tests/testthat/test_peakPantheRAnnotation_outputAnnotationDiagnostic-method.R

In peakPantheR: Peak Picking and Annotation of High Resolution Experiments

context('peakPantheRAnnotation_outputAnnotationDiagnostic()')

## Test the output of annotation parameters as CSV and diagnostic plots

skip_if_not_installed('faahKO',  minimum_version = '1.18.0')
library(faahKO)

# remove Rplots.pdf created by ggplot2
on.exit( tryCatch({ file.remove('./Rplots.pdf') }, error=function(e){ invisible() }, warning=function(w){ invisible() }) )


## 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, FALSE, 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('default output, with plots and colours, serial, verbose, no verbose', {
  # temporary file
  savePath1         <- tempdir()
  # clear temp folder
  suppressWarnings(do.call(file.remove, list(list.files(savePath1, full.names = TRUE))))
  
  # input
  input_annotation  <- filledAnnotation
  input_colour      <- c('blue', 'green', 'red')
  
  # expected
  expected_path_CSV   <- file.path(savePath1, "annotationParameters_summary.csv")
  expected_path_plot1 <- file.path(savePath1, "cpd_1.png")
  expected_path_plot2 <- file.path(savePath1, "cpd_2.png")
  expected_CSV        <- data.frame(matrix(nrow=2,ncol=21,dimnames=list(c(), c('cpdID', 'cpdName', 'X', 'ROI_rt', 'ROI_mz','ROI_rtMin', 'ROI_rtMax', 'ROI_mzMin', 'ROI_mzMax', 'X', 'uROI_rtMin', 'uROI_rtMax', 'uROI_mzMin', 'uROI_mzMax', 'uROI_rt', 'uROI_mz', 'X', 'FIR_rtMin', 'FIR_rtMax', 'FIR_mzMin', 'FIR_mzMax'))))
  expected_CSV[1,]    <- c('ID-1', 'Cpd 1', '|', 3344.888, 522.2, 3310., 3390., 522.194778, 522.205222, '|', 9., 11., 12., 14., 10., 13., '|',  1., 2., 3., 4.)
  expected_CSV[2,]    <- c('ID-2', 'Cpd 2', '|', 3385.577, 496.2, 3280., 3440., 496.195038, 496.204962, '|', 15., 17., 18., 20., 16., 19., '|', 5., 6., 7., 8.)
  expected_CSV[,-c(1,2,3,10,17)]  <- sapply(expected_CSV[,-c(1,2,3,10,17)], as.numeric)
  
  # results (output, warnings and messages)
  result_save     <- evaluate_promise(outputAnnotationDiagnostic(input_annotation, saveFolder=savePath1, savePlots=TRUE, sampleColour=input_colour, verbose=TRUE, ncores=0))
  
  # Check CSV has been produced
  expect_true(file.exists(expected_path_CSV))
  # Check plot have been saved
  expect_true(file.exists(expected_path_plot1))
  expect_true(file.exists(expected_path_plot2))
  
  # Check values saved
  saved_CSV       <- read.csv(expected_path_CSV, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
  expect_equal(saved_CSV, expected_CSV)
  
  # Check result messages (save path)
  expect_equal(length(result_save$messages), 4)
  
  
  ## no verbose
  savePath2       <- tempdir()
  # clear temp folder
  suppressWarnings(do.call(file.remove, list(list.files(savePath2, full.names = TRUE))))
  result_save2    <- evaluate_promise(outputAnnotationDiagnostic(input_annotation, saveFolder=savePath2, savePlots=TRUE, sampleColour=input_colour, verbose=FALSE, ncores=0)
  )
  expect_equal(length(result_save2$messages), 0)
})

test_that('default output, with plots and colours, parallel, verbose, no verbose', {
  # temporary file
  savePath3         <- tempdir()
  # clear temp folder
  suppressWarnings(do.call(file.remove, list(list.files(savePath3, full.names = TRUE))))
  
  # input
  input_annotation  <- filledAnnotation
  input_colour      <- c('blue', 'green', 'red')
  
  # expected
  expected_path_CSV   <- file.path(savePath3, "annotationParameters_summary.csv")
  expected_path_plot1 <- file.path(savePath3, "cpd_1.png")
  expected_path_plot2 <- file.path(savePath3, "cpd_2.png")
  expected_CSV        <- data.frame(matrix(nrow=2,ncol=21,dimnames=list(c(), c('cpdID', 'cpdName', 'X', 'ROI_rt', 'ROI_mz','ROI_rtMin', 'ROI_rtMax', 'ROI_mzMin', 'ROI_mzMax', 'X', 'uROI_rtMin', 'uROI_rtMax', 'uROI_mzMin', 'uROI_mzMax', 'uROI_rt', 'uROI_mz', 'X', 'FIR_rtMin', 'FIR_rtMax', 'FIR_mzMin', 'FIR_mzMax'))))
  expected_CSV[1,]    <- c('ID-1', 'Cpd 1', '|', 3344.888, 522.2, 3310., 3390., 522.194778, 522.205222, '|', 9., 11., 12., 14., 10., 13., '|',  1., 2., 3., 4.)
  expected_CSV[2,]    <- c('ID-2', 'Cpd 2', '|', 3385.577, 496.2, 3280., 3440., 496.195038, 496.204962, '|', 15., 17., 18., 20., 16., 19., '|', 5., 6., 7., 8.)
  expected_CSV[,-c(1,2,3,10,17)]  <- sapply(expected_CSV[,-c(1,2,3,10,17)], as.numeric)
  
