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tests/testthat/test_peakPantheRAnnotation_outputAnnotationResult-method.R
In peakPantheR: Peak Picking and Annotation of High Resolution Experiments
context('peakPantheRAnnotation_outputAnnotationResult()')
## Test the CSV output of all annotation results
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, 15, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "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, 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, 1128960, 1113576.69008227298, FALSE, 0.024601030353423384, 0.95180722891564074, 1.0053782620427065, 1.0093180792278085)
peakTable1[,c(1,11)] <- sapply(peakTable1[,c(1,11)], as.logical)
peakTable1[,c(2:10,12:15)] <- sapply(peakTable1[,c(2:10,12:15)], as.numeric)
# 2
peakTable2 <- data.frame(matrix(vector(), 2, 15, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "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, 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, 1099264, 1098720.2929832144, FALSE, 0.024601030353423384, 20.2139999999999, 1.0839602450900523, 1.1717845972583161)
peakTable2[,c(1,11)] <- sapply(peakTable2[,c(1,11)], as.logical)
peakTable2[,c(2:10,12:15)] <- sapply(peakTable2[,c(2:10,12:15)], as.numeric)
# 3
peakTable3 <- data.frame(matrix(vector(), 2, 15, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "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, 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, 1149440, 1145857.7611069249, TRUE, 0.024601030353423384, 27.918253012047899, 1.0081407426394933, 1.0143315197994494)
peakTable3[,c(1,11)] <- sapply(peakTable3[,c(1,11)], as.logical)
peakTable3[,c(2:10,12:15)] <- sapply(peakTable3[,c(2:10,12:15)], 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('csv output with FIR, verbose, no verbose', {
# temporary file
savePath1 <- tempdir()
# input
input_annotation <- filledAnnotation
# expected
expected_path_cpdMeta <- file.path(savePath1, "testProject_cpdMetadata.csv")
expected_path_specMeta <- file.path(savePath1, "testProject_spectraMetadata.csv")
expected_path_summary <- file.path(savePath1, "testProject_summary.csv")
expected_path_asym <- file.path(savePath1, "testProject_asymmetryFactor.csv")
expected_path_found <- file.path(savePath1, "testProject_found.csv")
expected_path_is_filled <- file.path(savePath1, "testProject_is_filled.csv")
expected_path_maxIntMeas <- file.path(savePath1, "testProject_maxIntMeasured.csv")
expected_path_maxIntPred <- file.path(savePath1, "testProject_maxIntPredicted.csv")
expected_path_mz <- file.path(savePath1, "testProject_mz.csv")
expected_path_mzMax <- file.path(savePath1, "testProject_mzMax.csv")
expected_path_mzMin <- file.path(savePath1, "testProject_mzMin.csv")
expected_path_peakArea <- file.path(savePath1, "testProject_peakArea.csv")
expected_path_ppmErr <- file.path(savePath1, "testProject_ppm_error.csv")
expected_path_rt <- file.path(savePath1, "testProject_rt.csv")
expected_path_rtDev <- file.path(savePath1, "testProject_rt_dev_sec.csv")
expected_path_rtMax <- file.path(savePath1, "testProject_rtMax.csv")
expected_path_rtMin <- file.path(savePath1, "testProject_rtMin.csv")
expected_path_tailfact <- file.path(savePath1, "testProject_tailingFactor.csv")
# expected values
expected_cpdMeta <- data.frame(matrix(data=c('ID-1','Cpd 1',3360.0542530120501,522.20001220703102,'a',1,'ID-2','Cpd 2',3396.1599036144598,496.20001220703102,'b',2), nrow=2,ncol=6,dimnames=list(c(), c('cpdID', 'cpdName', 'Retention.Time','m.z','testcol1', 'testcol2')), byrow=TRUE), stringsAsFactors=FALSE)
expected_cpdMeta[,c(3,4,6)] <- apply(expected_cpdMeta[,c(3,4,6)], 2, as.numeric)
expected_specMeta <- data.frame(matrix(nrow=3,ncol=4,dimnames=list(c(), c('acquisitionTime', 'TIC', 'testcol1', 'testcol2')), byrow=TRUE), stringsAsFactors=FALSE) # no filepath
expected_specMeta$acquisitionTime <- input_acquisitionTime
expected_specMeta$TIC <- input_TIC
expected_specMeta$testcol1 <- input_spectraMetadata$testcol1
expected_specMeta$testcol2 <- as.numeric(input_spectraMetadata$testcol2)
expected_summary <- data.frame(matrix(data=c('ID-1 - Cpd 1',1,0,0.02337616,8.499586, 'ID-2 - Cpd 2',0.6666667,0.333333,0.02460103,16.361353), nrow=2,ncol=5,dimnames=list(c(), c('X', 'ratio_peaks_found', 'ratio_peaks_filled', 'ppm_error', 'rt_dev_sec')), byrow=TRUE), stringsAsFactors=FALSE)
expected_summary[,2:5] <- sapply(expected_summary[,2:5], as.numeric)
expected_asym <- annotationTable(input_annotation, column='asymmetryFactor')
expected_asym <- data.frame(X=rownames(expected_asym), ID.1=expected_asym$`ID-1`, ID.2=expected_asym$`ID-2`, stringsAsFactors=FALSE)
expected_found <- annotationTable(input_annotation, column='found')
expected_found <- data.frame(X=rownames(expected_found), ID.1=expected_found$`ID-1`, ID.2=expected_found$`ID-2`, stringsAsFactors=FALSE)
expected_is_filled <- annotationTable(input_annotation, column='is_filled')
expected_is_filled <- data.frame(X=rownames(expected_is_filled), ID.1=expected_is_filled$`ID-1`, ID.2=expected_is_filled$`ID-2`, stringsAsFactors=FALSE)
expected_maxIntMeas <- annotationTable(input_annotation, column='maxIntMeasured')
expected_maxIntMeas <- data.frame(X=rownames(expected_maxIntMeas), ID.1=expected_maxIntMeas$`ID-1`, ID.2=expected_maxIntMeas$`ID-2`, stringsAsFactors=FALSE)
expected_maxIntPred <- annotationTable(input_annotation, column='maxIntPredicted')
expected_maxIntPred <- data.frame(X=rownames(expected_maxIntPred), ID.1=expected_maxIntPred$`ID-1`, ID.2=expected_maxIntPred$`ID-2`, stringsAsFactors=FALSE)
expected_mz <- annotationTable(input_annotation, column='mz')
expected_mz <- data.frame(X=rownames(expected_mz), ID.1=expected_mz$`ID-1`, ID.2=expected_mz$`ID-2`, stringsAsFactors=FALSE)
expected_mzMax <- annotationTable(input_annotation, column='mzMax')
expected_mzMax <- data.frame(X=rownames(expected_mzMax), ID.1=expected_mzMax$`ID-1`, ID.2=expected_mzMax$`ID-2`, stringsAsFactors=FALSE)
expected_mzMin <- annotationTable(input_annotation, column='mzMin')
expected_mzMin <- data.frame(X=rownames(expected_mzMin), ID.1=expected_mzMin$`ID-1`, ID.2=expected_mzMin$`ID-2`, stringsAsFactors=FALSE)
expected_peakArea <- annotationTable(input_annotation, column='peakArea')
expected_peakArea <- data.frame(X=rownames(expected_peakArea), ID.1=expected_peakArea$`ID-1`, ID.2=expected_peakArea$`ID-2`, stringsAsFactors=FALSE)
