CNVPanelizer
Reliable CNV detection in targeted sequencing applications

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inst/unitTests/test_testing.R

inst/unitTests/test_testing.R
In CNVPanelizer: Reliable CNV detection in targeted sequencing applications 

# test.descriptiveStatistics <- function() {
# 	distribution <- c(1, 2, 3, 4, 5)
# 	myDescriptiveStatistics <- DescriptiveStatistics(distribution)
# 	checkEquals(myDescriptiveStatistics$centralTendency == 3)
# 	checkEquals(myDescriptiveStatistics$variability == 1.581139)
# }
#
# test.descriptiveStatisticsRobust <- function() {
# 	distribution <- c(1, 2, 3, 4, 5)
# 	myDescriptiveStatistics <- DescriptiveStatistics(distribution, robust = TRUE)
# 	checkEquals(myDescriptiveStatistics$centralTendency == 3)
# 	checkEquals(myDescriptiveStatistics$variability == 1)
# }

test.bootListForSamplasAsNonMatrix <- function() {
  kNumberOfReferenceMatrixLines <- 4
  kNumberOfReferenceMatrixColumns <- 4
  kNumberOfElements <- kNumberOfReferenceMatrixLines * kNumberOfReferenceMatrixColumns
  kNumberOfReferenceMatrixColumns
  referenceValuesReadCounts <- 1:kNumberOfElements
  # Reference read counts matrix
  referenceReadCounts <- matrix(referenceValuesReadCounts,
                                nrow = kNumberOfReferenceMatrixLines,
                                ncol = kNumberOfReferenceMatrixColumns,
                                byrow = FALSE)
  # TODO this is the important part of the test.. maybe is possible to reduce code duplication
  sampleReadCounts <- 1:kNumberOfReferenceMatrixLines
  # colnames(sampleReadCounts) <- paste0("c:/somefile", 1, ".bam")
  # amplicons indexes for each gene
  genesPositionsIndex = list("gene_1" = c(1),
                             "gene_2" = c(2, 3, 4))
  geneNames <- c("GENE1",rep("GENE2",3))
  set.seed(1)
  kNumberOfReplicates <- 1
  #getOption("RUnit")$silent
  checkException(BootList(geneNames,
                          sampleReadCounts,
                          referenceReadCounts,
                          replicates = kNumberOfReplicates),
                 silent = TRUE)
}

test.bootListForSamplasAsMatrixWithNonNamedColumns <- function() {
  kNumberOfReferenceMatrixLines <- 4
  kNumberOfReferenceMatrixColumns <- 4
  kNumberOfElements <- kNumberOfReferenceMatrixLines *
    kNumberOfReferenceMatrixColumns
  referenceValuesReadCounts <- 1:kNumberOfElements
  # Reference read counts matrix
  referenceReadCounts <- matrix(referenceValuesReadCounts,
                                nrow = kNumberOfReferenceMatrixLines,
                                ncol = kNumberOfReferenceMatrixColumns,
                                byrow = FALSE)
  # TODO this is the important part of the test.. maybe is possible to reduce code duplication
  sampleReadCounts <- matrix(1:kNumberOfReferenceMatrixLines)
  #	colnames(sampleReadCounts) <- paste0("c:/somefile", 1, ".bam")

  # amplicons indexes for each gene
  genesPositionsIndex = list("gene_1" = c(1),
                             "gene_2" = c(2, 3, 4))

  geneNames <- c("GENE1",rep("GENE2",3))

  set.seed(1)
  kNumberOfReplicates <- 1

  #getOption("RUnit")$silent
  checkException(BootList(geneNames,
                          sampleReadCounts,
                          referenceReadCounts,
                          replicates = kNumberOfReplicates),
                 silent = TRUE)
}





test.bootList <- function() {
  kNumberOfReferenceMatrixLines <- 4
  kNumberOfReferenceMatrixColumns <- 4
  kNumberOfElements <- kNumberOfReferenceMatrixLines *
    kNumberOfReferenceMatrixColumns
  referenceValuesReadCounts <- 1:kNumberOfElements
  # Reference read counts matrix
  referenceReadCounts <- matrix(referenceValuesReadCounts,
                                nrow = kNumberOfReferenceMatrixLines,
                                ncol = kNumberOfReferenceMatrixColumns,
                                byrow = FALSE)
  sampleReadCounts <- matrix(1:kNumberOfReferenceMatrixLines)

  colnames(sampleReadCounts) <- paste0("c:/somefile", 1, ".bam")

