tests/testthat/test_peakPantheRAnnotation_extract-method.R
In phenomecentre/peakPantheR: Peak Picking and Annotation of High Resolution Experiments
context('peakPantheRAnnotation_extract-method()')
## Test the extract sub-setting "[" method
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)
test_that('no i j input returns an untouched object', {
## Expected values
expected_ROI <- input_targetFeatTable[, c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax")]
expected_peakTables <- input_peakTables
expected_dataPoints <- input_dataPoints
expected_peakFit <- input_peakFit
# no sub-setting
noChange <- filledAnnotation[]
# whole object
expect_equal(noChange, filledAnnotation)
# cpdID
expect_equal(noChange@cpdID, c("ID-1", "ID-2"))
# cpdName
expect_equal(noChange@cpdName, c("Cpd 1", "Cpd 2"))
# ROI
expect_equal(noChange@ROI, expected_ROI)
# FIR
expect_equal(noChange@FIR, input_FIR)
# uROI
expect_equal(noChange@uROI, input_uROI)
# filepath
expect_equal(noChange@filepath, input_spectraPaths)
# cpdMetadata
expect_equal(noChange@cpdMetadata, input_cpdMetadata)
# spectraMetadata
expect_equal(noChange@spectraMetadata, input_spectraMetadata)
# acquisitionTime
expect_equal(noChange@acquisitionTime, input_acquisitionTime)
# uROIExist
expect_true(noChange@uROIExist)
# useUROI
expect_true(noChange@useUROI)
# useFIR
expect_true(noChange@useFIR)
# TIC
expect_equal(noChange@TIC, input_TIC)
# peakTables
expect_equal(noChange@peakTables, expected_peakTables)
# dataPoints
expect_equal(noChange@dataPoints, expected_dataPoints)
# peakFit
expect_equal(noChange@peakFit, expected_peakFit)
# isAnnotated
expect_true(noChange@isAnnotated)
})
test_that('missing i, set j', {
## i will default to all samples, set j
## Expected values
expected_ROI <- input_targetFeatTable[1, c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax")]
expected_FIR <- input_FIR[1,]
expected_uROI <- input_uROI[1,]
expected_cpdMetadata <- input_cpdMetadata[1,]
expected_peakTables <- list(peakTable1[1,], peakTable2[1,], peakTable3[1,])
expected_dataPoints <- list(list(input_dataPoints[[1]][[1]]), list(input_dataPoints[[2]][[1]]), list(input_dataPoints[[3]][[1]]))
expected_peakFit <- list(list(input_peakFit[[1]][[1]]), list(input_peakFit[[2]][[1]]), list(input_peakFit[[3]][[1]]))
# no sub-setting
setJ <- filledAnnotation[,1]
# cpdID
expect_equal(setJ@cpdID, c("ID-1"))
# cpdName
expect_equal(setJ@cpdName, c("Cpd 1"))
# ROI
expect_equal(setJ@ROI, expected_ROI)
# FIR
expect_equal(setJ@FIR, expected_FIR)
# uROI
expect_equal(setJ@uROI, expected_uROI)
# filepath
expect_equal(setJ@filepath, input_spectraPaths)
# cpdMetadata
expect_equal(setJ@cpdMetadata, expected_cpdMetadata)
# spectraMetadata
expect_equal(setJ@spectraMetadata, input_spectraMetadata)
# acquisitionTime
expect_equal(setJ@acquisitionTime, input_acquisitionTime)
# uROIExist
expect_true(setJ@uROIExist)
# useUROI
expect_true(setJ@useUROI)
# useFIR
expect_true(setJ@useFIR)
# TIC
expect_equal(setJ@TIC, input_TIC)
# peakTables
expect_equal(setJ@peakTables, expected_peakTables)
# dataPoints
expect_equal(setJ@dataPoints, expected_dataPoints)
# peakFit
expect_equal(setJ@peakFit, expected_peakFit)