expected_ppmErr <- annotationTable(input_annotation, column='ppm_error')
expected_ppmErr <- data.frame(X=rownames(expected_ppmErr), ID.1=expected_ppmErr$`ID-1`, ID.2=expected_ppmErr$`ID-2`, stringsAsFactors=FALSE)
expected_rt <- annotationTable(input_annotation, column='rt')
expected_rt <- data.frame(X=rownames(expected_rt), ID.1=expected_rt$`ID-1`, ID.2=expected_rt$`ID-2`, stringsAsFactors=FALSE)
expected_rtDev <- annotationTable(input_annotation, column='rt_dev_sec')
expected_rtDev <- data.frame(X=rownames(expected_rtDev), ID.1=expected_rtDev$`ID-1`, ID.2=expected_rtDev$`ID-2`, stringsAsFactors=FALSE)
expected_rtMax <- annotationTable(input_annotation, column='rtMax')
expected_rtMax <- data.frame(X=rownames(expected_rtMax), ID.1=expected_rtMax$`ID-1`, ID.2=expected_rtMax$`ID-2`, stringsAsFactors=FALSE)
expected_rtMin <- annotationTable(input_annotation, column='rtMin')
expected_rtMin <- data.frame(X=rownames(expected_rtMin), ID.1=expected_rtMin$`ID-1`, ID.2=expected_rtMin$`ID-2`, stringsAsFactors=FALSE)
expected_tailfact <- annotationTable(input_annotation, column='tailingFactor')
expected_tailfact <- data.frame(X=rownames(expected_tailfact), ID.1=expected_tailfact$`ID-1`, ID.2=expected_tailfact$`ID-2`, stringsAsFactors=FALSE)
# results (output, warnings and messages)
result_save <- evaluate_promise(outputAnnotationResult(input_annotation, saveFolder=savePath1, annotationName='testProject', verbose=TRUE))
# Check CSV has been produced
expect_true(all(file.exists(expected_path_cpdMeta, expected_path_specMeta, expected_path_summary, expected_path_asym, expected_path_found, expected_path_is_filled, expected_path_maxIntMeas, expected_path_maxIntPred, expected_path_mz, expected_path_mzMax, expected_path_mzMin, expected_path_peakArea, expected_path_ppmErr, expected_path_rt, expected_path_rtDev, expected_path_rtMax, expected_path_rtMin, expected_path_tailfact)))
# Check values saved
saved_cpdMeta <- read.csv(expected_path_cpdMeta, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_cpdMeta, expected_cpdMeta)
saved_specMeta <- read.csv(expected_path_specMeta, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE) # no filepath
expect_equal(saved_specMeta[,-1], expected_specMeta)
saved_summary <- read.csv(expected_path_summary, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_summary, expected_summary, tolerance=1e-6)
saved_asym <- read.csv(expected_path_asym, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_asym, expected_asym)
saved_found <- read.csv(expected_path_found, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_found, expected_found)
saved_is_filled <- read.csv(expected_path_is_filled, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_is_filled, expected_is_filled)
saved_maxIntMeas <- read.csv(expected_path_maxIntMeas, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_maxIntMeas, expected_maxIntMeas)
saved_maxIntPred <- read.csv(expected_path_maxIntPred, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_maxIntPred, expected_maxIntPred)
saved_mz <- read.csv(expected_path_mz, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_mz, expected_mz)
saved_mzMax <- read.csv(expected_path_mzMax, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_mzMax, expected_mzMax)
saved_mzMin <- read.csv(expected_path_mzMin, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_mzMin, expected_mzMin)
saved_peakArea <- read.csv(expected_path_peakArea, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_peakArea, expected_peakArea)
saved_ppmErr <- read.csv(expected_path_ppmErr, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_ppmErr, expected_ppmErr)
saved_rt <- read.csv(expected_path_rt, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rt, expected_rt)
saved_rtDev <- read.csv(expected_path_rtDev, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rtDev, expected_rtDev)
saved_rtMax <- read.csv(expected_path_rtMax, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rtMax, expected_rtMax)
saved_rtMin <- read.csv(expected_path_rtMin, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rtMin, expected_rtMin)
saved_tailfact <- read.csv(expected_path_tailfact, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_tailfact, expected_tailfact)
# Check result messages (save path)
expect_equal(length(result_save$messages), 18)
## no verbose
savePath2 <- tempdir()
result_save2 <- evaluate_promise(outputAnnotationResult(input_annotation, saveFolder=savePath2, annotationName='testProject', verbose=FALSE))
expect_equal(length(result_save2$messages), 0)
})
test_that('csv output without FIR, verbose, no verbose', {
# temporary file
savePath3 <- tempdir()
# clear temp folder
suppressWarnings(do.call(file.remove, list(list.files(savePath3, full.names = TRUE))))
# input
filledAnnotation2 <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable, FIR=input_FIR, uROI=input_uROI, useFIR=FALSE, 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)
input_annotation <- filledAnnotation2
# expected
expected_path_cpdMeta <- file.path(savePath3, "testProject_cpdMetadata.csv")
expected_path_specMeta <- file.path(savePath3, "testProject_spectraMetadata.csv")
expected_path_summary <- file.path(savePath3, "testProject_summary.csv")
expected_path_asym <- file.path(savePath3, "testProject_asymmetryFactor.csv")
expected_path_found <- file.path(savePath3, "testProject_found.csv")
expected_path_is_filled <- file.path(savePath3, "testProject_is_filled.csv")
expected_path_maxIntMeas <- file.path(savePath3, "testProject_maxIntMeasured.csv")
expected_path_maxIntPred <- file.path(savePath3, "testProject_maxIntPredicted.csv")
expected_path_mz <- file.path(savePath3, "testProject_mz.csv")
expected_path_mzMax <- file.path(savePath3, "testProject_mzMax.csv")
expected_path_mzMin <- file.path(savePath3, "testProject_mzMin.csv")
expected_path_peakArea <- file.path(savePath3, "testProject_peakArea.csv")
expected_path_ppmErr <- file.path(savePath3, "testProject_ppm_error.csv")
expected_path_rt <- file.path(savePath3, "testProject_rt.csv")
expected_path_rtDev <- file.path(savePath3, "testProject_rt_dev_sec.csv")
expected_path_rtMax <- file.path(savePath3, "testProject_rtMax.csv")
expected_path_rtMin <- file.path(savePath3, "testProject_rtMin.csv")
expected_path_tailfact <- file.path(savePath3, "testProject_tailingFactor.csv")
