tests/testthat/test_peakPantheRAnnotation_accessor-method.R
In phenomecentre/peakPantheR: Peak Picking and Annotation of High Resolution Experiments
context('peakPantheRAnnotation_accessor-method()')
## Test the accessors with the right values
skip_if_not_installed('faahKO', minimum_version = '1.18.0')
library(faahKO)
## Input data
# spectraPaths
input_spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = "faahKO"),
system.file('cdf/KO/ko16.CDF', package = "faahKO"),
system.file('cdf/KO/ko18.CDF', package = "faahKO"))
# targetFeatTable
input_targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c("cpdID", "cpdName", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax"))), stringsAsFactors=FALSE)
input_targetFeatTable[1,] <- c("ID-1", "Cpd 1", 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222)
input_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962)
input_targetFeatTable[,c(3:8)] <- sapply(input_targetFeatTable[,c(3:8)], as.numeric)
# FIR
input_FIR <- data.frame(matrix(vector(), 2, 4, dimnames=list(c(), c("rtMin", "rtMax", "mzMin", "mzMax"))), stringsAsFactors=FALSE)
input_FIR[1,] <- c(1., 2., 3., 4.)
input_FIR[2,] <- c(5., 6., 7., 8.)
# uROI
input_uROI <- data.frame(matrix(vector(), 2, 6, dimnames=list(c(), c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax"))), stringsAsFactors=FALSE)
input_uROI[1,] <- c(9., 10., 11., 12., 13., 14.)
input_uROI[2,] <- c(15., 16., 17., 18., 19., 20.)
# TICs
input_TIC <- c(2410533091, 2524040155, 2332817115)
# cpdMetadata
input_cpdMetadata <- data.frame(matrix(data=c('a','b',1,2), nrow=2, ncol=2, dimnames=list(c(),c('testcol1','testcol2')), byrow=FALSE), stringsAsFactors=FALSE)
# spectraMetadata
input_spectraMetadata <- data.frame(matrix(data=c('c','d','e',3,4,5), nrow=3, ncol=2, dimnames=list(c(),c('testcol1','testcol2')), byrow=FALSE), stringsAsFactors=FALSE)
# acquisitionTime
input_acquisitionTime <- c(as.character(Sys.time()), as.character(Sys.time()+900), as.character(Sys.time()+1800))
# peakTables
# 1
peakTable1 <- data.frame(matrix(vector(), 2, 16, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "peakAreaRaw", "maxIntMeasured", "maxIntPredicted", "is_filled", "ppm_error", "rt_dev_sec", "tailingFactor", "asymmetryFactor"))),stringsAsFactors=FALSE)
peakTable1[1,] <- c(TRUE, 3309.7589296586070, 3346.8277590361445, 3385.4098874628098, 522.194778, 522.20001220703125, 522.205222, 26133726.6811244078, 26133726, 889280, 901015.80529226747, FALSE, 0.023376160866574614, 1.93975903614455092, 1.0153573486330891, 1.0268238825675249)
peakTable1[2,] <- c(TRUE, 3345.3766648628907, 3386.5288072289159, 3428.2788374983961, 496.20001220703125, 496.20001220703125, 496.20001220703125, 35472141.3330242932, 35472141, 1128960, 1113576.69008227298, FALSE, 0.024601030353423384, 0.95180722891564074, 1.0053782620427065, 1.0093180792278085)
peakTable1[,c(1,12)] <- sapply(peakTable1[,c(1,12)], as.logical)
peakTable1[,c(2:11,13:16)] <- sapply(peakTable1[,c(2:11,13:16)], as.numeric)
# 2
peakTable2 <- data.frame(matrix(vector(), 2, 16, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "peakAreaRaw", "maxIntMeasured", "maxIntPredicted", "is_filled", "ppm_error", "rt_dev_sec", "tailingFactor", "asymmetryFactor"))),stringsAsFactors=FALSE)
peakTable2[1,] <- c(TRUE, 3326.1063495851854, 3365.102, 3407.2726475892355, 522.194778, 522.20001220703125, 522.205222, 24545301.622835573, 24545301, 761664, 790802.2209998488, FALSE, 0.023376160866574614, 0.2139999999999, 1.0339153786516375, 1.0630802030537212)
peakTable2[2,] <- c(TRUE, 3365.0238566258713, 3405.791, 3453.4049569205681, 496.195038, 496.20001220703125, 496.204962, 37207579.286265120, 37207579, 1099264, 1098720.2929832144, FALSE, 0.024601030353423384, 20.2139999999999, 1.0839602450900523, 1.1717845972583161)
peakTable2[,c(1,12)] <- sapply(peakTable2[,c(1,12)], as.logical)
peakTable2[,c(2:11,13:16)] <- sapply(peakTable2[,c(2:11,13:16)], as.numeric)