# isAnnotated
expect_true(setJ@isAnnotated)
})
test_that('set i, missing j', {
## set i, j will default to all compounds
## Expected values
expected_ROI <- input_targetFeatTable[, c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax")]
expected_FIR <- input_FIR
expected_uROI <- input_uROI
expected_filepath <- input_spectraPaths[1:2]
expected_spectraMetadata <- input_spectraMetadata[1:2,]
expected_acquisitionTime <- input_acquisitionTime[1:2]
expected_TIC <- input_TIC[1:2]
expected_peakTables <- list(peakTable1, peakTable2)
expected_dataPoints <- input_dataPoints[1:2]
expected_peakFit <- input_peakFit[1:2]
# no sub-setting
setI <- filledAnnotation[1:2,]
# cpdID
expect_equal(setI@cpdID, c("ID-1", "ID-2"))
# cpdName
expect_equal(setI@cpdName, c("Cpd 1", "Cpd 2"))
# ROI
expect_equal(setI@ROI, expected_ROI)
# FIR
expect_equal(setI@FIR, expected_FIR)
# uROI
expect_equal(setI@uROI, expected_uROI)
# filepath
expect_equal(setI@filepath, expected_filepath)
# cpdMetadata
expect_equal(setI@cpdMetadata, input_cpdMetadata)
# spectraMetadata
expect_equal(setI@spectraMetadata, expected_spectraMetadata)
# acquisitionTime
expect_equal(setI@acquisitionTime, expected_acquisitionTime)
# uROIExist
expect_true(setI@uROIExist)
# useUROI
expect_true(setI@useUROI)
# useFIR
expect_true(setI@useFIR)
# TIC
expect_equal(setI@TIC, expected_TIC)
# peakTables
expect_equal(setI@peakTables, expected_peakTables)
# dataPoints
expect_equal(setI@dataPoints, expected_dataPoints)
# peakFit
expect_equal(setI@peakFit, expected_peakFit)
# isAnnotated
expect_true(setI@isAnnotated)
})
test_that('set i, empty peakTables, dataPoints and peakFit', {
## peakTables, dataPoints and peakFit are all NULL for the selected samples, trigger a special case
## object with cpd and spectra set
defaultInit_cpd_spectra <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable, cpdMetadata=input_cpdMetadata, spectraMetadata=input_spectraMetadata, isAnnotated=TRUE)
## Expected values
expected_ROI <- input_targetFeatTable[, c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax")]
expected_FIR <- data.frame(matrix(vector(), 2, 4, dimnames=list(c(), c("rtMin", "rtMax", "mzMin", "mzMax"))), stringsAsFactors=FALSE)
expected_FIR[1,] <- sapply(expected_FIR[1,], as.numeric)
expected_FIR[2,] <- sapply(expected_FIR[2,], as.numeric)
expected_uROI <- data.frame(matrix(vector(), 2, 6, dimnames=list(c(), c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax"))), stringsAsFactors=FALSE)
expected_uROI[1,] <- sapply(expected_uROI[1,], as.numeric)
expected_uROI[2,] <- sapply(expected_uROI[2,], as.numeric)
expected_filepath <- input_spectraPaths[1:2]
expected_spectraMetadata <- input_spectraMetadata[1:2,]
expected_acquisitionTime <- as.character(c(NA, NA))
expected_peakTables <- vector("list", 2)
expected_dataPoints <- vector("list", 2)
expected_peakFit <- vector("list", 2)
# subset samples
setIandNULL <- defaultInit_cpd_spectra[1:2,]
# cpdID
expect_equal(setIandNULL@cpdID, c("ID-1", "ID-2"))
# cpdName
expect_equal(setIandNULL@cpdName, c("Cpd 1", "Cpd 2"))
# ROI
expect_equal(setIandNULL@ROI, expected_ROI)
# FIR
expect_equal(setIandNULL@FIR, expected_FIR)
# uROI
expect_equal(setIandNULL@uROI, expected_uROI)