  # results (output, warnings and messages)
  result_save3    <- evaluate_promise(outputAnnotationDiagnostic(input_annotation, saveFolder=savePath3, savePlots=TRUE, sampleColour=input_colour, verbose=TRUE, ncores=1))
  
  # Check CSV has been produced
  expect_true(file.exists(expected_path_CSV))
  # Check plot have been saved
  expect_true(file.exists(expected_path_plot1))
  expect_true(file.exists(expected_path_plot2))
  
  # Check values saved
  saved_CSV       <- read.csv(expected_path_CSV, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
  expect_equal(saved_CSV, expected_CSV)
  
  # Check result messages (save path)
  expect_equal(length(result_save3$messages), 3)
  
  
  ## no verbose
  savePath4       <- tempdir()
  # clear temp folder
  suppressWarnings(do.call(file.remove, list(list.files(savePath4, full.names = TRUE))))
  result_save4    <- evaluate_promise(outputAnnotationDiagnostic(input_annotation, saveFolder=savePath4, savePlots=TRUE, sampleColour=input_colour, verbose=FALSE, ncores=1)
  )
  expect_equal(length(result_save4$messages), 0)
})

test_that('no plot saved, verbose', {
  # temporary file
  savePath5         <- tempdir()
  # clear temp folder
  suppressWarnings(do.call(file.remove, list(list.files(savePath5, full.names = TRUE))))
  
  # input
  input_annotation  <- filledAnnotation

  # expected
  expected_path_CSV   <- file.path(savePath5, "annotationParameters_summary.csv")
  expected_path_plot1 <- file.path(savePath5, "cpd_1.png")
  expected_path_plot2 <- file.path(savePath5, "cpd_2.png")
  expected_CSV        <- data.frame(matrix(nrow=2,ncol=21,dimnames=list(c(), c('cpdID', 'cpdName', 'X', 'ROI_rt', 'ROI_mz','ROI_rtMin', 'ROI_rtMax', 'ROI_mzMin', 'ROI_mzMax', 'X', 'uROI_rtMin', 'uROI_rtMax', 'uROI_mzMin', 'uROI_mzMax', 'uROI_rt', 'uROI_mz', 'X', 'FIR_rtMin', 'FIR_rtMax', 'FIR_mzMin', 'FIR_mzMax'))))
  expected_CSV[1,]    <- c('ID-1', 'Cpd 1', '|', 3344.888, 522.2, 3310., 3390., 522.194778, 522.205222, '|', 9., 11., 12., 14., 10., 13., '|',  1., 2., 3., 4.)
  expected_CSV[2,]    <- c('ID-2', 'Cpd 2', '|', 3385.577, 496.2, 3280., 3440., 496.195038, 496.204962, '|', 15., 17., 18., 20., 16., 19., '|', 5., 6., 7., 8.)
  expected_CSV[,-c(1,2,3,10,17)]  <- sapply(expected_CSV[,-c(1,2,3,10,17)], as.numeric)
  
  # results (output, warnings and messages)
  result_save5     <- evaluate_promise(outputAnnotationDiagnostic(input_annotation, saveFolder=savePath5, savePlots=FALSE, verbose=TRUE))
  
  # Check CSV has been produced
  expect_true(file.exists(expected_path_CSV))
  # Check plot have not been saved
  expect_false(file.exists(expected_path_plot1))
  expect_false(file.exists(expected_path_plot2))
  
  # Check values saved
  saved_CSV       <- read.csv(expected_path_CSV, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
  expect_equal(saved_CSV, expected_CSV)
  
  # Check result messages (save path)
  expect_equal(length(result_save5$messages), 1)
})

test_that('no data to plot, serial, verbose', {
  # temporary file
  savePath6         <- tempdir()
  # clear temp folder
  suppressWarnings(do.call(file.remove, list(list.files(savePath6, full.names = TRUE))))
  
