not character
wrongDF3 <- tmp_targetFeatTable
wrongDF3$cpdName <- c(5, 5)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=wrongDF3, referenceTable=tmp_referenceTable), 'cpdName must be character')
# targetFeatTable$rt not numeric
wrongDF4 <- tmp_targetFeatTable
wrongDF4$rt <- c('notNumeric', 'notNumeric')
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=wrongDF4, referenceTable=tmp_referenceTable), 'rt must be numeric')
# targetFeatTable$rt_dev_sec not numeric or NA
wrongDF5 <- tmp_targetFeatTable
wrongDF5$rt_dev_sec <- c('notNumeric', 'notNumeric')
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=wrongDF5, referenceTable=tmp_referenceTable), 'rt_dev_sec must be numeric or NA')
# wrong referenceTable
# referenceTable is not a data.frame
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable='notADataFrame'), 'specified referenceTable is not a data.frame')
# referenceTable has wrong columns
wrongDF6 <- tmp_referenceTable
colnames(wrongDF6) <- c("not cpdID", "cpdName", "rt", "rt_dev_sec")
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=wrongDF6), 'expected columns in referenceTable are "cpdID", "cpdName", "rt" and "rt_dev_sec"')
# referenceTable$cpdID not character
wrongDF11 <- tmp_referenceTable
wrongDF11$cpdID <- c(5, 5)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=wrongDF11), 'cpdID must be character')
# referenceTable$cpdName not character
wrongDF12 <- tmp_referenceTable
wrongDF12$cpdName <- c(5, 5)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=wrongDF12), 'cpdName must be character')
# referenceTable$rt not numeric or NA
wrongDF13 <- tmp_referenceTable
wrongDF13$rt <- c('notNumeric', 'notNumeric')
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=wrongDF13), 'rt must be numeric or NA')
# referenceTable$rt_dev_sec not numeric or NA
wrongDF14 <- tmp_referenceTable
wrongDF14$rt_dev_sec <- c('notNumeric', 'notNumeric')
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=wrongDF14), 'rt_dev_sec must be numeric or NA')
# unknown correction method
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='notKnown'), 'Error: \"method\" must be one of: \"polynomial\", \"constant\"', fixed=TRUE)
# params is not a list or character
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, params=5), 'Check input, "params" must be list', fixed=TRUE)
# Robust is not logical
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, robust=3, params=list(polynomialOrder=1)), 'robust must be either TRUE or FALSE', fixed=TRUE)
# Method <-> params SPECIFIC CHECKS
# method = polynomial but no polynomial order
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(a=5), robust=TRUE), 'polynomialOrder must be provided in params', fixed=TRUE)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(a=5), robust=FALSE), 'polynomialOrder must be provided in params', fixed=TRUE)
# method = polynomial but polynomial order is not valid
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder='NA'), robust=FALSE), 'polynomialOrder must be an integer and equal or greater than 1', fixed=TRUE)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=1.3), robust=FALSE), 'polynomialOrder must be an integer and equal or greater than 1', fixed=TRUE)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=1.3), robust=TRUE), 'polynomialOrder must be an integer and equal or greater than 1', fixed=TRUE)
# method = polynomial, valid polynomial order but exceeds number of references
expect_warning(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=4), robust=FALSE), '`polynomialOrder` is larger than the number of references passed. `polynomialOrder` will be set equal to number of reference compounds - 1', fixed=TRUE)
expect_warning(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=4), robust=TRUE), '`polynomialOrder` is larger than the number of references passed. `polynomialOrder` will be set equal to number of reference compounds - 1', fixed=TRUE)
# test sugestion of method=constant if ref = 1
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable[1, ], method='polynomial', params=list(polynomialOrder=1), robust=FALSE), 'No function can be fitted with a single reference. Use method=\`constant\` instead.', fixed=TRUE)
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable[1, ], method='polynomial', params=list(polynomialOrder=1), robust=TRUE), 'No function can be fitted with a single reference. Use method=\`constant\` instead.', fixed=TRUE)