# filepath
expect_equal(setIandNULL@filepath, expected_filepath)
# cpdMetadata
expect_equal(setIandNULL@cpdMetadata, input_cpdMetadata)
# spectraMetadata
expect_equal(setIandNULL@spectraMetadata, expected_spectraMetadata)
# acquisitionTime
expect_equal(setIandNULL@acquisitionTime, expected_acquisitionTime)
# uROIExist
expect_false(setIandNULL@uROIExist)
# useUROI
expect_false(setIandNULL@useUROI)
# useFIR
expect_false(setIandNULL@useFIR)
# TIC
expect_equal(setIandNULL@TIC, c(as.numeric(NA), as.numeric(NA)))
# peakTables
expect_equal(setIandNULL@peakTables, expected_peakTables)
# dataPoints
expect_equal(setIandNULL@dataPoints, expected_dataPoints)
# peakFit
expect_equal(setIandNULL@peakFit, expected_peakFit)
# isAnnotated
expect_true(setIandNULL@isAnnotated)
})
test_that('set i and j', {
## set i and j
## Expected values
expected_ROI <- input_targetFeatTable[1, c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax")]
expected_FIR <- input_FIR[1,]
expected_uROI <- input_uROI[1,]
expected_filepath <- input_spectraPaths[1:2]
expected_cpdMetadata <- input_cpdMetadata[1,]
expected_spectraMetadata <- input_spectraMetadata[1:2,]
expected_acquisitionTime <- input_acquisitionTime[1:2]
expected_TIC <- input_TIC[1:2]
expected_peakTables <- list(peakTable1[1,], peakTable2[1,])
expected_dataPoints <- list(input_dataPoints[[1]][1], input_dataPoints[[2]][1])
expected_peakFit <- list(input_peakFit[[1]][1], input_peakFit[[2]][1])
# no sub-setting
setIJ <- filledAnnotation[1:2,1]
# cpdID
expect_equal(setIJ@cpdID, c("ID-1"))
# cpdName
expect_equal(setIJ@cpdName, c("Cpd 1"))
# ROI
expect_equal(setIJ@ROI, expected_ROI)
# FIR
expect_equal(setIJ@FIR, expected_FIR)
# uROI
expect_equal(setIJ@uROI, expected_uROI)
# filepath
expect_equal(setIJ@filepath, expected_filepath)
# cpdMetadata
expect_equal(setIJ@cpdMetadata, expected_cpdMetadata)
# spectraMetadata
expect_equal(setIJ@spectraMetadata, expected_spectraMetadata)
# acquisitionTime
expect_equal(setIJ@acquisitionTime, expected_acquisitionTime)
# uROIExist
expect_true(setIJ@uROIExist)
# useUROI
expect_true(setIJ@useUROI)
# useFIR
expect_true(setIJ@useFIR)
# TIC
expect_equal(setIJ@TIC, expected_TIC)
# peakTables
expect_equal(setIJ@peakTables, expected_peakTables)
# dataPoints
expect_equal(setIJ@dataPoints, expected_dataPoints)
# peakFit
expect_equal(setIJ@peakFit, expected_peakFit)
# isAnnotated
expect_true(setIJ@isAnnotated)
})
test_that('reorder i and j', {
## reorder i and j
## Expected values
expected_ROI <- input_targetFeatTable[c(2,1), c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax")]
expected_FIR <- input_FIR[c(2,1),]
expected_uROI <- input_uROI[c(2,1),]
expected_filepath <- input_spectraPaths[c(3,2,1)]
expected_acquisitionTime <- input_acquisitionTime[c(3,2,1)]
expected_cpdMetadata <- input_cpdMetadata[c(2,1),]
expected_spectraMetadata <- input_spectraMetadata[c(3,2,1),]
expected_TIC <- input_TIC[c(3,2,1)]
expected_peakTables <- list(peakTable3[c(2,1),], peakTable2[c(2,1),], peakTable1[c(2,1),])
expected_dataPoints <- list(input_dataPoints[[3]][c(2,1)], input_dataPoints[[2]][c(2,1)], input_dataPoints[[1]][c(2,1)])