# expected values
expected_cpdMeta <- data.frame(matrix(data=c('ID-1','Cpd 1',3360.0542530120501,522.20001220703102,'a',1,'ID-2','Cpd 2',3396.1599036144598,496.20001220703102,'b',2), nrow=2,ncol=6,dimnames=list(c(), c('cpdID', 'cpdName', 'Retention.Time','m.z','testcol1', 'testcol2')), byrow=TRUE), stringsAsFactors=FALSE)
expected_cpdMeta[,c(3,4,6)] <- apply(expected_cpdMeta[,c(3,4,6)], 2, as.numeric)
expected_specMeta <- data.frame(matrix(nrow=3,ncol=4,dimnames=list(c(), c('acquisitionTime', 'TIC', 'testcol1', 'testcol2')), byrow=TRUE), stringsAsFactors=FALSE) # no filepath
expected_specMeta$acquisitionTime <- input_acquisitionTime
expected_specMeta$TIC <- input_TIC
expected_specMeta$testcol1 <- input_spectraMetadata$testcol1
expected_specMeta$testcol2 <- as.numeric(input_spectraMetadata$testcol2)
expected_summary <- data.frame(matrix(data=c('ID-1 - Cpd 1',1,0,0.02337616,8.499586, 'ID-2 - Cpd 2',1,0,0.02460103,16.361353), nrow=2,ncol=5,dimnames=list(c(), c('X', 'ratio_peaks_found', 'ratio_peaks_filled', 'ppm_error', 'rt_dev_sec')), byrow=TRUE), stringsAsFactors=FALSE)
expected_summary[,2:5] <- sapply(expected_summary[,2:5], as.numeric)
expected_asym <- annotationTable(input_annotation, column='asymmetryFactor')
expected_asym <- data.frame(X=rownames(expected_asym), ID.1=expected_asym$`ID-1`, ID.2=expected_asym$`ID-2`, stringsAsFactors=FALSE)
expected_found <- annotationTable(input_annotation, column='found')
expected_found <- data.frame(X=rownames(expected_found), ID.1=expected_found$`ID-1`, ID.2=expected_found$`ID-2`, stringsAsFactors=FALSE)
expected_is_filled <- annotationTable(input_annotation, column='is_filled')
expected_is_filled <- data.frame(X=rownames(expected_is_filled), ID.1=expected_is_filled$`ID-1`, ID.2=expected_is_filled$`ID-2`, stringsAsFactors=FALSE)
expected_maxIntMeas <- annotationTable(input_annotation, column='maxIntMeasured')
expected_maxIntMeas <- data.frame(X=rownames(expected_maxIntMeas), ID.1=expected_maxIntMeas$`ID-1`, ID.2=expected_maxIntMeas$`ID-2`, stringsAsFactors=FALSE)
expected_maxIntPred <- annotationTable(input_annotation, column='maxIntPredicted')
expected_maxIntPred <- data.frame(X=rownames(expected_maxIntPred), ID.1=expected_maxIntPred$`ID-1`, ID.2=expected_maxIntPred$`ID-2`, stringsAsFactors=FALSE)
expected_mz <- annotationTable(input_annotation, column='mz')
expected_mz <- data.frame(X=rownames(expected_mz), ID.1=expected_mz$`ID-1`, ID.2=expected_mz$`ID-2`, stringsAsFactors=FALSE)
expected_mzMax <- annotationTable(input_annotation, column='mzMax')
expected_mzMax <- data.frame(X=rownames(expected_mzMax), ID.1=expected_mzMax$`ID-1`, ID.2=expected_mzMax$`ID-2`, stringsAsFactors=FALSE)
expected_mzMin <- annotationTable(input_annotation, column='mzMin')
expected_mzMin <- data.frame(X=rownames(expected_mzMin), ID.1=expected_mzMin$`ID-1`, ID.2=expected_mzMin$`ID-2`, stringsAsFactors=FALSE)
expected_peakArea <- annotationTable(input_annotation, column='peakArea')
expected_peakArea <- data.frame(X=rownames(expected_peakArea), ID.1=expected_peakArea$`ID-1`, ID.2=expected_peakArea$`ID-2`, stringsAsFactors=FALSE)
expected_ppmErr <- annotationTable(input_annotation, column='ppm_error')
expected_ppmErr <- data.frame(X=rownames(expected_ppmErr), ID.1=expected_ppmErr$`ID-1`, ID.2=expected_ppmErr$`ID-2`, stringsAsFactors=FALSE)
expected_rt <- annotationTable(input_annotation, column='rt')
expected_rt <- data.frame(X=rownames(expected_rt), ID.1=expected_rt$`ID-1`, ID.2=expected_rt$`ID-2`, stringsAsFactors=FALSE)
expected_rtDev <- annotationTable(input_annotation, column='rt_dev_sec')
expected_rtDev <- data.frame(X=rownames(expected_rtDev), ID.1=expected_rtDev$`ID-1`, ID.2=expected_rtDev$`ID-2`, stringsAsFactors=FALSE)
expected_rtMax <- annotationTable(input_annotation, column='rtMax')
expected_rtMax <- data.frame(X=rownames(expected_rtMax), ID.1=expected_rtMax$`ID-1`, ID.2=expected_rtMax$`ID-2`, stringsAsFactors=FALSE)
expected_rtMin <- annotationTable(input_annotation, column='rtMin')
expected_rtMin <- data.frame(X=rownames(expected_rtMin), ID.1=expected_rtMin$`ID-1`, ID.2=expected_rtMin$`ID-2`, stringsAsFactors=FALSE)
expected_tailfact <- annotationTable(input_annotation, column='tailingFactor')
expected_tailfact <- data.frame(X=rownames(expected_tailfact), ID.1=expected_tailfact$`ID-1`, ID.2=expected_tailfact$`ID-2`, stringsAsFactors=FALSE)
# results (output, warnings and messages)
result_save <- evaluate_promise(outputAnnotationResult(input_annotation, saveFolder=savePath3, annotationName='testProject', verbose=TRUE))
# Check CSV has been produced
expect_true(all(file.exists(expected_path_cpdMeta, expected_path_specMeta, expected_path_summary, expected_path_asym, expected_path_found, expected_path_is_filled, expected_path_maxIntMeas, expected_path_maxIntPred, expected_path_mz, expected_path_mzMax, expected_path_mzMin, expected_path_peakArea, expected_path_ppmErr, expected_path_rt, expected_path_rtDev, expected_path_rtMax, expected_path_rtMin, expected_path_tailfact)))
# Check values saved
saved_cpdMeta <- read.csv(expected_path_cpdMeta, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_cpdMeta, expected_cpdMeta)
saved_specMeta <- read.csv(expected_path_specMeta, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE) # no filepath
expect_equal(saved_specMeta[,-1], expected_specMeta)
saved_summary <- read.csv(expected_path_summary, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_summary, expected_summary, tolerance=1e-6)
saved_asym <- read.csv(expected_path_asym, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_asym, expected_asym)
saved_found <- read.csv(expected_path_found, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_found, expected_found)
saved_is_filled <- read.csv(expected_path_is_filled, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_is_filled, expected_is_filled)
saved_maxIntMeas <- read.csv(expected_path_maxIntMeas, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_maxIntMeas, expected_maxIntMeas)
saved_maxIntPred <- read.csv(expected_path_maxIntPred, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_maxIntPred, expected_maxIntPred)
saved_mz <- read.csv(expected_path_mz, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_mz, expected_mz)
saved_mzMax <- read.csv(expected_path_mzMax, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_mzMax, expected_mzMax)