# test constant method throws error with multiple references
expect_error(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='constant', params=list(polynomialOrder=1), robust=TRUE), "`constant` Rt correction can only use a single reference", fixed=TRUE)
})
test_that('polynomial method rt correction', {
# Input
tmp_targetFeatTable <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_targetFeatTable[1,] <- c("ID-1", "Cpd 1", 11, 1.1)
tmp_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 25, 1.5)
tmp_targetFeatTable[3,] <- c("ID-3", "Cpd 3", 35, 3.5)
tmp_targetFeatTable[4,] <- c("ID-4", "Cpd 4", 45, 5.5)
tmp_targetFeatTable[5,] <- c("ID-5", "Cpd 5", 55, 7)
tmp_targetFeatTable[, c(3:4)] <- sapply(tmp_targetFeatTable[, c(3:4)], as.numeric)
tmp_referenceTable <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_referenceTable[1,] <- c("Ref-1", "Ref Cpd 1", 11, 1.1)
tmp_referenceTable[2,] <- c("Ref-2", "Ref Cpd 2", 25, 1.5)
tmp_referenceTable[3,] <- c("Ref-3", "Ref Cpd 3", 35, 3.5)
tmp_referenceTable[4,] <- c("Ref-4", "Ref Cpd 4", 45, 5.5)
tmp_referenceTable[5,] <- c("Ref-5", "Ref Cpd 5", 55, 7)
tmp_referenceTable[, c(3:4)] <- sapply(tmp_referenceTable[, c(3:4)], as.numeric)
# Expected model and results
expected_corrected <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=10.61282, predictedRtDrift=0.387176)
expected_corrected[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=1.5, isReference="External set", correctedRt=22.60164, predictedRtDrift=2.398363)
expected_corrected[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt= 31.16508, predictedRtDrift=3.834925)
expected_corrected[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.72851, predictedRtDrift=5.271487)
expected_corrected[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.29195, predictedRtDrift=6.708049)
expected_corrected_dg3 <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected_dg3[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=9.907072, predictedRtDrift=1.092928)
expected_corrected_dg3[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=1.5, isReference="External set", correctedRt=23.457678, predictedRtDrift=1.542322)
expected_corrected_dg3[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt= 31.574063, predictedRtDrift=3.425937)
expected_corrected_dg3[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.447720, predictedRtDrift=5.552280)
expected_corrected_dg3[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.013466, predictedRtDrift=6.986534)
# results (output, warnings and messages)
polynomial_regression_dg1 <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=1), robust=FALSE))
polynomial_regression_dg3 <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable,
method='polynomial', params=list(polynomialOrder=3), robust=FALSE))
# Check results
expect_equal(polynomial_regression_dg1$result, expected_corrected, tolerance=1e-5)
expect_equal(polynomial_regression_dg3$result, expected_corrected_dg3, tolerance=1e-5)
# test isReference is assigned correctely
tmp_referenceTable <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_referenceTable[1,] <- c("Ref-1", "Ref Cpd 1", 11, 1.1)
tmp_referenceTable[2,] <- c("ID-2", "Cpd 2", 25, 1.5)
tmp_referenceTable[3,] <- c("Ref-3", "Ref Cpd 3", 35, 3.5)
tmp_referenceTable[4,] <- c("Ref-4", "Ref Cpd 4", 45, 5.5)
tmp_referenceTable[5,] <- c("Ref-5", "Ref Cpd 5", 55, 7)
tmp_referenceTable[, c(3:4)] <- sapply(tmp_referenceTable[, c(3:4)], as.numeric)
expected_corrected_isRef <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected_isRef[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=10.61282, predictedRtDrift=0.387176)
expected_corrected_isRef[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=1.5, isReference="Reference set", correctedRt=22.60164, predictedRtDrift=2.398363)
expected_corrected_isRef[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt= 31.16508, predictedRtDrift=3.834925)
expected_corrected_isRef[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.72851, predictedRtDrift=5.271487)
expected_corrected_isRef[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.29195, predictedRtDrift=6.708049)
polynomial_regression_ref <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=1), robust=FALSE))
expect_equal(polynomial_regression_ref$result, expected_corrected_isRef, tolerance=1e-5)