# 3
peakTable3 <- data.frame(matrix(vector(), 2, 16, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "peakAreaRaw", "maxIntMeasured", "maxIntPredicted", "is_filled", "ppm_error", "rt_dev_sec", "tailingFactor", "asymmetryFactor"))),stringsAsFactors=FALSE)
peakTable3[1,] <- c(TRUE, 3333.8625894557053, 3368.233, 3407.4362838927614, 522.194778, 522.20001220703125, 522.205222, 21447174.404490683, 21447174, 758336, 765009.9805796633, FALSE, 0.023376160866574614, 23.345000000000255, 1.0609102044546637, 1.1155310457756928)
peakTable3[2,] <- c(TRUE, 3373.3998828113113, 3413.4952530120481, 3454.4490330927388, 496.195038, 496.20001220703125, 496.204962, 35659353.614476241, 35659353, 1149440, 1145857.7611069249, TRUE, 0.024601030353423384, 27.918253012047899, 1.0081407426394933, 1.0143315197994494)
peakTable3[,c(1,12)] <- sapply(peakTable3[,c(1,12)], as.logical)
peakTable3[,c(2:11, 13:16)] <- sapply(peakTable3[,c(2:11,13:16)], as.numeric)
input_peakTables <- list(peakTable1, peakTable2, peakTable3)
# peakFit
# 1
cFit1.1 <- list(amplitude=162404.8057918259, center=3341.888, sigma=0.078786133031045896, gamma=0.0018336101984172684, fitStatus=2, curveModel="skewedGaussian")
class(cFit1.1) <- 'peakPantheR_curveFit'
cFit1.2 <- list(amplitude=199249.10572753669, center=3382.577, sigma=0.074904415304607966, gamma=0.0011471899372353885, fitStatus=2, curveModel="skewedGaussian")
class(cFit1.2) <- 'peakPantheR_curveFit'
# 2
cFit2.1 <- list(amplitude=124090.83425474487, center=3359.102, sigma=0.071061541060964212, gamma=0.0018336072657203239, fitStatus=2, curveModel="skewedGaussian")
class(cFit2.1) <- 'peakPantheR_curveFit'
cFit2.2 <- list(amplitude=151407.23415130575, center=3399.791, sigma=0.063753866057052563, gamma=0.001676782834598999, fitStatus=2, curveModel="skewedGaussian")
class(cFit2.2) <- 'peakPantheR_curveFit'
# 3
cFit3.1 <- list(amplitude=122363.51256736703, center=3362.233, sigma=0.075489598945304492, gamma=0.0025160536725299734, fitStatus=2, curveModel="skewedGaussian")
class(cFit3.1) <- 'peakPantheR_curveFit'
cFit3.2 <- list(amplitude=204749.86097918145, center=3409.182, sigma=0.075731781812843249, gamma=0.0013318670577834328, fitStatus=2, curveModel="skewedGaussian")
class(cFit3.2) <- 'peakPantheR_curveFit'
input_peakFit <- list(list(cFit1.1, cFit1.2), list(cFit2.1, cFit2.2), list(cFit3.1, cFit3.2))
# dataPoint
tmp_raw_data1 <- MSnbase::readMSData(input_spectraPaths[1], centroided=TRUE, mode='onDisk')
ROIDataPoints1 <- extractSignalRawData(tmp_raw_data1, rt=input_targetFeatTable[,c('rtMin','rtMax')], mz=input_targetFeatTable[,c('mzMin','mzMax')], verbose=FALSE)
tmp_raw_data2 <- MSnbase::readMSData(input_spectraPaths[2], centroided=TRUE, mode='onDisk')
ROIDataPoints2 <- extractSignalRawData(tmp_raw_data2, rt=input_targetFeatTable[,c('rtMin','rtMax')], mz=input_targetFeatTable[,c('mzMin','mzMax')], verbose=FALSE)
tmp_raw_data3 <- MSnbase::readMSData(input_spectraPaths[3], centroided=TRUE, mode='onDisk')
ROIDataPoints3 <- extractSignalRawData(tmp_raw_data3, rt=input_targetFeatTable[,c('rtMin','rtMax')], mz=input_targetFeatTable[,c('mzMin','mzMax')], verbose=FALSE)
input_dataPoints <- list(ROIDataPoints1, ROIDataPoints2, ROIDataPoints3)
# Object, fully filled
filledAnnotation <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable, FIR=input_FIR, uROI=input_uROI, useFIR=TRUE, uROIExist=TRUE, useUROI=TRUE, cpdMetadata=input_cpdMetadata, spectraMetadata=input_spectraMetadata, acquisitionTime=input_acquisitionTime, TIC=input_TIC, peakTables=input_peakTables, dataPoints=input_dataPoints, peakFit=input_peakFit, isAnnotated=TRUE)
# Empty annotation, special case for peakTables
emptyAnnotation <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable)
test_that('accessors return the correct values', {
expected_ROI <- input_targetFeatTable[, c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "cpdID", "cpdName")]
expected_FIR <- cbind.data.frame(input_FIR, cpdID=c("ID-1","ID-2"), cpdName=c("Cpd 1", "Cpd 2"), stringsAsFactors=FALSE)