  # input
  input_annotation  <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable)
  
  # expected
  expected_path_CSV   <- file.path(savePath6, "annotationParameters_summary.csv")
  expected_path_plot1 <- file.path(savePath6, "cpd_1.png")
  expected_path_plot2 <- file.path(savePath6, "cpd_2.png")
  expected_CSV        <- data.frame(matrix(nrow=2,ncol=21,dimnames=list(c(), c('cpdID', 'cpdName', 'X', 'ROI_rt', 'ROI_mz','ROI_rtMin', 'ROI_rtMax', 'ROI_mzMin', 'ROI_mzMax', 'X', 'uROI_rtMin', 'uROI_rtMax', 'uROI_mzMin', 'uROI_mzMax', 'uROI_rt', 'uROI_mz', 'X', 'FIR_rtMin', 'FIR_rtMax', 'FIR_mzMin', 'FIR_mzMax'))))
  expected_CSV[1,]    <- c('ID-1', 'Cpd 1', '|', 3344.888, 522.2, 3310., 3390., 522.194778, 522.205222, '|', NA, NA, NA, NA, NA, NA, '|', NA, NA, NA, NA)
  expected_CSV[2,]    <- c('ID-2', 'Cpd 2', '|', 3385.577, 496.2, 3280., 3440., 496.195038, 496.204962, '|', NA, NA, NA, NA, NA, NA, '|', NA, NA, NA, NA)
  expected_CSV[,-c(1,2,3,10:21)]  <- sapply(expected_CSV[,-c(1,2,3,10:21)], as.numeric)
  expected_CSV[,c(11:16, 18:21)]  <- sapply(expected_CSV[,c(11:16, 18:21)], as.logical)
  expected_message    <- c("Saving diagnostic plots:\n", "Warning: the object has not been annotated, return an empty diagnostic plot list\n", "  No plot to save for compound 1/2\n", "Warning: the object has not been annotated, return an empty diagnostic plot list\n", "  No plot to save for compound 2/2\n" )
  
  # results (output, warnings and messages)
  result_save6   <- evaluate_promise(outputAnnotationDiagnostic(input_annotation, saveFolder=savePath6, savePlots=TRUE, verbose=TRUE, ncores=0))
  
  # Check CSV has been produced
  expect_true(file.exists(expected_path_CSV))
  # Check plot have not been saved
  expect_false(file.exists(expected_path_plot1))
  expect_false(file.exists(expected_path_plot2))
  
  # Check values saved
  saved_CSV       <- read.csv(expected_path_CSV, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
  expect_equal(saved_CSV, expected_CSV)
  
  # Check result messages (without save path)
  expect_equal(length(result_save6$messages), 6)
  expect_equal(result_save6$messages[2:6], expected_message)
})

test_that('no data to plot, parallel, verbose', {
  ## 'No plot to save' message cannot come back from the parallel backend
  
  # temporary file
  savePath7         <- tempdir()
  # clear temp folder
  suppressWarnings(do.call(file.remove, list(list.files(savePath7, full.names = TRUE))))
  
  # input
  input_annotation  <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_targetFeatTable)
  
  # expected
  expected_path_CSV   <- file.path(savePath7, "annotationParameters_summary.csv")
  expected_path_plot1 <- file.path(savePath7, "cpd_1.png")
  expected_path_plot2 <- file.path(savePath7, "cpd_2.png")
  expected_CSV        <- data.frame(matrix(nrow=2,ncol=21,dimnames=list(c(), c('cpdID', 'cpdName', 'X', 'ROI_rt', 'ROI_mz','ROI_rtMin', 'ROI_rtMax', 'ROI_mzMin', 'ROI_mzMax', 'X', 'uROI_rtMin', 'uROI_rtMax', 'uROI_mzMin', 'uROI_mzMax', 'uROI_rt', 'uROI_mz', 'X', 'FIR_rtMin', 'FIR_rtMax', 'FIR_mzMin', 'FIR_mzMax'))))
  expected_CSV[1,]    <- c('ID-1', 'Cpd 1', '|', 3344.888, 522.2, 3310., 3390., 522.194778, 522.205222, '|', NA, NA, NA, NA, NA, NA, '|', NA, NA, NA, NA)
  expected_CSV[2,]    <- c('ID-2', 'Cpd 2', '|', 3385.577, 496.2, 3280., 3440., 496.195038, 496.204962, '|', NA, NA, NA, NA, NA, NA, '|', NA, NA, NA, NA)
  expected_CSV[,-c(1,2,3,10:21)]  <- sapply(expected_CSV[,-c(1,2,3,10:21)], as.numeric)
  expected_CSV[,c(11:16, 18:21)]  <- sapply(expected_CSV[,c(11:16, 18:21)], as.logical)
  
  # results (output, warnings and messages)
  result_save7     <- evaluate_promise(outputAnnotationDiagnostic(input_annotation, saveFolder=savePath7, savePlots=TRUE, verbose=TRUE, ncores=1))
  
  # Check CSV has been produced
  expect_true(file.exists(expected_path_CSV))
  # Check plot have not been saved
  expect_false(file.exists(expected_path_plot1))
  expect_false(file.exists(expected_path_plot2))
  
  # Check values saved
  saved_CSV       <- read.csv(expected_path_CSV, header=TRUE, sep=",", quote="\"", stringsAsFactors=FALSE)
  expect_equal(saved_CSV, expected_CSV)
  
  # Check result messages (without save path)
  expect_equal(length(result_save7$messages), 3)
})