saved_mzMin <- read.csv(expected_path_mzMin, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_mzMin, expected_mzMin)
saved_peakArea <- read.csv(expected_path_peakArea, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_peakArea, expected_peakArea)
saved_ppmErr <- read.csv(expected_path_ppmErr, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_ppmErr, expected_ppmErr)
saved_rt <- read.csv(expected_path_rt, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rt, expected_rt)
saved_rtDev <- read.csv(expected_path_rtDev, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rtDev, expected_rtDev)
saved_rtMax <- read.csv(expected_path_rtMax, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rtMax, expected_rtMax)
saved_rtMin <- read.csv(expected_path_rtMin, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rtMin, expected_rtMin)
saved_tailfact <- read.csv(expected_path_tailfact, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_tailfact, expected_tailfact)
# Check result messages (save path)
expect_equal(length(result_save$messages), 18)
## no verbose
savePath4 <- tempdir()
result_save4 <- evaluate_promise(outputAnnotationResult(input_annotation, saveFolder=savePath4, annotationName='testProject', verbose=FALSE))
expect_equal(length(result_save4$messages), 0)
})
test_that('cpdMetadata mean rt mz calculation with and without filled, verbose, no verbose', {
# Calculate mean rt mz of found peaks but not filled
# only the peakTable is correctly generated in the test data
# temporary file
savePath5 <- tempdir()
# clear temp folder
suppressWarnings(do.call(file.remove, list(list.files(savePath5, full.names = TRUE))))
# cpd 1 (no FIR used) sample 1-2 found (not filled), sample 3 not found, (not filled)
# cpd 2 (FIR used) sample 1 found (filled), sample 2-3 found, (not filled)
# peakTables
# sample 1
tmp_peakTable1 <- data.frame(matrix(vector(), 2, 15, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "maxIntMeasured", "maxIntPredicted", "is_filled", "ppm_error", "rt_dev_sec", "tailingFactor", "asymmetryFactor"))),stringsAsFactors=FALSE)
tmp_peakTable1[1,] <- c(TRUE, 3309.7589296586070, 3346.8277590361445, 3385.4098874628098, 522.194778, 522.21, 522.205222, 26133726.6811244078, 889280, 901015.80529226747, FALSE, 0.023376160866574614, 1.93975903614455092, 1.0153573486330891, 1.0268238825675249)
tmp_peakTable1[2,] <- c(TRUE, 3345.3766648628907, 3386.5288072289159, 3428.2788374983961, 496.20001220703125, 496.20001220703125, 496.20001220703125, 35472141.3330242932, 1128960, 1113576.69008227298, TRUE, 0.024601030353423384, 0.95180722891564074, 1.0053782620427065, 1.0093180792278085)
tmp_peakTable1[,c(1,11)] <- sapply(tmp_peakTable1[,c(1,11)], as.logical)
tmp_peakTable1[,c(2:10,12:15)] <- sapply(tmp_peakTable1[,c(2:10,12:15)], as.numeric)
# sample 2
tmp_peakTable2 <- data.frame(matrix(vector(), 2, 15, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "maxIntMeasured", "maxIntPredicted", "is_filled", "ppm_error", "rt_dev_sec", "tailingFactor", "asymmetryFactor"))),stringsAsFactors=FALSE)
tmp_peakTable2[1,] <- c(TRUE, 3326.1063495851854, 3365.102, 3407.2726475892355, 522.194778, 522.20001220703125, 522.205222, 24545301.622835573, 761664, 790802.2209998488, FALSE, 0.023376160866574614, 0.2139999999999, 1.0339153786516375, 1.0630802030537212)
tmp_peakTable2[2,] <- c(TRUE, 3365.0238566258713, 3405.791, 3453.4049569205681, 496.195038, 496.20001220703125, 496.204962, 37207579.286265120, 1099264, 1098720.2929832144, FALSE, 0.024601030353423384, 20.2139999999999, 1.0839602450900523, 1.1717845972583161)
tmp_peakTable2[,c(1,11)] <- sapply(tmp_peakTable2[,c(1,11)], as.logical)
tmp_peakTable2[,c(2:10,12:15)] <- sapply(tmp_peakTable2[,c(2:10,12:15)], as.numeric)
# sample 3
tmp_peakTable3 <- data.frame(matrix(vector(), 2, 15, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "maxIntMeasured", "maxIntPredicted", "is_filled", "ppm_error", "rt_dev_sec", "tailingFactor", "asymmetryFactor"))),stringsAsFactors=FALSE)
tmp_peakTable3[1,] <- c(FALSE, 3333.8625894557053, 3368.233, 3407.4362838927614, 522.194778, 522.20001220703125, 522.205222, 21447174.404490683, 758336, 765009.9805796633, FALSE, 0.023376160866574614, 23.345000000000255, 1.0609102044546637, 1.1155310457756928)
tmp_peakTable3[2,] <- c(TRUE, 3373.3998828113113, 3413.4952530120481, 3454.4490330927388, 496.195038, 496.21, 496.204962, 35659353.614476241, 1149440, 1145857.7611069249, FALSE, 0.024601030353423384, 27.918253012047899, 1.0081407426394933, 1.0143315197994494)
tmp_peakTable3[,c(1,11)] <- sapply(tmp_peakTable3[,c(1,11)], as.logical)
tmp_peakTable3[,c(2:10,12:15)] <- sapply(tmp_peakTable3[,c(2:10,12:15)], as.numeric)
tmp_peakTables <- list(tmp_peakTable1, tmp_peakTable2, tmp_peakTable3)
# input
filledAnnotation3 <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable, FIR=input_FIR, uROI=input_uROI, useFIR=FALSE, uROIExist=TRUE, useUROI=TRUE, cpdMetadata=input_cpdMetadata, spectraMetadata=input_spectraMetadata, acquisitionTime=input_acquisitionTime, TIC=input_TIC, peakTables=tmp_peakTables, dataPoints=input_dataPoints, peakFit=input_peakFit, isAnnotated=TRUE)
input_annotation <- filledAnnotation3
# expected
expected_path_cpdMeta <- file.path(savePath5, "testCpdMeta_cpdMetadata.csv")
# expected values
expected_cpdMeta <- data.frame(matrix(data=c('ID-1','Cpd 1',3355.9648795180719,522.205,'a',1,'ID-2','Cpd 2',3409.6431265060241,496.205,'b',2), nrow=2,ncol=6,dimnames=list(c(), c('cpdID', 'cpdName', 'Retention.Time','m.z','testcol1', 'testcol2')), byrow=TRUE), stringsAsFactors=FALSE)
expected_cpdMeta[,c(3,4,6)] <- apply(expected_cpdMeta[,c(3,4,6)], 2, as.numeric)
# results (output, warnings and messages)
result_save <- evaluate_promise(outputAnnotationResult(input_annotation, saveFolder=savePath5, annotationName='testCpdMeta', verbose=TRUE))
# Check CSV has been produced
expect_true(file.exists(expected_path_cpdMeta))
# Check values saved
saved_cpdMeta <- read.csv(expected_path_cpdMeta, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_cpdMeta, expected_cpdMeta)
})
test_that('raise error', {
# error if object is not annotated
savePath1 <- tempdir()
input_annotation <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable)
msg1 <- 'Object has not been annotated, no annotation results to save'
expect_error(outputAnnotationResult(input_annotation, saveFolder=savePath1), msg1, fixed=TRUE)