expected_uROI <- cbind.data.frame(input_uROI, cpdID=c("ID-1","ID-2"), cpdName=c("Cpd 1", "Cpd 2"), stringsAsFactors=FALSE)
expected_acquisitionTime <- as.POSIXct(input_acquisitionTime)
expected_peakTables <- list(cbind.data.frame(peakTable1, cpdID=c("ID-1","ID-2"), cpdName=c("Cpd 1","Cpd 2"), stringsAsFactors=FALSE), cbind.data.frame(peakTable2, cpdID=c("ID-1","ID-2"), cpdName=c("Cpd 1","Cpd 2"), stringsAsFactors=FALSE), cbind.data.frame(peakTable3, cpdID=c("ID-1","ID-2"), cpdName=c("Cpd 1","Cpd 2"), stringsAsFactors=FALSE))
expected_dataPoints <- input_dataPoints
expected_peakFit <- input_peakFit
expected_filename <- c("ko15", "ko16", "ko18")
# Check accessors
# Basic slots
# cpdID
expect_equal(cpdID(filledAnnotation), c("ID-1","ID-2"))
# cpdName
expect_equal(cpdName(filledAnnotation), c("Cpd 1", "Cpd 2"))
# ROI
expect_equal(ROI(filledAnnotation), expected_ROI)
# FIR
expect_equal(FIR(filledAnnotation), expected_FIR)
# uROI
expect_equal(uROI(filledAnnotation), expected_uROI)
# filepath
expect_equal(filepath(filledAnnotation), input_spectraPaths)
# cpdMetadata
expect_equal(cpdMetadata(filledAnnotation), input_cpdMetadata)
# spectraMetadata
expect_equal(spectraMetadata(filledAnnotation), input_spectraMetadata)
# acquisitionTIme
expect_equal(acquisitionTime(filledAnnotation), expected_acquisitionTime)
# uROIExist
expect_true(uROIExist(filledAnnotation))
# useUROI
expect_true(useUROI(filledAnnotation))
# useFIR
expect_true(useFIR(filledAnnotation))
# TIC
expect_equal(TIC(filledAnnotation), input_TIC)
# peakTables
expect_equal(peakTables(filledAnnotation), expected_peakTables)
# dataPoints
expect_equal(dataPoints(filledAnnotation), expected_dataPoints)
# peakFit
expect_equal(peakFit(filledAnnotation), expected_peakFit)
# isAnnotated
expect_true(isAnnotated(filledAnnotation))
# nbSamples
expect_equal(nbSamples(filledAnnotation), 3)
# nbCompounds
expect_equal(nbCompounds(filledAnnotation), 2)
# filename
expect_equal(filename(filledAnnotation), expected_filename)
# annotationTable
# simple value
expected_annotationTable <- data.frame(matrix(c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), 3, 2), stringsAsFactors=FALSE)
rownames(expected_annotationTable) <- input_spectraPaths
colnames(expected_annotationTable) <- c("ID-1", "ID-2")
expect_equal(annotationTable(filledAnnotation, 'found'), expected_annotationTable)
# no sample
tmp_noSample <- peakPantheRAnnotation()
expected_noSample <- data.frame(matrix(vector(), 0, 0), stringsAsFactors=FALSE)
rownames(expected_noSample) <- tmp_noSample@filepath
colnames(expected_noSample) <- tmp_noSample@cpdID
expect_equal(annotationTable(tmp_noSample, 'found'), expected_noSample)
# no peakTables (not annotated or no compounds)
tmp_noPeakTables <- filledAnnotation
tmp_noPeakTables@peakTables <- vector("list", 3)
expected_noPeakTables <- data.frame(matrix(vector(), 3, 2), stringsAsFactors=FALSE)
rownames(expected_noPeakTables) <- tmp_noPeakTables@filepath
colnames(expected_noPeakTables) <- tmp_noPeakTables@cpdID
expect_equal(annotationTable(tmp_noPeakTables, 'found'), expected_noPeakTables)
# only 1 compounds (sapply simplify to vector and not matrix)
tmp_singleCpd <- filledAnnotation[,1]
expected_mz <- data.frame(matrix(c(522.20001220703125, 522.20001220703125, 522.20001220703125), 3, 1), stringsAsFactors=FALSE)
rownames(expected_mz) <- filledAnnotation@filepath
colnames(expected_mz) <- filledAnnotation@cpdID[1]
expect_equal(annotationTable(tmp_singleCpd, 'mz'), expected_mz)
# raise error if column doesn't exist
expect_error(annotationTable(filledAnnotation, 'notAnExistingColumn'), 'input column is not a column of peakTables', fixed=TRUE)
## try some more of the peakTable columns
# mz
expected_mz <- data.frame(matrix(c(522.20001220703125, 522.20001220703125, 522.20001220703125, 496.20001220703125, 496.20001220703125, 496.20001220703125), 3, 2), stringsAsFactors=FALSE)
rownames(expected_mz) <- filledAnnotation@filepath