  # amplicons indexes for each gene
  genesPositionsIndex = list("gene_1" = c(1),
                             "gene_2" = c(2, 3, 4))

  geneNames <- c("GENE1",rep("GENE2",3))

  set.seed(1)
  kNumberOfReplicates <- 10
  bootList = BootList(geneNames,
                      sampleReadCounts,
                      referenceReadCounts,
                      replicates = kNumberOfReplicates)

  #    resultForGENE1 <- bootList[[1]]["GENE1"][[1]]
  #    resultForGENE2 <- bootList[[1]]["GENE2"][[1]]

  resultForGENE1 <- bootList[[1]][1, "GENE1"]
  resultForGENE2 <- bootList[[1]][1, "GENE2"]

  print(paste("GENE 1: " , resultForGENE1))
  print(paste("GENE 2: " , resultForGENE2))

  tolerance <- 0.0001
  checkEquals(resultForGENE1, 0.16667,
              checkNames = FALSE,
              tolerance = tolerance)
  checkEquals(resultForGENE2, 0.35634,
              checkNames = FALSE,
              tolerance = tolerance)
}

test.NormalizeCountsMethodTSS <- function() {
  myMatrix = matrix(c(10, 10, 80, 30, 30, 0), nrow=3, ncol=2)
  myMatrixNormalized = NormalizeCounts(myMatrix, method = "tss")
  checkEquals(myMatrixNormalized[1,1], 0.1)
  checkEquals(myMatrixNormalized[2,1], 0.1)
  checkEquals(myMatrixNormalized[3,1], 0.8)
  checkEquals(myMatrixNormalized[1,2], 0.5)
  checkEquals(myMatrixNormalized[2,2], 0.5)
  checkEquals(myMatrixNormalized[3,2], 0.0)
}

test.NormalizeCountsMethodTMM <- function() {
  myMatrix = matrix(c(10, 10, 80, 20, 20, 160), nrow=3, ncol=2)
  myMatrixNormalized = NormalizeCounts(myMatrix, method = "tmm")
  checkEquals(myMatrixNormalized[1,1], 15)
  checkEquals(myMatrixNormalized[2,1], 15)
  checkEquals(myMatrixNormalized[3,1], 120)
  checkEquals(myMatrixNormalized[1,2], 15)
  checkEquals(myMatrixNormalized[2,2], 15)
  checkEquals(myMatrixNormalized[3,2], 120)
}


test.ReportTablesWithSingleSample <- function() {
  kNumberOfReferenceMatrixLines <- 4
  kNumberOfReferenceMatrixColumns <- 4
  kNumberOfElements <- kNumberOfReferenceMatrixLines *
    kNumberOfReferenceMatrixColumns
  referenceValuesReadCounts <- 1:kNumberOfElements
  # Reference read counts matrix
  referenceReadCounts <- matrix(referenceValuesReadCounts,
                                nrow = kNumberOfReferenceMatrixLines,
                                ncol = kNumberOfReferenceMatrixColumns,
                                byrow = FALSE)
  sampleReadCounts <- matrix(1:kNumberOfReferenceMatrixLines)

  colnames(sampleReadCounts) <- paste0("c:/somefile", 1, ".bam")