expected_peakFit <- list(input_peakFit[[3]][c(2,1)], input_peakFit[[2]][c(2,1)], input_peakFit[[1]][c(2,1)])
# no sub-setting
reorderIJ <- filledAnnotation[3:1, c(2,1)]
# cpdID
expect_equal(reorderIJ@cpdID, c("ID-2", "ID-1"))
# cpdName
expect_equal(reorderIJ@cpdName, c("Cpd 2", "Cpd 1"))
# ROI
expect_equal(reorderIJ@ROI, expected_ROI)
# FIR
expect_equal(reorderIJ@FIR, expected_FIR)
# uROI
expect_equal(reorderIJ@uROI, expected_uROI)
# filepath
expect_equal(reorderIJ@filepath, expected_filepath)
# cpdMetadata
expect_equal(reorderIJ@cpdMetadata, expected_cpdMetadata)
# spectraMetadata
expect_equal(reorderIJ@spectraMetadata, expected_spectraMetadata)
# acquisitionTime
expect_equal(reorderIJ@acquisitionTime, expected_acquisitionTime)
# uROIExist
expect_true(reorderIJ@uROIExist)
# useUROI
expect_true(reorderIJ@useUROI)
# useFIR
expect_true(reorderIJ@useFIR)
# TIC
expect_equal(reorderIJ@TIC, expected_TIC)
# peakTables
expect_equal(reorderIJ@peakTables, expected_peakTables)
# dataPoints
expect_equal(reorderIJ@dataPoints, expected_dataPoints)
# peakFit
expect_equal(reorderIJ@peakFit, expected_peakFit)
# isAnnotated
expect_true(reorderIJ@isAnnotated)
})
test_that('raise error if i and j are out of bound', {
# i out of bound
msgI <- paste('i index out of bound: maximum 3', sep='')
expect_error(filledAnnotation[4,], msgI, fixed=TRUE)
expect_error(filledAnnotation[10:15,], msgI, fixed=TRUE)
# j out of bound
msgJ <- paste('j index out of bound: maximum 2', sep='')
expect_error(filledAnnotation[,4], msgJ, fixed=TRUE)
expect_error(filledAnnotation[,10:15], msgJ, fixed=TRUE)
})
phenomecentre/peakPantheR documentation built on Feb. 29, 2024, 9:07 p.m.
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context('peakPantheRAnnotation_extract-method()')
## Test the extract sub-setting "[" method
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)
test_that('no i j input returns an untouched object', {
## Expected values
expected_ROI <- input_targetFeatTable[, c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax")]
expected_peakTables <- input_peakTables
expected_dataPoints <- input_dataPoints
expected_peakFit <- input_peakFit
# no sub-setting
noChange <- filledAnnotation[]
# whole object
expect_equal(noChange, filledAnnotation)
# cpdID
expect_equal(noChange@cpdID, c("ID-1", "ID-2"))
# cpdName
expect_equal(noChange@cpdName, c("Cpd 1", "Cpd 2"))
# ROI
expect_equal(noChange@ROI, expected_ROI)
# FIR
expect_equal(noChange@FIR, input_FIR)
# uROI
expect_equal(noChange@uROI, input_uROI)
# filepath
expect_equal(noChange@filepath, input_spectraPaths)
# cpdMetadata
expect_equal(noChange@cpdMetadata, input_cpdMetadata)
# spectraMetadata
expect_equal(noChange@spectraMetadata, input_spectraMetadata)
# acquisitionTime
expect_equal(noChange@acquisitionTime, input_acquisitionTime)
# uROIExist
expect_true(noChange@uROIExist)
# useUROI
expect_true(noChange@useUROI)
# useFIR
expect_true(noChange@useFIR)
# TIC
expect_equal(noChange@TIC, input_TIC)
# peakTables
expect_equal(noChange@peakTables, expected_peakTables)
# dataPoints
expect_equal(noChange@dataPoints, expected_dataPoints)
# peakFit
expect_equal(noChange@peakFit, expected_peakFit)