colnames(expected_mz) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'mz'), expected_mz)
# mzMin
expected_mzMin <- data.frame(matrix(c(522.194778, 522.194778, 522.194778, 496.20001220703125, 496.195038, 496.195038), 3, 2), stringsAsFactors=FALSE)
rownames(expected_mzMin) <- filledAnnotation@filepath
colnames(expected_mzMin) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'mzMin'), expected_mzMin)
# mzMax
expected_mzMax <- data.frame(matrix(c(522.205222, 522.205222, 522.205222, 496.20001220703125, 496.204962, 496.204962), 3, 2), stringsAsFactors=FALSE)
rownames(expected_mzMax) <- filledAnnotation@filepath
colnames(expected_mzMax) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'mzMax'), expected_mzMax)
# rt
expected_rt <- data.frame(matrix(c(3346.8277590361445, 3365.102, 3368.233, 3386.5288072289159, 3405.791, 3413.4952530120481), 3, 2), stringsAsFactors=FALSE)
rownames(expected_rt) <- filledAnnotation@filepath
colnames(expected_rt) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'rt'), expected_rt)
# rtMin
expected_rtMin <- data.frame(matrix(c(3309.7589296586070, 3326.1063495851854, 3333.8625894557053, 3345.3766648628907, 3365.0238566258713, 3373.3998828113113), 3, 2), stringsAsFactors=FALSE)
rownames(expected_rtMin) <- filledAnnotation@filepath
colnames(expected_rtMin) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'rtMin'), expected_rtMin)
# rtMax
expected_rtMax <- data.frame(matrix(c(3385.4098874628098, 3407.2726475892355, 3407.4362838927614, 3428.2788374983961, 3453.4049569205681, 3454.4490330927388), 3, 2), stringsAsFactors=FALSE)
rownames(expected_rtMax) <- filledAnnotation@filepath
colnames(expected_rtMax) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'rtMax'), expected_rtMax)
# peakArea
expected_peakArea <- data.frame(matrix(c(26133726.6811244078, 24545301.622835573, 21447174.404490683, 35472141.3330242932, 37207579.286265120, 35659353.614476241), 3, 2), stringsAsFactors=FALSE)
rownames(expected_peakArea) <- filledAnnotation@filepath
colnames(expected_peakArea) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'peakArea'), expected_peakArea)
# EICs
# default (sum)
tmp_EIC_sum_1.1 <- generateIonChromatogram(input_dataPoints[[1]][[1]], aggregationFunction='sum')
tmp_EIC_sum_1.2 <- generateIonChromatogram(input_dataPoints[[1]][[2]], aggregationFunction='sum')
tmp_EIC_sum_2.1 <- generateIonChromatogram(input_dataPoints[[2]][[1]], aggregationFunction='sum')
tmp_EIC_sum_2.2 <- generateIonChromatogram(input_dataPoints[[2]][[2]], aggregationFunction='sum')
tmp_EIC_sum_3.1 <- generateIonChromatogram(input_dataPoints[[3]][[1]], aggregationFunction='sum')
tmp_EIC_sum_3.2 <- generateIonChromatogram(input_dataPoints[[3]][[2]], aggregationFunction='sum')
expected_EIC_sum <- list(list(tmp_EIC_sum_1.1, tmp_EIC_sum_1.2),
list(tmp_EIC_sum_2.1, tmp_EIC_sum_2.2),
list(tmp_EIC_sum_3.1, tmp_EIC_sum_3.2))
expect_equal(EICs(filledAnnotation), expected_EIC_sum)
# change aggregationFunction (max)
tmp_EIC_max_1.1 <- generateIonChromatogram(input_dataPoints[[1]][[1]], aggregationFunction='max')
tmp_EIC_max_1.2 <- generateIonChromatogram(input_dataPoints[[1]][[2]], aggregationFunction='max')
tmp_EIC_max_2.1 <- generateIonChromatogram(input_dataPoints[[2]][[1]], aggregationFunction='max')
tmp_EIC_max_2.2 <- generateIonChromatogram(input_dataPoints[[2]][[2]], aggregationFunction='max')
tmp_EIC_max_3.1 <- generateIonChromatogram(input_dataPoints[[3]][[1]], aggregationFunction='max')
tmp_EIC_max_3.2 <- generateIonChromatogram(input_dataPoints[[3]][[2]], aggregationFunction='max')
expected_EIC_max <- list(list(tmp_EIC_max_1.1, tmp_EIC_max_1.2),
list(tmp_EIC_max_2.1, tmp_EIC_max_2.2),
list(tmp_EIC_max_3.1, tmp_EIC_max_3.2))
expect_equal(EICs(filledAnnotation, 'max'), expected_EIC_max)
# Special peakTables behaviour when peakTable is NULL
expect_equal(peakTables(emptyAnnotation), list(NULL, NULL, NULL))
})
phenomecentre/peakPantheR documentation built on Feb. 29, 2024, 9:07 p.m.