  # amplicons indexes for each gene
  genesPositionsIndex = list("gene_1" = c(1),
                             "gene_2" = c(2, 3, 4))

  geneNames <- c("GENE1",rep("GENE2",3))

  set.seed(1)
  kNumberOfReplicates <- 1000
  bootList = BootList(geneNames,
                      sampleReadCounts,
                      referenceReadCounts,
                      replicates = kNumberOfReplicates)
  myAmpliconNames <- c("amp1", "amp2", "amp3", "amp4")
  normalizedReadCountsWithAmpliconNames <- CombinedNormalizedCounts(sampleReadCounts,
                                                                    referenceReadCounts,
                                                                    ampliconNames = myAmpliconNames)

  checkEquals(rownames(normalizedReadCountsWithAmpliconNames$samples),
              myAmpliconNames)
  checkEquals(rownames(normalizedReadCountsWithAmpliconNames$reference),
              myAmpliconNames)
}

test.RobustTRUE <- function() {
  kNumberOfReferenceMatrixLines <- 4
  kNumberOfReferenceMatrixColumns <- 4
  kNumberOfElements <- kNumberOfReferenceMatrixLines *
    kNumberOfReferenceMatrixColumns
  referenceValuesReadCounts <- 1:kNumberOfElements
  # Reference read counts matrix
  referenceReadCounts <- matrix(referenceValuesReadCounts,
                                nrow = kNumberOfReferenceMatrixLines,
                                ncol = kNumberOfReferenceMatrixColumns,
                                byrow = FALSE)
  sampleReadCounts <- matrix(1:kNumberOfReferenceMatrixLines)

  colnames(sampleReadCounts) <- paste0("c:/somefile", 1, ".bam")

  # amplicons indexes for each gene
  genesPositionsIndex = list("gene_1" = c(1),
                             "gene_2" = c(2, 3, 4))

  geneNames <- c("GENE1",rep("GENE2",3))

  set.seed(1)
  kNumberOfReplicates <- 1000
  bootList = BootList(geneNames,
                      sampleReadCounts,
                      referenceReadCounts,
                      replicates = kNumberOfReplicates)

  normalizedReadCounts <- CombinedNormalizedCounts(sampleReadCounts, referenceReadCounts)

  samplesNormalizedReadCounts = normalizedReadCounts["samples"][[1]]
  referenceNormalizedReadCounts = normalizedReadCounts["reference"][[1]]

  backgroundNoise <- Background(geneNames,
                                samplesNormalizedReadCounts,
                                referenceNormalizedReadCounts,
                                bootList,
                                replicates = kNumberOfReplicates,
                                robust = TRUE)

  tolerance <- 0.00001
  checkTrue(all.equal(backgroundNoise[[1]]$`1`["LowerNoise"], c(LowerNoise=0.5401818), tolerance = tolerance))
  checkTrue(all.equal(backgroundNoise[[1]]$`1`["MeanNoise"], c(MeanNoise=0.5401818), tolerance = tolerance))
  checkTrue(all.equal(backgroundNoise[[1]]$`1`["UpperNoise"], c(UpperNoise=0.5401818), tolerance = tolerance))
  checkTrue(all.equal(backgroundNoise[[1]]$`3`["LowerNoise"], c(LowerNoise=0.7654648), tolerance = tolerance))
  checkTrue(all.equal(backgroundNoise[[1]]$`3`["MeanNoise"], c(MeanNoise=0.8365187), tolerance = tolerance))
  checkTrue(all.equal(backgroundNoise[[1]]$`3`["UpperNoise"], c(UpperNoise=0.9141681), tolerance = tolerance))
}

test.PlotBootstrap <- function() {
  referenceReadCounts <- as.matrix(read.table(header = TRUE, text = "
                                              r1 r2 r3 r4
                                              gene1_1 1 2 1 2
                                              gene2_1 3 2 2 2
                                              gene2_2 1 3 3 1
                                              gene3_1 2 3 1 2
                                              gene3_2 2 2 2 1
                                              gene3_3 1 1 2 2
                                              "))

  sampleReadCounts <- as.matrix(read.table(header = TRUE, text = "
                                           s1 s2 s3 s4
                                           gene1_1 2 5 2 1
                                           gene2_1 2 2 2 4
                                           gene2_2 2 3 2 4
                                           gene3_1 2 4 3 6
                                           gene3_2 2 5 2 6
                                           gene3_3 1 6 2 6
                                           "))

  ampliconNames <- rownames(sampleReadCounts)
  # it should be the same as they should be generated from the same bed file
  #ampliconNames <- rownames(referenceReadCounts)