})
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context('peakPantheRAnnotation_outputAnnotationResult()')
## Test the CSV output of all annotation results
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, 15, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "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, 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, 1128960, 1113576.69008227298, FALSE, 0.024601030353423384, 0.95180722891564074, 1.0053782620427065, 1.0093180792278085)
peakTable1[,c(1,11)] <- sapply(peakTable1[,c(1,11)], as.logical)
peakTable1[,c(2:10,12:15)] <- sapply(peakTable1[,c(2:10,12:15)], as.numeric)
# 2
peakTable2 <- data.frame(matrix(vector(), 2, 15, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "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, 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, 1099264, 1098720.2929832144, FALSE, 0.024601030353423384, 20.2139999999999, 1.0839602450900523, 1.1717845972583161)
peakTable2[,c(1,11)] <- sapply(peakTable2[,c(1,11)], as.logical)
peakTable2[,c(2:10,12:15)] <- sapply(peakTable2[,c(2:10,12:15)], as.numeric)
# 3
peakTable3 <- data.frame(matrix(vector(), 2, 15, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "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, 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, 1149440, 1145857.7611069249, TRUE, 0.024601030353423384, 27.918253012047899, 1.0081407426394933, 1.0143315197994494)
peakTable3[,c(1,11)] <- sapply(peakTable3[,c(1,11)], as.logical)
peakTable3[,c(2:10,12:15)] <- sapply(peakTable3[,c(2:10,12:15)], 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('csv output with FIR, verbose, no verbose', {
# temporary file
savePath1 <- tempdir()
# input
input_annotation <- filledAnnotation
# expected
expected_path_cpdMeta <- file.path(savePath1, "testProject_cpdMetadata.csv")
expected_path_specMeta <- file.path(savePath1, "testProject_spectraMetadata.csv")
expected_path_summary <- file.path(savePath1, "testProject_summary.csv")
expected_path_asym <- file.path(savePath1, "testProject_asymmetryFactor.csv")
expected_path_found <- file.path(savePath1, "testProject_found.csv")
expected_path_is_filled <- file.path(savePath1, "testProject_is_filled.csv")
expected_path_maxIntMeas <- file.path(savePath1, "testProject_maxIntMeasured.csv")
expected_path_maxIntPred <- file.path(savePath1, "testProject_maxIntPredicted.csv")
expected_path_mz <- file.path(savePath1, "testProject_mz.csv")
expected_path_mzMax <- file.path(savePath1, "testProject_mzMax.csv")
expected_path_mzMin <- file.path(savePath1, "testProject_mzMin.csv")
expected_path_peakArea <- file.path(savePath1, "testProject_peakArea.csv")
expected_path_ppmErr <- file.path(savePath1, "testProject_ppm_error.csv")
expected_path_rt <- file.path(savePath1, "testProject_rt.csv")
expected_path_rtDev <- file.path(savePath1, "testProject_rt_dev_sec.csv")
expected_path_rtMax <- file.path(savePath1, "testProject_rtMax.csv")
expected_path_rtMin <- file.path(savePath1, "testProject_rtMin.csv")
expected_path_tailfact <- file.path(savePath1, "testProject_tailingFactor.csv")
# expected values
expected_cpdMeta <- data.frame(matrix(data=c('ID-1','Cpd 1',3360.0542530120501,522.20001220703102,'a',1,'ID-2','Cpd 2',3396.1599036144598,496.20001220703102,'b',2), nrow=2,ncol=6,dimnames=list(c(), c('cpdID', 'cpdName', 'Retention.Time','m.z','testcol1', 'testcol2')), byrow=TRUE), stringsAsFactors=FALSE)
expected_cpdMeta[,c(3,4,6)] <- apply(expected_cpdMeta[,c(3,4,6)], 2, as.numeric)
expected_specMeta <- data.frame(matrix(nrow=3,ncol=4,dimnames=list(c(), c('acquisitionTime', 'TIC', 'testcol1', 'testcol2')), byrow=TRUE), stringsAsFactors=FALSE) # no filepath
expected_specMeta$acquisitionTime <- input_acquisitionTime
expected_specMeta$TIC <- input_TIC
expected_specMeta$testcol1 <- input_spectraMetadata$testcol1
expected_specMeta$testcol2 <- as.numeric(input_spectraMetadata$testcol2)
expected_summary <- data.frame(matrix(data=c('ID-1 - Cpd 1',1,0,0.02337616,8.499586, 'ID-2 - Cpd 2',0.6666667,0.333333,0.02460103,16.361353), nrow=2,ncol=5,dimnames=list(c(), c('X', 'ratio_peaks_found', 'ratio_peaks_filled', 'ppm_error', 'rt_dev_sec')), byrow=TRUE), stringsAsFactors=FALSE)
expected_summary[,2:5] <- sapply(expected_summary[,2:5], as.numeric)
expected_asym <- annotationTable(input_annotation, column='asymmetryFactor')
expected_asym <- data.frame(X=rownames(expected_asym), ID.1=expected_asym$`ID-1`, ID.2=expected_asym$`ID-2`, stringsAsFactors=FALSE)
expected_found <- annotationTable(input_annotation, column='found')
expected_found <- data.frame(X=rownames(expected_found), ID.1=expected_found$`ID-1`, ID.2=expected_found$`ID-2`, stringsAsFactors=FALSE)
expected_is_filled <- annotationTable(input_annotation, column='is_filled')
expected_is_filled <- data.frame(X=rownames(expected_is_filled), ID.1=expected_is_filled$`ID-1`, ID.2=expected_is_filled$`ID-2`, stringsAsFactors=FALSE)
expected_maxIntMeas <- annotationTable(input_annotation, column='maxIntMeasured')
expected_maxIntMeas <- data.frame(X=rownames(expected_maxIntMeas), ID.1=expected_maxIntMeas$`ID-1`, ID.2=expected_maxIntMeas$`ID-2`, stringsAsFactors=FALSE)
expected_maxIntPred <- annotationTable(input_annotation, column='maxIntPredicted')
expected_maxIntPred <- data.frame(X=rownames(expected_maxIntPred), ID.1=expected_maxIntPred$`ID-1`, ID.2=expected_maxIntPred$`ID-2`, stringsAsFactors=FALSE)
expected_mz <- annotationTable(input_annotation, column='mz')
expected_mz <- data.frame(X=rownames(expected_mz), ID.1=expected_mz$`ID-1`, ID.2=expected_mz$`ID-2`, stringsAsFactors=FALSE)
expected_mzMax <- annotationTable(input_annotation, column='mzMax')
expected_mzMax <- data.frame(X=rownames(expected_mzMax), ID.1=expected_mzMax$`ID-1`, ID.2=expected_mzMax$`ID-2`, stringsAsFactors=FALSE)
expected_mzMin <- annotationTable(input_annotation, column='mzMin')
expected_mzMin <- data.frame(X=rownames(expected_mzMin), ID.1=expected_mzMin$`ID-1`, ID.2=expected_mzMin$`ID-2`, stringsAsFactors=FALSE)
expected_peakArea <- annotationTable(input_annotation, column='peakArea')
expected_peakArea <- data.frame(X=rownames(expected_peakArea), ID.1=expected_peakArea$`ID-1`, ID.2=expected_peakArea$`ID-2`, stringsAsFactors=FALSE)
expected_ppmErr <- annotationTable(input_annotation, column='ppm_error')