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context('peakPantheRAnnotation_accessor-method()')
## Test the accessors with the right values
skip_if_not_installed('faahKO', minimum_version = '1.18.0')
library(faahKO)
## Input data
# spectraPaths
input_spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = "faahKO"),
system.file('cdf/KO/ko16.CDF', package = "faahKO"),
system.file('cdf/KO/ko18.CDF', package = "faahKO"))
# targetFeatTable
input_targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c("cpdID", "cpdName", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax"))), stringsAsFactors=FALSE)
input_targetFeatTable[1,] <- c("ID-1", "Cpd 1", 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222)
input_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962)
input_targetFeatTable[,c(3:8)] <- sapply(input_targetFeatTable[,c(3:8)], as.numeric)
# FIR
input_FIR <- data.frame(matrix(vector(), 2, 4, dimnames=list(c(), c("rtMin", "rtMax", "mzMin", "mzMax"))), stringsAsFactors=FALSE)
input_FIR[1,] <- c(1., 2., 3., 4.)
input_FIR[2,] <- c(5., 6., 7., 8.)
# uROI
input_uROI <- data.frame(matrix(vector(), 2, 6, dimnames=list(c(), c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax"))), stringsAsFactors=FALSE)
input_uROI[1,] <- c(9., 10., 11., 12., 13., 14.)
input_uROI[2,] <- c(15., 16., 17., 18., 19., 20.)
# TICs
input_TIC <- c(2410533091, 2524040155, 2332817115)
# cpdMetadata
input_cpdMetadata <- data.frame(matrix(data=c('a','b',1,2), nrow=2, ncol=2, dimnames=list(c(),c('testcol1','testcol2')), byrow=FALSE), stringsAsFactors=FALSE)
# spectraMetadata
input_spectraMetadata <- data.frame(matrix(data=c('c','d','e',3,4,5), nrow=3, ncol=2, dimnames=list(c(),c('testcol1','testcol2')), byrow=FALSE), stringsAsFactors=FALSE)
# acquisitionTime
input_acquisitionTime <- c(as.character(Sys.time()), as.character(Sys.time()+900), as.character(Sys.time()+1800))
# peakTables
# 1
peakTable1 <- data.frame(matrix(vector(), 2, 16, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "peakAreaRaw", "maxIntMeasured", "maxIntPredicted", "is_filled", "ppm_error", "rt_dev_sec", "tailingFactor", "asymmetryFactor"))),stringsAsFactors=FALSE)
peakTable1[1,] <- c(TRUE, 3309.7589296586070, 3346.8277590361445, 3385.4098874628098, 522.194778, 522.20001220703125, 522.205222, 26133726.6811244078, 26133726, 889280, 901015.80529226747, FALSE, 0.023376160866574614, 1.93975903614455092, 1.0153573486330891, 1.0268238825675249)
peakTable1[2,] <- c(TRUE, 3345.3766648628907, 3386.5288072289159, 3428.2788374983961, 496.20001220703125, 496.20001220703125, 496.20001220703125, 35472141.3330242932, 35472141, 1128960, 1113576.69008227298, FALSE, 0.024601030353423384, 0.95180722891564074, 1.0053782620427065, 1.0093180792278085)
peakTable1[,c(1,12)] <- sapply(peakTable1[,c(1,12)], as.logical)
peakTable1[,c(2:11,13:16)] <- sapply(peakTable1[,c(2:11,13:16)], as.numeric)
# 2
peakTable2 <- data.frame(matrix(vector(), 2, 16, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "peakAreaRaw", "maxIntMeasured", "maxIntPredicted", "is_filled", "ppm_error", "rt_dev_sec", "tailingFactor", "asymmetryFactor"))),stringsAsFactors=FALSE)
peakTable2[1,] <- c(TRUE, 3326.1063495851854, 3365.102, 3407.2726475892355, 522.194778, 522.20001220703125, 522.205222, 24545301.622835573, 24545301, 761664, 790802.2209998488, FALSE, 0.023376160866574614, 0.2139999999999, 1.0339153786516375, 1.0630802030537212)
peakTable2[2,] <- c(TRUE, 3365.0238566258713, 3405.791, 3453.4049569205681, 496.195038, 496.20001220703125, 496.204962, 37207579.286265120, 37207579, 1099264, 1098720.2929832144, FALSE, 0.024601030353423384, 20.2139999999999, 1.0839602450900523, 1.1717845972583161)
peakTable2[,c(1,12)] <- sapply(peakTable2[,c(1,12)], as.logical)
peakTable2[,c(2:11,13:16)] <- sapply(peakTable2[,c(2:11,13:16)], as.numeric)