# Check messages (cannot check time on message)
#expect_equal(length(result_integrateFIR$messages), 5)
#expect_equal(result_integrateFIR$messages[c(1,2,4)], expected_messages)
})
test_that('RANSAC rt correction function', {
# Input
tmp_targetFeatTable <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_targetFeatTable[1,] <- c("ID-1", "Cpd 1", 11, 1.1)
tmp_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 25, 1.5)
tmp_targetFeatTable[3,] <- c("ID-3", "Cpd 3", 35, 3.5)
tmp_targetFeatTable[4,] <- c("ID-4", "Cpd 4", 45, 5.5)
tmp_targetFeatTable[5,] <- c("ID-5", "Cpd 5", 55, 7)
tmp_targetFeatTable[, c(3:4)] <- sapply(tmp_targetFeatTable[, c(3:4)], as.numeric)
tmp_referenceTable <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_referenceTable[1,] <- c("Ref-1", "Ref Cpd 1", 11, 1.1)
tmp_referenceTable[2,] <- c("Ref-2", "Ref Cpd 2", 25, 1.5)
tmp_referenceTable[3,] <- c("Ref-3", "Ref Cpd 3", 35, 3.5)
tmp_referenceTable[4,] <- c("Ref-4", "Ref Cpd 4", 45, 5.5)
tmp_referenceTable[5,] <- c("Ref-5", "Ref Cpd 5", 55, 7)
tmp_referenceTable[, c(3:4)] <- sapply(tmp_referenceTable[, c(3:4)], as.numeric)
# Expected model and results
expected_corrected <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=10.61282, predictedRtDrift=0.387176)
expected_corrected[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=1.5, isReference="External set", correctedRt=22.60164, predictedRtDrift=2.398363)
expected_corrected[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt= 31.16508, predictedRtDrift=3.834925)
expected_corrected[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.72851, predictedRtDrift=5.271487)
expected_corrected[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.29195, predictedRtDrift=6.708049)
expected_corrected_dg3 <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected_dg3[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=9.907072, predictedRtDrift=1.092928)
expected_corrected_dg3[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=1.5, isReference="External set", correctedRt=23.457678, predictedRtDrift=1.542322)
expected_corrected_dg3[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt= 31.574063, predictedRtDrift=3.425937)
expected_corrected_dg3[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.447720, predictedRtDrift=5.552280)
expected_corrected_dg3[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.013466, predictedRtDrift=6.986534)
# results (output, warnings and messages)
set.seed(19472)
polynomial_ransac_dg1 <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=1), robust=TRUE))
set.seed(390753)
polynomial_ransac_dg3 <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='polynomial', params=list(polynomialOrder=3), robust=TRUE))
# Check results
expect_equal(polynomial_ransac_dg1$result, expected_corrected, tolerance=1e-5)
expect_equal(polynomial_ransac_dg3$result, expected_corrected_dg3, tolerance=1e-5)
# Add outlier
tmp_referenceTable_outlier <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_referenceTable_outlier[1,] <- c("Ref-1", "Ref Cpd 1", 11, 1.1)
tmp_referenceTable_outlier[2,] <- c("ID-2", "Cpd 2", 25, 15)
tmp_referenceTable_outlier[3,] <- c("Ref-3", "Ref Cpd 3", 35, 3.5)
tmp_referenceTable_outlier[4,] <- c("Ref-4", "Ref Cpd 4", 45, 5.5)
tmp_referenceTable_outlier[5,] <- c("Ref-5", "Ref Cpd 5", 55, 7)
tmp_referenceTable_outlier[, c(3:4)] <- sapply(tmp_referenceTable_outlier[, c(3:4)], as.numeric)
# Modify outlier in targetFeatTable
tmp_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 25, 15)
tmp_targetFeatTable[, c(3:4)] <- sapply(tmp_targetFeatTable[, c(3:4)], as.numeric)
expected_corrected_outlier <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected_outlier[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=10.14133, predictedRtDrift=0.8586692)
expected_corrected_outlier[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=15.0, isReference="Reference set outlier", correctedRt=22.26570, predictedRtDrift=2.7343018)
expected_corrected_outlier[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt=30.92596, predictedRtDrift=4.0740394)
expected_corrected_outlier[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.58622, predictedRtDrift=5.4137769)
expected_corrected_outlier[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.24649, predictedRtDrift=6.7535145)