expected_ppmErr <- data.frame(X=rownames(expected_ppmErr), ID.1=expected_ppmErr$`ID-1`, ID.2=expected_ppmErr$`ID-2`, stringsAsFactors=FALSE)
expected_rt <- annotationTable(input_annotation, column='rt')
expected_rt <- data.frame(X=rownames(expected_rt), ID.1=expected_rt$`ID-1`, ID.2=expected_rt$`ID-2`, stringsAsFactors=FALSE)
expected_rtDev <- annotationTable(input_annotation, column='rt_dev_sec')
expected_rtDev <- data.frame(X=rownames(expected_rtDev), ID.1=expected_rtDev$`ID-1`, ID.2=expected_rtDev$`ID-2`, stringsAsFactors=FALSE)
expected_rtMax <- annotationTable(input_annotation, column='rtMax')
expected_rtMax <- data.frame(X=rownames(expected_rtMax), ID.1=expected_rtMax$`ID-1`, ID.2=expected_rtMax$`ID-2`, stringsAsFactors=FALSE)
expected_rtMin <- annotationTable(input_annotation, column='rtMin')
expected_rtMin <- data.frame(X=rownames(expected_rtMin), ID.1=expected_rtMin$`ID-1`, ID.2=expected_rtMin$`ID-2`, stringsAsFactors=FALSE)
expected_tailfact <- annotationTable(input_annotation, column='tailingFactor')
expected_tailfact <- data.frame(X=rownames(expected_tailfact), ID.1=expected_tailfact$`ID-1`, ID.2=expected_tailfact$`ID-2`, stringsAsFactors=FALSE)
# results (output, warnings and messages)
result_save <- evaluate_promise(outputAnnotationResult(input_annotation, saveFolder=savePath1, annotationName='testProject', verbose=TRUE))
# Check CSV has been produced
expect_true(all(file.exists(expected_path_cpdMeta, expected_path_specMeta, expected_path_summary, expected_path_asym, expected_path_found, expected_path_is_filled, expected_path_maxIntMeas, expected_path_maxIntPred, expected_path_mz, expected_path_mzMax, expected_path_mzMin, expected_path_peakArea, expected_path_ppmErr, expected_path_rt, expected_path_rtDev, expected_path_rtMax, expected_path_rtMin, expected_path_tailfact)))
# Check values saved
saved_cpdMeta <- read.csv(expected_path_cpdMeta, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_cpdMeta, expected_cpdMeta)
saved_specMeta <- read.csv(expected_path_specMeta, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE) # no filepath
expect_equal(saved_specMeta[,-1], expected_specMeta)
saved_summary <- read.csv(expected_path_summary, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_summary, expected_summary, tolerance=1e-6)
saved_asym <- read.csv(expected_path_asym, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_asym, expected_asym)
saved_found <- read.csv(expected_path_found, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_found, expected_found)
saved_is_filled <- read.csv(expected_path_is_filled, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_is_filled, expected_is_filled)
saved_maxIntMeas <- read.csv(expected_path_maxIntMeas, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_maxIntMeas, expected_maxIntMeas)
saved_maxIntPred <- read.csv(expected_path_maxIntPred, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_maxIntPred, expected_maxIntPred)
saved_mz <- read.csv(expected_path_mz, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_mz, expected_mz)
saved_mzMax <- read.csv(expected_path_mzMax, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_mzMax, expected_mzMax)
saved_mzMin <- read.csv(expected_path_mzMin, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_mzMin, expected_mzMin)
saved_peakArea <- read.csv(expected_path_peakArea, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_peakArea, expected_peakArea)
saved_ppmErr <- read.csv(expected_path_ppmErr, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_ppmErr, expected_ppmErr)
saved_rt <- read.csv(expected_path_rt, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rt, expected_rt)
saved_rtDev <- read.csv(expected_path_rtDev, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rtDev, expected_rtDev)
saved_rtMax <- read.csv(expected_path_rtMax, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rtMax, expected_rtMax)
saved_rtMin <- read.csv(expected_path_rtMin, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rtMin, expected_rtMin)
saved_tailfact <- read.csv(expected_path_tailfact, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_tailfact, expected_tailfact)
# Check result messages (save path)
expect_equal(length(result_save$messages), 18)
## no verbose
savePath2 <- tempdir()
result_save2 <- evaluate_promise(outputAnnotationResult(input_annotation, saveFolder=savePath2, annotationName='testProject', verbose=FALSE))
expect_equal(length(result_save2$messages), 0)
})
test_that('csv output without FIR, verbose, no verbose', {
# temporary file
savePath3 <- tempdir()
# clear temp folder
suppressWarnings(do.call(file.remove, list(list.files(savePath3, full.names = TRUE))))
# input
filledAnnotation2 <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable, FIR=input_FIR, uROI=input_uROI, useFIR=FALSE, 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)
input_annotation <- filledAnnotation2
# expected
expected_path_cpdMeta <- file.path(savePath3, "testProject_cpdMetadata.csv")
expected_path_specMeta <- file.path(savePath3, "testProject_spectraMetadata.csv")
expected_path_summary <- file.path(savePath3, "testProject_summary.csv")
expected_path_asym <- file.path(savePath3, "testProject_asymmetryFactor.csv")
expected_path_found <- file.path(savePath3, "testProject_found.csv")
expected_path_is_filled <- file.path(savePath3, "testProject_is_filled.csv")
expected_path_maxIntMeas <- file.path(savePath3, "testProject_maxIntMeasured.csv")
expected_path_maxIntPred <- file.path(savePath3, "testProject_maxIntPredicted.csv")
expected_path_mz <- file.path(savePath3, "testProject_mz.csv")
expected_path_mzMax <- file.path(savePath3, "testProject_mzMax.csv")
expected_path_mzMin <- file.path(savePath3, "testProject_mzMin.csv")
expected_path_peakArea <- file.path(savePath3, "testProject_peakArea.csv")
expected_path_ppmErr <- file.path(savePath3, "testProject_ppm_error.csv")
expected_path_rt <- file.path(savePath3, "testProject_rt.csv")
expected_path_rtDev <- file.path(savePath3, "testProject_rt_dev_sec.csv")
expected_path_rtMax <- file.path(savePath3, "testProject_rtMax.csv")
expected_path_rtMin <- file.path(savePath3, "testProject_rtMin.csv")
expected_path_tailfact <- file.path(savePath3, "testProject_tailingFactor.csv")