# 3
peakTable3 <- data.frame(matrix(vector(), 2, 16, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "peakAreaRaw", "maxIntMeasured", "maxIntPredicted", "is_filled", "ppm_error", "rt_dev_sec", "tailingFactor", "asymmetryFactor"))),stringsAsFactors=FALSE)
peakTable3[1,] <- c(TRUE, 3333.8625894557053, 3368.233, 3407.4362838927614, 522.194778, 522.20001220703125, 522.205222, 21447174.404490683, 21447174, 758336, 765009.9805796633, FALSE, 0.023376160866574614, 23.345000000000255, 1.0609102044546637, 1.1155310457756928)
peakTable3[2,] <- c(TRUE, 3373.3998828113113, 3413.4952530120481, 3454.4490330927388, 496.195038, 496.20001220703125, 496.204962, 35659353.614476241, 35659353, 1149440, 1145857.7611069249, TRUE, 0.024601030353423384, 27.918253012047899, 1.0081407426394933, 1.0143315197994494)
peakTable3[,c(1,12)] <- sapply(peakTable3[,c(1,12)], as.logical)
peakTable3[,c(2:11, 13:16)] <- sapply(peakTable3[,c(2:11,13:16)], as.numeric)
input_peakTables <- list(peakTable1, peakTable2, peakTable3)
# peakFit
# 1
cFit1.1 <- list(amplitude=162404.8057918259, center=3341.888, sigma=0.078786133031045896, gamma=0.0018336101984172684, fitStatus=2, curveModel="skewedGaussian")
class(cFit1.1) <- 'peakPantheR_curveFit'
cFit1.2 <- list(amplitude=199249.10572753669, center=3382.577, sigma=0.074904415304607966, gamma=0.0011471899372353885, fitStatus=2, curveModel="skewedGaussian")
class(cFit1.2) <- 'peakPantheR_curveFit'
# 2
cFit2.1 <- list(amplitude=124090.83425474487, center=3359.102, sigma=0.071061541060964212, gamma=0.0018336072657203239, fitStatus=2, curveModel="skewedGaussian")
class(cFit2.1) <- 'peakPantheR_curveFit'
cFit2.2 <- list(amplitude=151407.23415130575, center=3399.791, sigma=0.063753866057052563, gamma=0.001676782834598999, fitStatus=2, curveModel="skewedGaussian")
class(cFit2.2) <- 'peakPantheR_curveFit'
# 3
cFit3.1 <- list(amplitude=122363.51256736703, center=3362.233, sigma=0.075489598945304492, gamma=0.0025160536725299734, fitStatus=2, curveModel="skewedGaussian")
class(cFit3.1) <- 'peakPantheR_curveFit'
cFit3.2 <- list(amplitude=204749.86097918145, center=3409.182, sigma=0.075731781812843249, gamma=0.0013318670577834328, fitStatus=2, curveModel="skewedGaussian")
class(cFit3.2) <- 'peakPantheR_curveFit'
input_peakFit <- list(list(cFit1.1, cFit1.2), list(cFit2.1, cFit2.2), list(cFit3.1, cFit3.2))
# dataPoint
tmp_raw_data1 <- MSnbase::readMSData(input_spectraPaths[1], centroided=TRUE, mode='onDisk')
ROIDataPoints1 <- extractSignalRawData(tmp_raw_data1, rt=input_targetFeatTable[,c('rtMin','rtMax')], mz=input_targetFeatTable[,c('mzMin','mzMax')], verbose=FALSE)
tmp_raw_data2 <- MSnbase::readMSData(input_spectraPaths[2], centroided=TRUE, mode='onDisk')
ROIDataPoints2 <- extractSignalRawData(tmp_raw_data2, rt=input_targetFeatTable[,c('rtMin','rtMax')], mz=input_targetFeatTable[,c('mzMin','mzMax')], verbose=FALSE)
tmp_raw_data3 <- MSnbase::readMSData(input_spectraPaths[3], centroided=TRUE, mode='onDisk')
ROIDataPoints3 <- extractSignalRawData(tmp_raw_data3, rt=input_targetFeatTable[,c('rtMin','rtMax')], mz=input_targetFeatTable[,c('mzMin','mzMax')], verbose=FALSE)
input_dataPoints <- list(ROIDataPoints1, ROIDataPoints2, ROIDataPoints3)
# Object, fully filled
filledAnnotation <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable, FIR=input_FIR, uROI=input_uROI, useFIR=TRUE, uROIExist=TRUE, useUROI=TRUE, cpdMetadata=input_cpdMetadata, spectraMetadata=input_spectraMetadata, acquisitionTime=input_acquisitionTime, TIC=input_TIC, peakTables=input_peakTables, dataPoints=input_dataPoints, peakFit=input_peakFit, isAnnotated=TRUE)
# Empty annotation, special case for peakTables
emptyAnnotation <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable)
test_that('accessors return the correct values', {
expected_ROI <- input_targetFeatTable[, c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "cpdID", "cpdName")]
expected_FIR <- cbind.data.frame(input_FIR, cpdID=c("ID-1","ID-2"), cpdName=c("Cpd 1", "Cpd 2"), stringsAsFactors=FALSE)