# isAnnotated
expect_true(noChange@isAnnotated)
})
test_that('missing i, set j', {
## i will default to all samples, set j
## Expected values
expected_ROI <- input_targetFeatTable[1, c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax")]
expected_FIR <- input_FIR[1,]
expected_uROI <- input_uROI[1,]
expected_cpdMetadata <- input_cpdMetadata[1,]
expected_peakTables <- list(peakTable1[1,], peakTable2[1,], peakTable3[1,])
expected_dataPoints <- list(list(input_dataPoints[[1]][[1]]), list(input_dataPoints[[2]][[1]]), list(input_dataPoints[[3]][[1]]))
expected_peakFit <- list(list(input_peakFit[[1]][[1]]), list(input_peakFit[[2]][[1]]), list(input_peakFit[[3]][[1]]))
# no sub-setting
setJ <- filledAnnotation[,1]
# cpdID
expect_equal(setJ@cpdID, c("ID-1"))
# cpdName
expect_equal(setJ@cpdName, c("Cpd 1"))
# ROI
expect_equal(setJ@ROI, expected_ROI)
# FIR
expect_equal(setJ@FIR, expected_FIR)
# uROI
expect_equal(setJ@uROI, expected_uROI)
# filepath
expect_equal(setJ@filepath, input_spectraPaths)
# cpdMetadata
expect_equal(setJ@cpdMetadata, expected_cpdMetadata)
# spectraMetadata
expect_equal(setJ@spectraMetadata, input_spectraMetadata)
# acquisitionTime
expect_equal(setJ@acquisitionTime, input_acquisitionTime)
# uROIExist
expect_true(setJ@uROIExist)
# useUROI
expect_true(setJ@useUROI)
# useFIR
expect_true(setJ@useFIR)
# TIC
expect_equal(setJ@TIC, input_TIC)
# peakTables
expect_equal(setJ@peakTables, expected_peakTables)
# dataPoints
expect_equal(setJ@dataPoints, expected_dataPoints)
# peakFit
expect_equal(setJ@peakFit, expected_peakFit)
# isAnnotated
expect_true(setJ@isAnnotated)
})
test_that('set i, missing j', {
## set i, j will default to all compounds
## Expected values
expected_ROI <- input_targetFeatTable[, c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax")]
expected_FIR <- input_FIR
expected_uROI <- input_uROI
expected_filepath <- input_spectraPaths[1:2]
expected_spectraMetadata <- input_spectraMetadata[1:2,]
expected_acquisitionTime <- input_acquisitionTime[1:2]
expected_TIC <- input_TIC[1:2]
expected_peakTables <- list(peakTable1, peakTable2)
expected_dataPoints <- input_dataPoints[1:2]
expected_peakFit <- input_peakFit[1:2]
# no sub-setting
setI <- filledAnnotation[1:2,]
# cpdID
expect_equal(setI@cpdID, c("ID-1", "ID-2"))
# cpdName
expect_equal(setI@cpdName, c("Cpd 1", "Cpd 2"))
# ROI
expect_equal(setI@ROI, expected_ROI)
# FIR
expect_equal(setI@FIR, expected_FIR)
# uROI
expect_equal(setI@uROI, expected_uROI)
# filepath
expect_equal(setI@filepath, expected_filepath)
# cpdMetadata
expect_equal(setI@cpdMetadata, input_cpdMetadata)
# spectraMetadata
expect_equal(setI@spectraMetadata, expected_spectraMetadata)
# acquisitionTime
expect_equal(setI@acquisitionTime, expected_acquisitionTime)
# uROIExist
expect_true(setI@uROIExist)
# useUROI
expect_true(setI@useUROI)
# useFIR
expect_true(setI@useFIR)
# TIC
expect_equal(setI@TIC, expected_TIC)
# peakTables
expect_equal(setI@peakTables, expected_peakTables)
# dataPoints
expect_equal(setI@dataPoints, expected_dataPoints)
# peakFit
expect_equal(setI@peakFit, expected_peakFit)
# isAnnotated
expect_true(setI@isAnnotated)
})
test_that('set i, empty peakTables, dataPoints and peakFit', {