# expected values
expected_cpdMeta <- data.frame(matrix(data=c('ID-1','Cpd 1',3360.0542530120501,522.20001220703102,'a',1,'ID-2','Cpd 2',3396.1599036144598,496.20001220703102,'b',2), nrow=2,ncol=6,dimnames=list(c(), c('cpdID', 'cpdName', 'Retention.Time','m.z','testcol1', 'testcol2')), byrow=TRUE), stringsAsFactors=FALSE)
expected_cpdMeta[,c(3,4,6)] <- apply(expected_cpdMeta[,c(3,4,6)], 2, as.numeric)
expected_specMeta <- data.frame(matrix(nrow=3,ncol=4,dimnames=list(c(), c('acquisitionTime', 'TIC', 'testcol1', 'testcol2')), byrow=TRUE), stringsAsFactors=FALSE) # no filepath
expected_specMeta$acquisitionTime <- input_acquisitionTime
expected_specMeta$TIC <- input_TIC
expected_specMeta$testcol1 <- input_spectraMetadata$testcol1
expected_specMeta$testcol2 <- as.numeric(input_spectraMetadata$testcol2)
expected_summary <- data.frame(matrix(data=c('ID-1 - Cpd 1',1,0,0.02337616,8.499586, 'ID-2 - Cpd 2',1,0,0.02460103,16.361353), nrow=2,ncol=5,dimnames=list(c(), c('X', 'ratio_peaks_found', 'ratio_peaks_filled', 'ppm_error', 'rt_dev_sec')), byrow=TRUE), stringsAsFactors=FALSE)
expected_summary[,2:5] <- sapply(expected_summary[,2:5], as.numeric)
expected_asym <- annotationTable(input_annotation, column='asymmetryFactor')
expected_asym <- data.frame(X=rownames(expected_asym), ID.1=expected_asym$`ID-1`, ID.2=expected_asym$`ID-2`, stringsAsFactors=FALSE)
expected_found <- annotationTable(input_annotation, column='found')
expected_found <- data.frame(X=rownames(expected_found), ID.1=expected_found$`ID-1`, ID.2=expected_found$`ID-2`, stringsAsFactors=FALSE)
expected_is_filled <- annotationTable(input_annotation, column='is_filled')
expected_is_filled <- data.frame(X=rownames(expected_is_filled), ID.1=expected_is_filled$`ID-1`, ID.2=expected_is_filled$`ID-2`, stringsAsFactors=FALSE)
expected_maxIntMeas <- annotationTable(input_annotation, column='maxIntMeasured')
expected_maxIntMeas <- data.frame(X=rownames(expected_maxIntMeas), ID.1=expected_maxIntMeas$`ID-1`, ID.2=expected_maxIntMeas$`ID-2`, stringsAsFactors=FALSE)
expected_maxIntPred <- annotationTable(input_annotation, column='maxIntPredicted')
expected_maxIntPred <- data.frame(X=rownames(expected_maxIntPred), ID.1=expected_maxIntPred$`ID-1`, ID.2=expected_maxIntPred$`ID-2`, stringsAsFactors=FALSE)
expected_mz <- annotationTable(input_annotation, column='mz')
expected_mz <- data.frame(X=rownames(expected_mz), ID.1=expected_mz$`ID-1`, ID.2=expected_mz$`ID-2`, stringsAsFactors=FALSE)
expected_mzMax <- annotationTable(input_annotation, column='mzMax')
expected_mzMax <- data.frame(X=rownames(expected_mzMax), ID.1=expected_mzMax$`ID-1`, ID.2=expected_mzMax$`ID-2`, stringsAsFactors=FALSE)
expected_mzMin <- annotationTable(input_annotation, column='mzMin')
expected_mzMin <- data.frame(X=rownames(expected_mzMin), ID.1=expected_mzMin$`ID-1`, ID.2=expected_mzMin$`ID-2`, stringsAsFactors=FALSE)
expected_peakArea <- annotationTable(input_annotation, column='peakArea')
expected_peakArea <- data.frame(X=rownames(expected_peakArea), ID.1=expected_peakArea$`ID-1`, ID.2=expected_peakArea$`ID-2`, stringsAsFactors=FALSE)
expected_ppmErr <- annotationTable(input_annotation, column='ppm_error')
expected_ppmErr <- data.frame(X=rownames(expected_ppmErr), ID.1=expected_ppmErr$`ID-1`, ID.2=expected_ppmErr$`ID-2`, stringsAsFactors=FALSE)
expected_rt <- annotationTable(input_annotation, column='rt')
expected_rt <- data.frame(X=rownames(expected_rt), ID.1=expected_rt$`ID-1`, ID.2=expected_rt$`ID-2`, stringsAsFactors=FALSE)
expected_rtDev <- annotationTable(input_annotation, column='rt_dev_sec')
expected_rtDev <- data.frame(X=rownames(expected_rtDev), ID.1=expected_rtDev$`ID-1`, ID.2=expected_rtDev$`ID-2`, stringsAsFactors=FALSE)
expected_rtMax <- annotationTable(input_annotation, column='rtMax')
expected_rtMax <- data.frame(X=rownames(expected_rtMax), ID.1=expected_rtMax$`ID-1`, ID.2=expected_rtMax$`ID-2`, stringsAsFactors=FALSE)
expected_rtMin <- annotationTable(input_annotation, column='rtMin')
expected_rtMin <- data.frame(X=rownames(expected_rtMin), ID.1=expected_rtMin$`ID-1`, ID.2=expected_rtMin$`ID-2`, stringsAsFactors=FALSE)
expected_tailfact <- annotationTable(input_annotation, column='tailingFactor')
expected_tailfact <- data.frame(X=rownames(expected_tailfact), ID.1=expected_tailfact$`ID-1`, ID.2=expected_tailfact$`ID-2`, stringsAsFactors=FALSE)
# results (output, warnings and messages)
result_save <- evaluate_promise(outputAnnotationResult(input_annotation, saveFolder=savePath3, annotationName='testProject', verbose=TRUE))
# Check CSV has been produced
expect_true(all(file.exists(expected_path_cpdMeta, expected_path_specMeta, expected_path_summary, expected_path_asym, expected_path_found, expected_path_is_filled, expected_path_maxIntMeas, expected_path_maxIntPred, expected_path_mz, expected_path_mzMax, expected_path_mzMin, expected_path_peakArea, expected_path_ppmErr, expected_path_rt, expected_path_rtDev, expected_path_rtMax, expected_path_rtMin, expected_path_tailfact)))
# Check values saved
saved_cpdMeta <- read.csv(expected_path_cpdMeta, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_cpdMeta, expected_cpdMeta)
saved_specMeta <- read.csv(expected_path_specMeta, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE) # no filepath
expect_equal(saved_specMeta[,-1], expected_specMeta)
saved_summary <- read.csv(expected_path_summary, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_summary, expected_summary, tolerance=1e-6)
saved_asym <- read.csv(expected_path_asym, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_asym, expected_asym)
saved_found <- read.csv(expected_path_found, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_found, expected_found)
saved_is_filled <- read.csv(expected_path_is_filled, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_is_filled, expected_is_filled)
saved_maxIntMeas <- read.csv(expected_path_maxIntMeas, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_maxIntMeas, expected_maxIntMeas)
saved_maxIntPred <- read.csv(expected_path_maxIntPred, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_maxIntPred, expected_maxIntPred)
saved_mz <- read.csv(expected_path_mz, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_mz, expected_mz)
saved_mzMax <- read.csv(expected_path_mzMax, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_mzMax, expected_mzMax)
saved_mzMin <- read.csv(expected_path_mzMin, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_mzMin, expected_mzMin)