expected_uROI <- cbind.data.frame(input_uROI, cpdID=c("ID-1","ID-2"), cpdName=c("Cpd 1", "Cpd 2"), stringsAsFactors=FALSE)
expected_acquisitionTime <- as.POSIXct(input_acquisitionTime)
expected_peakTables <- list(cbind.data.frame(peakTable1, cpdID=c("ID-1","ID-2"), cpdName=c("Cpd 1","Cpd 2"), stringsAsFactors=FALSE), cbind.data.frame(peakTable2, cpdID=c("ID-1","ID-2"), cpdName=c("Cpd 1","Cpd 2"), stringsAsFactors=FALSE), cbind.data.frame(peakTable3, cpdID=c("ID-1","ID-2"), cpdName=c("Cpd 1","Cpd 2"), stringsAsFactors=FALSE))
expected_dataPoints <- input_dataPoints
expected_peakFit <- input_peakFit
expected_filename <- c("ko15", "ko16", "ko18")
# Check accessors
# Basic slots
# cpdID
expect_equal(cpdID(filledAnnotation), c("ID-1","ID-2"))
# cpdName
expect_equal(cpdName(filledAnnotation), c("Cpd 1", "Cpd 2"))
# ROI
expect_equal(ROI(filledAnnotation), expected_ROI)
# FIR
expect_equal(FIR(filledAnnotation), expected_FIR)
# uROI
expect_equal(uROI(filledAnnotation), expected_uROI)
# filepath
expect_equal(filepath(filledAnnotation), input_spectraPaths)
# cpdMetadata
expect_equal(cpdMetadata(filledAnnotation), input_cpdMetadata)
# spectraMetadata
expect_equal(spectraMetadata(filledAnnotation), input_spectraMetadata)
# acquisitionTIme
expect_equal(acquisitionTime(filledAnnotation), expected_acquisitionTime)
# uROIExist
expect_true(uROIExist(filledAnnotation))
# useUROI
expect_true(useUROI(filledAnnotation))
# useFIR
expect_true(useFIR(filledAnnotation))
# TIC
expect_equal(TIC(filledAnnotation), input_TIC)
# peakTables
expect_equal(peakTables(filledAnnotation), expected_peakTables)
# dataPoints
expect_equal(dataPoints(filledAnnotation), expected_dataPoints)
# peakFit
expect_equal(peakFit(filledAnnotation), expected_peakFit)
# isAnnotated
expect_true(isAnnotated(filledAnnotation))
# nbSamples
expect_equal(nbSamples(filledAnnotation), 3)
# nbCompounds
expect_equal(nbCompounds(filledAnnotation), 2)
# filename
expect_equal(filename(filledAnnotation), expected_filename)
# annotationTable
# simple value
expected_annotationTable <- data.frame(matrix(c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), 3, 2), stringsAsFactors=FALSE)
rownames(expected_annotationTable) <- input_spectraPaths
colnames(expected_annotationTable) <- c("ID-1", "ID-2")
expect_equal(annotationTable(filledAnnotation, 'found'), expected_annotationTable)
# no sample
tmp_noSample <- peakPantheRAnnotation()
expected_noSample <- data.frame(matrix(vector(), 0, 0), stringsAsFactors=FALSE)
rownames(expected_noSample) <- tmp_noSample@filepath
colnames(expected_noSample) <- tmp_noSample@cpdID
expect_equal(annotationTable(tmp_noSample, 'found'), expected_noSample)
# no peakTables (not annotated or no compounds)
tmp_noPeakTables <- filledAnnotation
tmp_noPeakTables@peakTables <- vector("list", 3)
expected_noPeakTables <- data.frame(matrix(vector(), 3, 2), stringsAsFactors=FALSE)
rownames(expected_noPeakTables) <- tmp_noPeakTables@filepath
colnames(expected_noPeakTables) <- tmp_noPeakTables@cpdID
expect_equal(annotationTable(tmp_noPeakTables, 'found'), expected_noPeakTables)
# only 1 compounds (sapply simplify to vector and not matrix)
tmp_singleCpd <- filledAnnotation[,1]
expected_mz <- data.frame(matrix(c(522.20001220703125, 522.20001220703125, 522.20001220703125), 3, 1), stringsAsFactors=FALSE)
rownames(expected_mz) <- filledAnnotation@filepath
colnames(expected_mz) <- filledAnnotation@cpdID[1]
expect_equal(annotationTable(tmp_singleCpd, 'mz'), expected_mz)
# raise error if column doesn't exist
expect_error(annotationTable(filledAnnotation, 'notAnExistingColumn'), 'input column is not a column of peakTables', fixed=TRUE)
## try some more of the peakTable columns
# mz
expected_mz <- data.frame(matrix(c(522.20001220703125, 522.20001220703125, 522.20001220703125, 496.20001220703125, 496.20001220703125, 496.20001220703125), 3, 2), stringsAsFactors=FALSE)
rownames(expected_mz) <- filledAnnotation@filepath