expected_corrected_outlier_dg3 <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected_outlier_dg3[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt= 9.90000, predictedRtDrift=1.100000)
expected_corrected_outlier_dg3[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=15.0, isReference="Reference set outlier", correctedRt=23.25802, predictedRtDrift=1.741979)
expected_corrected_outlier_dg3[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt=31.50000, predictedRtDrift=3.500000)
expected_corrected_outlier_dg3[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=39.50000, predictedRtDrift=5.500000)
expected_corrected_outlier_dg3[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.00000, predictedRtDrift=7.000000)
# Case where outlier is not detected
expected_corrected_outlier_nd <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected_outlier_nd[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt= 9.90000, predictedRtDrift=1.100000)
expected_corrected_outlier_nd[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=15.0, isReference="Reference set", correctedRt=10.00000, predictedRtDrift=15.000000)
expected_corrected_outlier_nd[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt=31.50000 , predictedRtDrift=3.500000)
expected_corrected_outlier_nd[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=50.23268, predictedRtDrift=-5.232684)
expected_corrected_outlier_nd[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=48.00000, predictedRtDrift=7.000000)
set.seed(22142)
polynomial_ransac_outlier <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable_outlier, method='polynomial', params=list(polynomialOrder=1), robust=TRUE))
set.seed(12149)
polynomial_ransac_outlier_dg3 <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable_outlier,
method='polynomial', params=list(polynomialOrder=3), robust=TRUE))
set.seed(4871414)
polynomial_ransac_outlier_nd <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable_outlier,
method='polynomial', params=list(polynomialOrder=3), robust=TRUE))
expect_equal(polynomial_ransac_outlier$result, expected_corrected_outlier, tolerance=1e-5)
expect_equal(polynomial_ransac_outlier_dg3$result, expected_corrected_outlier_dg3, tolerance=1e-5)
expect_equal(polynomial_ransac_outlier_nd$result, expected_corrected_outlier_nd, tolerance=1e-5)
})
test_that('constant rt correction', {
# Input
tmp_targetFeatTable <- data.frame(matrix(vector(), 5, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_targetFeatTable[1,] <- c("ID-1", "Cpd 1", 11, 1.1)
tmp_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 25, 1.5)
tmp_targetFeatTable[3,] <- c("ID-3", "Cpd 3", 35, 3.5)
tmp_targetFeatTable[4,] <- c("ID-4", "Cpd 4", 45, 5.5)
tmp_targetFeatTable[5,] <- c("ID-5", "Cpd 5", 55, 7)
tmp_targetFeatTable[, c(3:4)] <- sapply(tmp_targetFeatTable[, c(3:4)], as.numeric)
tmp_referenceTable <- data.frame(matrix(vector(), 1, 4, dimnames=list(c(), c("cpdID", "cpdName", "rt", "rt_dev_sec"))), stringsAsFactors=FALSE)
tmp_referenceTable[1,] <- c("Ref-1", "Ref Cpd 1", 11, 1.1)
tmp_referenceTable[, c(3:4)] <- sapply(tmp_referenceTable[, c(3:4)], as.numeric)
# Expected model and results
expected_corrected <- data.frame(cpdID=character(), cpdName=character(), rt=double(), rt_dev_sec=double(), isReference=character(),
correctedRt=double(), predictedRtDrift=double(), stringsAsFactors=FALSE)
expected_corrected[1,] <- list(cpdID="ID-1", cpdName="Cpd 1", rt=11, rt_dev_sec=1.1, isReference="External set", correctedRt=9.9, predictedRtDrift=1.1)
expected_corrected[2,] <- list(cpdID="ID-2", cpdName="Cpd 2", rt=25, rt_dev_sec=1.5, isReference="External set", correctedRt=23.9, predictedRtDrift=1.1)
expected_corrected[3,] <- list(cpdID="ID-3", cpdName="Cpd 3", rt=35, rt_dev_sec=3.5, isReference="External set", correctedRt=33.9, predictedRtDrift=1.1)
expected_corrected[4,] <- list(cpdID="ID-4", cpdName="Cpd 4", rt=45, rt_dev_sec=5.5, isReference="External set", correctedRt=43.9, predictedRtDrift=1.1)
expected_corrected[5,] <- list(cpdID="ID-5", cpdName="Cpd 5", rt=55, rt_dev_sec=7, isReference="External set", correctedRt=53.9, predictedRtDrift=1.1)
constant_correction <- evaluate_promise(peakPantheR_applyRTCorrection(targetFeatTable=tmp_targetFeatTable, referenceTable=tmp_referenceTable, method='constant', params=list(polynomialOrder=1), robust=FALSE))
expect_equal(constant_correction$result, expected_corrected, tolerance=1e-5)
})
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