## peakTables, dataPoints and peakFit are all NULL for the selected samples, trigger a special case
## object with cpd and spectra set
defaultInit_cpd_spectra <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable, cpdMetadata=input_cpdMetadata, spectraMetadata=input_spectraMetadata, isAnnotated=TRUE)
## Expected values
expected_ROI <- input_targetFeatTable[, c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax")]
expected_FIR <- data.frame(matrix(vector(), 2, 4, dimnames=list(c(), c("rtMin", "rtMax", "mzMin", "mzMax"))), stringsAsFactors=FALSE)
expected_FIR[1,] <- sapply(expected_FIR[1,], as.numeric)
expected_FIR[2,] <- sapply(expected_FIR[2,], as.numeric)
expected_uROI <- data.frame(matrix(vector(), 2, 6, dimnames=list(c(), c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax"))), stringsAsFactors=FALSE)
expected_uROI[1,] <- sapply(expected_uROI[1,], as.numeric)
expected_uROI[2,] <- sapply(expected_uROI[2,], as.numeric)
expected_filepath <- input_spectraPaths[1:2]
expected_spectraMetadata <- input_spectraMetadata[1:2,]
expected_acquisitionTime <- as.character(c(NA, NA))
expected_peakTables <- vector("list", 2)
expected_dataPoints <- vector("list", 2)
expected_peakFit <- vector("list", 2)
# subset samples
setIandNULL <- defaultInit_cpd_spectra[1:2,]
# cpdID
expect_equal(setIandNULL@cpdID, c("ID-1", "ID-2"))
# cpdName
expect_equal(setIandNULL@cpdName, c("Cpd 1", "Cpd 2"))
# ROI
expect_equal(setIandNULL@ROI, expected_ROI)
# FIR
expect_equal(setIandNULL@FIR, expected_FIR)
# uROI
expect_equal(setIandNULL@uROI, expected_uROI)
# filepath
expect_equal(setIandNULL@filepath, expected_filepath)
# cpdMetadata
expect_equal(setIandNULL@cpdMetadata, input_cpdMetadata)
# spectraMetadata
expect_equal(setIandNULL@spectraMetadata, expected_spectraMetadata)
# acquisitionTime
expect_equal(setIandNULL@acquisitionTime, expected_acquisitionTime)
# uROIExist
expect_false(setIandNULL@uROIExist)
# useUROI
expect_false(setIandNULL@useUROI)
# useFIR
expect_false(setIandNULL@useFIR)
# TIC
expect_equal(setIandNULL@TIC, c(as.numeric(NA), as.numeric(NA)))
# peakTables
expect_equal(setIandNULL@peakTables, expected_peakTables)
# dataPoints
expect_equal(setIandNULL@dataPoints, expected_dataPoints)
# peakFit
expect_equal(setIandNULL@peakFit, expected_peakFit)
# isAnnotated
expect_true(setIandNULL@isAnnotated)
})
test_that('set i and j', {
## set i and j
## Expected values
expected_ROI <- input_targetFeatTable[1, c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax")]
expected_FIR <- input_FIR[1,]
expected_uROI <- input_uROI[1,]
expected_filepath <- input_spectraPaths[1:2]
expected_cpdMetadata <- input_cpdMetadata[1,]
expected_spectraMetadata <- input_spectraMetadata[1:2,]
expected_acquisitionTime <- input_acquisitionTime[1:2]
expected_TIC <- input_TIC[1:2]
expected_peakTables <- list(peakTable1[1,], peakTable2[1,])
expected_dataPoints <- list(input_dataPoints[[1]][1], input_dataPoints[[2]][1])
expected_peakFit <- list(input_peakFit[[1]][1], input_peakFit[[2]][1])
# no sub-setting
setIJ <- filledAnnotation[1:2,1]
# cpdID
expect_equal(setIJ@cpdID, c("ID-1"))
# cpdName
expect_equal(setIJ@cpdName, c("Cpd 1"))