  listOfAmpliconNames <- strsplit(ampliconNames, split="_")
  geneNames  <- unlist(listOfAmpliconNames)[ c(TRUE, FALSE) ]

  set.seed(1)
  kNumberOfReplicates <- 10000
  bootList = BootList(geneNames,
                      sampleReadCounts,
                      referenceReadCounts,
                      replicates = kNumberOfReplicates)

  normalizedReadCounts <- CombinedNormalizedCounts(sampleReadCounts, referenceReadCounts)

  samplesNormalizedReadCounts = normalizedReadCounts["samples"][[1]]
  referenceNormalizedReadCounts = normalizedReadCounts["reference"][[1]]

  backgroundNoise <- Background(geneNames,
                                samplesNormalizedReadCounts,
                                referenceNormalizedReadCounts,
                                bootList,
                                replicates = kNumberOfReplicates)


  # 	backgroundNoise <- Background(geneNames,
  # 	                              sampleReadCounts,
  # 	                              referenceReadCounts,
  # 	                              bootList,
  # 	                              replicates = 10)


  reportTables <- ReportTables(geneNames,
                               samplesNormalizedReadCounts,
                               referenceNormalizedReadCounts,
                               bootList,
                               backgroundNoise)

  # From the first sample no gene had either nonReliable or Reliable changes
  checkEquals(reportTables[[1]][,"Passed"], c(0, 0, 0))

  # From the second sample only the first and third gene had Reliable changes, second gene had noChange
  checkEquals(reportTables[[2]][,"Passed"], c(2, 0, 2))

  gene3NonReliableChange <- read.table(header = TRUE, text = "
                                       Signif. AboveNoise
                                       gene1   FALSE      FALSE
                                       gene2   FALSE      FALSE
                                       gene3    TRUE      FALSE")
  checkEquals(reportTables[[3]][,c("Signif.","AboveNoise")], gene3NonReliableChange)

  gene1NoChange_gene2NonReliableChange_gene3ReliableChange <- read.table(
    header = TRUE, text = "
    Signif. AboveNoise
    gene1   FALSE      FALSE
    gene2    TRUE      FALSE
    gene3    TRUE       TRUE")
  checkEquals(reportTables[[4]][,c("Signif.","AboveNoise")],
              gene1NoChange_gene2NonReliableChange_gene3ReliableChange)

  PlotBootstrapDistributions(bootList,
                             reportTables,
                             outputFolder = tempdir(),
                             save=TRUE)

  sampleNames <- paste0("sample", 1:4)
  PlotBootstrapDistributions(bootList,
                             reportTables,
                             outputFolder = tempdir(),
                             sampleNames = sampleNames,
                             save=TRUE)