saved_peakArea <- read.csv(expected_path_peakArea, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_peakArea, expected_peakArea)
saved_ppmErr <- read.csv(expected_path_ppmErr, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_ppmErr, expected_ppmErr)
saved_rt <- read.csv(expected_path_rt, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rt, expected_rt)
saved_rtDev <- read.csv(expected_path_rtDev, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rtDev, expected_rtDev)
saved_rtMax <- read.csv(expected_path_rtMax, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rtMax, expected_rtMax)
saved_rtMin <- read.csv(expected_path_rtMin, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_rtMin, expected_rtMin)
saved_tailfact <- read.csv(expected_path_tailfact, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_tailfact, expected_tailfact)
# Check result messages (save path)
expect_equal(length(result_save$messages), 18)
## no verbose
savePath4 <- tempdir()
result_save4 <- evaluate_promise(outputAnnotationResult(input_annotation, saveFolder=savePath4, annotationName='testProject', verbose=FALSE))
expect_equal(length(result_save4$messages), 0)
})
test_that('cpdMetadata mean rt mz calculation with and without filled, verbose, no verbose', {
# Calculate mean rt mz of found peaks but not filled
# only the peakTable is correctly generated in the test data
# temporary file
savePath5 <- tempdir()
# clear temp folder
suppressWarnings(do.call(file.remove, list(list.files(savePath5, full.names = TRUE))))
# cpd 1 (no FIR used) sample 1-2 found (not filled), sample 3 not found, (not filled)
# cpd 2 (FIR used) sample 1 found (filled), sample 2-3 found, (not filled)
# peakTables
# sample 1
tmp_peakTable1 <- data.frame(matrix(vector(), 2, 15, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "maxIntMeasured", "maxIntPredicted", "is_filled", "ppm_error", "rt_dev_sec", "tailingFactor", "asymmetryFactor"))),stringsAsFactors=FALSE)
tmp_peakTable1[1,] <- c(TRUE, 3309.7589296586070, 3346.8277590361445, 3385.4098874628098, 522.194778, 522.21, 522.205222, 26133726.6811244078, 889280, 901015.80529226747, FALSE, 0.023376160866574614, 1.93975903614455092, 1.0153573486330891, 1.0268238825675249)
tmp_peakTable1[2,] <- c(TRUE, 3345.3766648628907, 3386.5288072289159, 3428.2788374983961, 496.20001220703125, 496.20001220703125, 496.20001220703125, 35472141.3330242932, 1128960, 1113576.69008227298, TRUE, 0.024601030353423384, 0.95180722891564074, 1.0053782620427065, 1.0093180792278085)
tmp_peakTable1[,c(1,11)] <- sapply(tmp_peakTable1[,c(1,11)], as.logical)
tmp_peakTable1[,c(2:10,12:15)] <- sapply(tmp_peakTable1[,c(2:10,12:15)], as.numeric)
# sample 2
tmp_peakTable2 <- data.frame(matrix(vector(), 2, 15, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "maxIntMeasured", "maxIntPredicted", "is_filled", "ppm_error", "rt_dev_sec", "tailingFactor", "asymmetryFactor"))),stringsAsFactors=FALSE)
tmp_peakTable2[1,] <- c(TRUE, 3326.1063495851854, 3365.102, 3407.2726475892355, 522.194778, 522.20001220703125, 522.205222, 24545301.622835573, 761664, 790802.2209998488, FALSE, 0.023376160866574614, 0.2139999999999, 1.0339153786516375, 1.0630802030537212)
tmp_peakTable2[2,] <- c(TRUE, 3365.0238566258713, 3405.791, 3453.4049569205681, 496.195038, 496.20001220703125, 496.204962, 37207579.286265120, 1099264, 1098720.2929832144, FALSE, 0.024601030353423384, 20.2139999999999, 1.0839602450900523, 1.1717845972583161)
tmp_peakTable2[,c(1,11)] <- sapply(tmp_peakTable2[,c(1,11)], as.logical)
tmp_peakTable2[,c(2:10,12:15)] <- sapply(tmp_peakTable2[,c(2:10,12:15)], as.numeric)
# sample 3
tmp_peakTable3 <- data.frame(matrix(vector(), 2, 15, dimnames=list(c(), c("found", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax", "peakArea", "maxIntMeasured", "maxIntPredicted", "is_filled", "ppm_error", "rt_dev_sec", "tailingFactor", "asymmetryFactor"))),stringsAsFactors=FALSE)
tmp_peakTable3[1,] <- c(FALSE, 3333.8625894557053, 3368.233, 3407.4362838927614, 522.194778, 522.20001220703125, 522.205222, 21447174.404490683, 758336, 765009.9805796633, FALSE, 0.023376160866574614, 23.345000000000255, 1.0609102044546637, 1.1155310457756928)
tmp_peakTable3[2,] <- c(TRUE, 3373.3998828113113, 3413.4952530120481, 3454.4490330927388, 496.195038, 496.21, 496.204962, 35659353.614476241, 1149440, 1145857.7611069249, FALSE, 0.024601030353423384, 27.918253012047899, 1.0081407426394933, 1.0143315197994494)
tmp_peakTable3[,c(1,11)] <- sapply(tmp_peakTable3[,c(1,11)], as.logical)
tmp_peakTable3[,c(2:10,12:15)] <- sapply(tmp_peakTable3[,c(2:10,12:15)], as.numeric)
tmp_peakTables <- list(tmp_peakTable1, tmp_peakTable2, tmp_peakTable3)
# input
filledAnnotation3 <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable, FIR=input_FIR, uROI=input_uROI, useFIR=FALSE, uROIExist=TRUE, useUROI=TRUE, cpdMetadata=input_cpdMetadata, spectraMetadata=input_spectraMetadata, acquisitionTime=input_acquisitionTime, TIC=input_TIC, peakTables=tmp_peakTables, dataPoints=input_dataPoints, peakFit=input_peakFit, isAnnotated=TRUE)
input_annotation <- filledAnnotation3
# expected
expected_path_cpdMeta <- file.path(savePath5, "testCpdMeta_cpdMetadata.csv")
# expected values
expected_cpdMeta <- data.frame(matrix(data=c('ID-1','Cpd 1',3355.9648795180719,522.205,'a',1,'ID-2','Cpd 2',3409.6431265060241,496.205,'b',2), nrow=2,ncol=6,dimnames=list(c(), c('cpdID', 'cpdName', 'Retention.Time','m.z','testcol1', 'testcol2')), byrow=TRUE), stringsAsFactors=FALSE)
expected_cpdMeta[,c(3,4,6)] <- apply(expected_cpdMeta[,c(3,4,6)], 2, as.numeric)
# results (output, warnings and messages)
result_save <- evaluate_promise(outputAnnotationResult(input_annotation, saveFolder=savePath5, annotationName='testCpdMeta', verbose=TRUE))
# Check CSV has been produced
expect_true(file.exists(expected_path_cpdMeta))
# Check values saved
saved_cpdMeta <- read.csv(expected_path_cpdMeta, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
expect_equal(saved_cpdMeta, expected_cpdMeta)
})
test_that('raise error', {
# error if object is not annotated
savePath1 <- tempdir()
input_annotation <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable)
msg1 <- 'Object has not been annotated, no annotation results to save'
expect_error(outputAnnotationResult(input_annotation, saveFolder=savePath1), msg1, fixed=TRUE)
})
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