# ROI
expect_equal(setIJ@ROI, expected_ROI)
# FIR
expect_equal(setIJ@FIR, expected_FIR)
# uROI
expect_equal(setIJ@uROI, expected_uROI)
# filepath
expect_equal(setIJ@filepath, expected_filepath)
# cpdMetadata
expect_equal(setIJ@cpdMetadata, expected_cpdMetadata)
# spectraMetadata
expect_equal(setIJ@spectraMetadata, expected_spectraMetadata)
# acquisitionTime
expect_equal(setIJ@acquisitionTime, expected_acquisitionTime)
# uROIExist
expect_true(setIJ@uROIExist)
# useUROI
expect_true(setIJ@useUROI)
# useFIR
expect_true(setIJ@useFIR)
# TIC
expect_equal(setIJ@TIC, expected_TIC)
# peakTables
expect_equal(setIJ@peakTables, expected_peakTables)
# dataPoints
expect_equal(setIJ@dataPoints, expected_dataPoints)
# peakFit
expect_equal(setIJ@peakFit, expected_peakFit)
# isAnnotated
expect_true(setIJ@isAnnotated)
})
test_that('reorder i and j', {
## reorder i and j
## Expected values
expected_ROI <- input_targetFeatTable[c(2,1), c("rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax")]
expected_FIR <- input_FIR[c(2,1),]
expected_uROI <- input_uROI[c(2,1),]
expected_filepath <- input_spectraPaths[c(3,2,1)]
expected_acquisitionTime <- input_acquisitionTime[c(3,2,1)]
expected_cpdMetadata <- input_cpdMetadata[c(2,1),]
expected_spectraMetadata <- input_spectraMetadata[c(3,2,1),]
expected_TIC <- input_TIC[c(3,2,1)]
expected_peakTables <- list(peakTable3[c(2,1),], peakTable2[c(2,1),], peakTable1[c(2,1),])
expected_dataPoints <- list(input_dataPoints[[3]][c(2,1)], input_dataPoints[[2]][c(2,1)], input_dataPoints[[1]][c(2,1)])
expected_peakFit <- list(input_peakFit[[3]][c(2,1)], input_peakFit[[2]][c(2,1)], input_peakFit[[1]][c(2,1)])
# no sub-setting
reorderIJ <- filledAnnotation[3:1, c(2,1)]
# cpdID
expect_equal(reorderIJ@cpdID, c("ID-2", "ID-1"))
# cpdName
expect_equal(reorderIJ@cpdName, c("Cpd 2", "Cpd 1"))
# ROI
expect_equal(reorderIJ@ROI, expected_ROI)
# FIR
expect_equal(reorderIJ@FIR, expected_FIR)
# uROI
expect_equal(reorderIJ@uROI, expected_uROI)
# filepath
expect_equal(reorderIJ@filepath, expected_filepath)
# cpdMetadata
expect_equal(reorderIJ@cpdMetadata, expected_cpdMetadata)
# spectraMetadata
expect_equal(reorderIJ@spectraMetadata, expected_spectraMetadata)
# acquisitionTime
expect_equal(reorderIJ@acquisitionTime, expected_acquisitionTime)
# uROIExist
expect_true(reorderIJ@uROIExist)
# useUROI
expect_true(reorderIJ@useUROI)
# useFIR
expect_true(reorderIJ@useFIR)
# TIC
expect_equal(reorderIJ@TIC, expected_TIC)
# peakTables
expect_equal(reorderIJ@peakTables, expected_peakTables)
# dataPoints
expect_equal(reorderIJ@dataPoints, expected_dataPoints)
# peakFit
expect_equal(reorderIJ@peakFit, expected_peakFit)
# isAnnotated
expect_true(reorderIJ@isAnnotated)
})
test_that('raise error if i and j are out of bound', {
# i out of bound
msgI <- paste('i index out of bound: maximum 3', sep='')
expect_error(filledAnnotation[4,], msgI, fixed=TRUE)
expect_error(filledAnnotation[10:15,], msgI, fixed=TRUE)
# j out of bound
msgJ <- paste('j index out of bound: maximum 2', sep='')
expect_error(filledAnnotation[,4], msgJ, fixed=TRUE)
expect_error(filledAnnotation[,10:15], msgJ, fixed=TRUE)
})
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