}

#' test.BedToGenomicRanges <- function() {
#'
#' 	bedContent <- 'track name="IAD34679_Design" description="CoveredBases_AmpliSeqID_IAD34679" type=bedDetail color=77,175,74 priority=2 AmpliSeq_Version=1.2.9
#' 	chr1	27056172	27056291	GENE11	.	AMPL3573652750
#' 	chr1	27057875	27058001	GENE11	.	AMPL3498925712
#' 	chr1	27059149	27059276	GENE11	.	AMPL3573725051
#' 	chr2	27088638	27088749	GENE21	.	AMPL414842062
#' 	chr2	27100836	27100964	GENE21	.	AMPL3573753863
#' 	chr2	27101392	27101517	GENE22	.	AMPL3573628614
#' 	chr3	27105485	27105614	GENE31	.	AMPL414126459
#' 	chr3	27105839	27105963	GENE31	.	AMPL414225948
#' 	chr3	27106283	27106414	GENE31	.	AMPL4828584845
#' 	chrX	27106751	27106852	GENEX1	.	AMPL3573650239
#' 	chrX	115252187	115252313	GENEX1	.	AMPL4713167298
#' 	chrX	115256484	115256587	GENEX2	.	AMPL389446477
#' 	chrX	115258676	115258805	GENEX2	.	AMPL389117292
#' 	chrY	48023081	48023207	GENEY1	.	AMPL3562810493'
#'
#' 	bedFilepath <- file.path(tempdir(), "myBed.bed")
#' 	writeLines(bedContent, bedFilepath)
#'
#' 	genomicRangesFromBed <- BedToGenomicRanges(bedFilepath,
#' 																						 4,
#' 																						 split = "_",
#' 																						 dropChromossomes = c("chrX"))
#'
#' 	metadataFromGenomicRanges <- elementMetadata(genomicRangesFromBed)
#' 	geneNames = metadataFromGenomicRanges["geneNames"][,1]
#' 	ampliconNames = metadataFromGenomicRanges["ampliconNames"][,1]
#'
#' 	checkEquals(geneNames, c("GENE11",
#' 													 "GENE11",
#' 													 "GENE11",
#' 													 "GENE21",
#' 													 "GENE21",
#' 													 "GENE22",
#' 													 "GENE31",
#' 													 "GENE31",
#' 													 "GENE31",
#' 													 "GENEY1"))
#'
#'
#' 	#
#' 	# TODO Testing for doReduce = FALSE (should be in another test)
#' 	#
#' 	genomicRangesFromBed <- BedToGenomicRanges(bedFilepath,
#' 																						 4,
#' 																						 doReduce = FALSE,
#' 																						 split = "_",
#' 																						 dropChromossomes = c("chrX"))
#'
#' 	metadataFromGenomicRanges <- elementMetadata(genomicRangesFromBed)
#' 	geneNames = metadataFromGenomicRanges["geneNames"][,1]
#' 	ampliconNames = metadataFromGenomicRanges["ampliconNames"][,1]
#'
#' 	checkEquals(geneNames, c("GENE11",
#' 													 "GENE11",
#' 													 "GENE11",
#' 													 "GENE21",
#' 													 "GENE21",
#' 													 "GENE22",
#' 													 "GENE31",
#' 													 "GENE31",
#' 													 "GENE31",
#' 													 "GENEY1"))
#'
#' }
#'
#' # test.adjustedLength <- function() {
#' #   param <- c(1, 2, 3)
#' #   lengthParam <- length(param)
#' #   checkEquals(lengthParam, 3)
#' #   sqrtParam <- sqrt(lengthParam)
#' #   checkEquals(sqrtParam, 1.732051)
#' #   roundSqrtParam <- round(sqrtParam)
#' #   checkEquals(roundSqrtParam, 2)
#' # }
#' #
#' # test.ratiosMean <- function() {
#' #   checkEquals(ratiosMean(c(1, 2, 3)), 1.817121)
#' # }
#'
#' test.readCountsFromBam <- function() {
#' 	# https://www.biostars.org/p/150010/
#'
#' 	bedHeaderContent <- 'track name="IAD34679_Design" description="CoveredBases_AmpliSeqID_IAD34679" type=bedDetail color=77,175,74 priority=2 AmpliSeq_Version=1.2.9'
#' 	bedBodyContent <- 'chr1	1	100	GENE11	.	AMPL3573652750
#' 	chr1	300	1000	GENE11	.	AMPL3498925712
#' 	chr2	100	500	GENE21	.	AMPL414842062
#' 	chr2	1000	2000	GENE21	.	AMPL3573753863'
#' 	bedContentHasNoEmptySpaces <- !grepl(" ", bedBodyContent)
#' 	checkTrue(bedContentHasNoEmptySpaces, msg="The only alowed column separator is the TAB (\t), and white spaces were found at the bed file")
#' 	bedContent <- paste0(bedHeaderContent, "\n", bedBodyContent)
#' 	bedFilepath <- file.path(tempdir(), "myBed.bed")
#' 	writeLines(bedContent, bedFilepath)
#' 	genomicRangesFromBed <- BedToGenomicRanges(bedFilepath,
#' 																						 4,
#' 																						 split = "_")
#' 	samContent <- "@SQ	SN:chr1	LN:2000
#' 	@SQ	SN:chr2	LN:3000