colnames(expected_mz) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'mz'), expected_mz)
# mzMin
expected_mzMin <- data.frame(matrix(c(522.194778, 522.194778, 522.194778, 496.20001220703125, 496.195038, 496.195038), 3, 2), stringsAsFactors=FALSE)
rownames(expected_mzMin) <- filledAnnotation@filepath
colnames(expected_mzMin) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'mzMin'), expected_mzMin)
# mzMax
expected_mzMax <- data.frame(matrix(c(522.205222, 522.205222, 522.205222, 496.20001220703125, 496.204962, 496.204962), 3, 2), stringsAsFactors=FALSE)
rownames(expected_mzMax) <- filledAnnotation@filepath
colnames(expected_mzMax) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'mzMax'), expected_mzMax)
# rt
expected_rt <- data.frame(matrix(c(3346.8277590361445, 3365.102, 3368.233, 3386.5288072289159, 3405.791, 3413.4952530120481), 3, 2), stringsAsFactors=FALSE)
rownames(expected_rt) <- filledAnnotation@filepath
colnames(expected_rt) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'rt'), expected_rt)
# rtMin
expected_rtMin <- data.frame(matrix(c(3309.7589296586070, 3326.1063495851854, 3333.8625894557053, 3345.3766648628907, 3365.0238566258713, 3373.3998828113113), 3, 2), stringsAsFactors=FALSE)
rownames(expected_rtMin) <- filledAnnotation@filepath
colnames(expected_rtMin) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'rtMin'), expected_rtMin)
# rtMax
expected_rtMax <- data.frame(matrix(c(3385.4098874628098, 3407.2726475892355, 3407.4362838927614, 3428.2788374983961, 3453.4049569205681, 3454.4490330927388), 3, 2), stringsAsFactors=FALSE)
rownames(expected_rtMax) <- filledAnnotation@filepath
colnames(expected_rtMax) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'rtMax'), expected_rtMax)
# peakArea
expected_peakArea <- data.frame(matrix(c(26133726.6811244078, 24545301.622835573, 21447174.404490683, 35472141.3330242932, 37207579.286265120, 35659353.614476241), 3, 2), stringsAsFactors=FALSE)
rownames(expected_peakArea) <- filledAnnotation@filepath
colnames(expected_peakArea) <- filledAnnotation@cpdID
expect_equal(annotationTable(filledAnnotation, 'peakArea'), expected_peakArea)
# EICs
# default (sum)
tmp_EIC_sum_1.1 <- generateIonChromatogram(input_dataPoints[[1]][[1]], aggregationFunction='sum')
tmp_EIC_sum_1.2 <- generateIonChromatogram(input_dataPoints[[1]][[2]], aggregationFunction='sum')
tmp_EIC_sum_2.1 <- generateIonChromatogram(input_dataPoints[[2]][[1]], aggregationFunction='sum')
tmp_EIC_sum_2.2 <- generateIonChromatogram(input_dataPoints[[2]][[2]], aggregationFunction='sum')
tmp_EIC_sum_3.1 <- generateIonChromatogram(input_dataPoints[[3]][[1]], aggregationFunction='sum')
tmp_EIC_sum_3.2 <- generateIonChromatogram(input_dataPoints[[3]][[2]], aggregationFunction='sum')
expected_EIC_sum <- list(list(tmp_EIC_sum_1.1, tmp_EIC_sum_1.2),
list(tmp_EIC_sum_2.1, tmp_EIC_sum_2.2),
list(tmp_EIC_sum_3.1, tmp_EIC_sum_3.2))
expect_equal(EICs(filledAnnotation), expected_EIC_sum)
# change aggregationFunction (max)
tmp_EIC_max_1.1 <- generateIonChromatogram(input_dataPoints[[1]][[1]], aggregationFunction='max')
tmp_EIC_max_1.2 <- generateIonChromatogram(input_dataPoints[[1]][[2]], aggregationFunction='max')
tmp_EIC_max_2.1 <- generateIonChromatogram(input_dataPoints[[2]][[1]], aggregationFunction='max')
tmp_EIC_max_2.2 <- generateIonChromatogram(input_dataPoints[[2]][[2]], aggregationFunction='max')
tmp_EIC_max_3.1 <- generateIonChromatogram(input_dataPoints[[3]][[1]], aggregationFunction='max')
tmp_EIC_max_3.2 <- generateIonChromatogram(input_dataPoints[[3]][[2]], aggregationFunction='max')
expected_EIC_max <- list(list(tmp_EIC_max_1.1, tmp_EIC_max_1.2),
list(tmp_EIC_max_2.1, tmp_EIC_max_2.2),
list(tmp_EIC_max_3.1, tmp_EIC_max_3.2))
expect_equal(EICs(filledAnnotation, 'max'), expected_EIC_max)
# Special peakTables behaviour when peakTable is NULL
expect_equal(peakTables(emptyAnnotation), list(NULL, NULL, NULL))
})
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