#' 	r001	0	chr1	1	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r002	16	chr1	400	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r003	16	chr1	410	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r004	0	chr1	450	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r005	16	chr1	800	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r006	0	chr2	900	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r007	16	chr2	1001	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r008	0	chr2	1500	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r009	0	chr2	1900	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r010	0	chr2	2050	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK"
#' 	samContentHasNoEmptySpaces <- !grepl(" ", samContent)
#' 	checkTrue(samContentHasNoEmptySpaces,
#' 						msg="The only alowed separator is the TAB (\t), and white spaces were found at the sam file")
#'
#' 	samFilepath <- file.path(tempdir(), "mySam.sam")
#' 	writeLines(samContent, samFilepath)
#' 	bamFilepathWithoutExtension <- gsub(pattern = "\\.sam$", "", samFilepath)
#' 	bamFilepath <- paste0(bamFilepathWithoutExtension, ".bam")
#' 	asBam(samFilepath,
#' 				destination = bamFilepathWithoutExtension,
#' 				overwrite = TRUE)
#'
#' 	sampleFilename <- "example"
#' 	readCountsFromBam <- ReadCountsFromBam(c(bamFilepath),
#' 																				 sampleNames = sampleFilename,
#' 																				 genomicRangesFromBed,
#' 																				 #                    ampliconNames = NULL,
#' 																				 removeDup = FALSE)
#'
#' 	readCountsResult = matrix(c(1, 4, 0, 3),
#' 														nrow=4,
#' 														ncol=1)
#' 	colnames(readCountsResult) = sampleFilename
#' 	#TODO why cant i just to this.. checkEquals(readCountsFromBam, readCountsResult)
#' 	checkEquals(as.vector(readCountsFromBam), as.vector(readCountsResult))
#' }
#'
#' test.IndexMultipleBams <- function() {
#' 	temporaryDirectory <- tempdir()
#' 	samContent <- "@SQ	SN:chr1	LN:2000
#' 	@SQ	SN:chr2	LN:3000
#' 	r001	0	chr1	1	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r002	16	chr1	400	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r003	16	chr1	410	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r004	0	chr1	450	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r005	16	chr1	800	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r006	0	chr2	900	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r007	16	chr2	1001	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r008	0	chr2	1500	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r009	0	chr2	1900	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK
#' 	r010	0	chr2	2050	30	20M	*	0	0	NNNNNNNNNNNNNNNNNNNN	KKKKKKKKKKKKKKKKKKKK"
#' 	samContentHasNoEmptySpaces <- !grepl(" ", samContent)
#' 	checkTrue(samContentHasNoEmptySpaces,
#' 						msg="The only alowed separator is the TAB (\t), and white spaces were found at the sam file")
#'
#' 	samFilepath <- file.path(temporaryDirectory, "mySam.sam")
#' 	writeLines(samContent, samFilepath)
#' 	bamFilepathWithoutExtension <- gsub(pattern = "\\.sam$", "", samFilepath)
#' 	bamFilepath <- paste0(bamFilepathWithoutExtension, ".bam")
#' 	baiFilepath <- paste0(bamFilepathWithoutExtension, ".bam.bai")
#' 	asBam(samFilepath,
#' 				destination = bamFilepathWithoutExtension,
#' 				overwrite = TRUE,
#' 				indexDestination = FALSE)
#'
#' 	IndexMultipleBams(bamFilepath)
#'
#' 	checkTrue(file.exists(baiFilepath), msg = paste("bai file was not generated at", bamFilepath))
#' }

test.WriteListToXLSXandReadXLSXToList <- function() {
  temporaryDirectory <- tempdir()
  myDataFrame <- as.data.frame(matrix(1:9, 3, 3))
  dataFrameName <- "SomeDataFrame"
  myList <- list(myDataFrame)
  names(myList) <- dataFrameName
  filepath <- file.path(temporaryDirectory, "samples.xlsx")
  WriteListToXLSX(listOfDataFrames = myList, filepath = filepath)
  checkTrue(file.exists(filepath), msg = paste("xlsx file was not generated at", filepath))
  otherList <- ReadXLSXToList(filepath)
  checkEquals(names(myList), names(otherList))
  checkTrue(all(myList[[dataFrameName]] == otherList[[dataFrameName]]))
}

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CNVPanelizer documentation built on Nov. 8, 2020, 6:47 p.m.

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