Nothing
tests/testthat/test-deprecated.R
In GenVisR: Genomic Visualizations in R
context("Deprecated")
test_that("waterfall levels data correctly and as such data aligns", {
out <- waterfall(brcaMAF, plotGenes=c("PIK3CA", "TP53"), out="data")
expect_equal(levels(out[["main"]]$sample), levels(out[["mutation_count"]]$sample))
})
test_that("waterfall correctly counts mutations in a sample", {
# Obtain output
out <- waterfall(brcaMAF, plotGenes=c("PIK3CA", "TP53"), out="data")
out_A <- out$mutation[out$mutation_count$sample == "TCGA-A1-A0SI-01A-11D-A142-09" &
out$mutation_count$trv_type == "Synonymous",]
out_B <- out$mutation[out$mutation_count$sample == "TCGA-A1-A0SI-01A-11D-A142-09" &
out$mutation_count$trv_type == "Non Synonymous",]
# Calculate expected output
expec <- brcaMAF[brcaMAF$Tumor_Sample_Barcode=="TCGA-A1-A0SI-01A-11D-A142-09",]
expec_A <- nrow(expec[expec$Variant_Classification == "Silent" ,c(1, 9, 16)])
expec_B <- nrow(expec[expec$Variant_Classification != "Silent" ,c(1, 9, 16)])
expect_equal(out_A$mutation_total, expec_A)
expect_equal(out_B$mutation_total, expec_B)
})
test_that("waterfall_calcMutFreq correctly counts mutations", {
x <- data.frame(sample=c('a', 'a', 'a', 'b', 'b'), gene=rep('b', 5), trv_type=rep('silent', 5))
out <- waterfall_calcMutFreq(x)
expect_equal(out[out$sample == 'a' & out$trv_type == 'Synonymous', 'mutation_total'], 3)
expect_equal(out[out$sample == 'b' & out$trv_type == 'Synonymous', 'mutation_total'], 2)
expect_equal(out[out$sample == 'a' & out$trv_type == 'Non Synonymous', 'mutation_total'], 0)
expect_equal(out[out$sample == 'b' & out$trv_type == 'Non Synonymous', 'mutation_total'], 0)
})
test_that("waterfall_calcMutFreq correctly identifies Synonymous, Non Synonymous mutations", {
x <- data.frame(sample=c('a', 'a', 'a'), gene=rep('b', 3), trv_type=c('not silent', 'Silent', 'siLenT'))
out <- waterfall_calcMutFreq(x)
expect_equal(out[out$sample == 'a' & out$trv_type == 'Synonymous', 'mutation_total'], 2)
expect_equal(out[out$sample == 'a' & out$trv_type == 'Non Synonymous', 'mutation_total'], 1)
})
test_that("waterfall_geneAlt checks for proper input", {
x <- data.frame(sample='a', gene='b', trv_type='missense')
genes <- c(1)
expect_warning(waterfall_geneAlt(x, genes), "not a character vector")
})
test_that("waterfall_geneAlt checks that genes are available for removal", {
x <- data.frame(sample='a', gene='b', trv_type='missense')
genes <- c('z')
expect_warning(waterfall_geneAlt(x, genes), "element not found in x")
})
test_that("waterfall_geneAlt successfully removes genes from input", {
x <- data.frame(sample=c('a','a'), gene=c('b','c'), trv_type=rep('missense',2))
genes <- c('b')
out <- waterfall_geneAlt(x, genes)
expect_equal(nrow(out), 1)
})
test_that("waterfall_geneRecurCutoff properly detects if recurrence cutoff exceeds max recurrence", {
x <- data.frame(sample=factor(seq(1:11)), gene=c(rep(c('b', 'c'), 5), 'd'), trv_type=rep('missense', 11))
recurrence_cutoff <- .51
expect_warning(waterfall_geneRecurCutoff(x, recurrence_cutoff), "cutoff specified exceeds")
})
test_that("waterfall_geneRecurCutoff properly removes entries not meeting a recurrence cutoff", {
x <- data.frame(sample=factor(seq(1:11)), gene=c(rep(c('b', 'c'), 5), 'd'), trv_type=rep('missense', 11))
recurrence_cutoff <- .51
out <- suppressWarnings(waterfall_geneRecurCutoff(x, recurrence_cutoff))
expect_equal(nrow(out), 10)
})
test_that("waterfall_geneSort properly orders genes", {
x <- data.frame(sample=rep('a', 10), gene=c(rep(c('b', 'c', 'd'), 3), 'e'), trv_type=rep('missense', 10))
out <- waterfall_geneSort(x)
expec <- c('e', 'b', 'c', 'd')
expect_equal(out, expec)
})
test_that("waterfall_geneSort defers to input to geneOrder if specified by the user", {
x <- data.frame(sample=rep('a', 10), gene=c(rep(c('b', 'c', 'd'), 3), 'e'), trv_type=rep('missense', 10))
gene_order <- c("d", "b", "c", "e")
out <- waterfall_geneSort(x, geneOrder=gene_order)
expec <- rev(gene_order)
expect_equal(out, expec)
gene_order <- c("none", "b", "d")
expec <- c("d", "b")
out <- suppressWarnings(waterfall_geneSort(x, geneOrder=gene_order))
expect_equal(out, expec)
})
test_that("waterfall_geneSort warns if it finds genes supplied to geneOrder not in the primary input", {
# test case where no genes in geneOrder are in primary input
x <- data.frame(sample=rep('a', 10), gene=c(rep(c('b', 'c', 'd'), 3), 'e'), trv_type=rep('missense', 10))
gene_order <- c("none")
expect_warning(waterfall_geneSort(x, geneOrder=gene_order), "Did not find any genes")
# test case where some but not all genes in geneOrder are in primary input
x <- data.frame(sample=rep('a', 10), gene=c(rep(c('b', 'c', 'd'), 3), 'e'), trv_type=rep('missense', 10))
gene_order <- c("none", "b", "d")
expect_warning(waterfall_geneSort(x, geneOrder=gene_order), "The following genes were not found")
})
test_that("waterfall_MAF2anno checks for proper column names",{
# Check without label column
x <- data.frame(Tumor_Sample_Barcode='samp1', incorrect='egfr', Variant_Classification='missense')
label_col <- NULL
expect_error(waterfall_MAF2anno(x, label_col), "Did not detect")
# Check with label column
x <- data.frame(Tumor_Sample_Barcode='samp1', Hugo_Symbol='egfr', Variant_Classification='missense', incorrect='R504K')
label_col <- 'label'
expect_error(waterfall_MAF2anno(x, label_col), "Did not detect")
})
test_that("waterfall_MAF2anno output format is correct", {
# check without label column
x <- data.frame(Tumor_Sample_Barcode='samp1', Hugo_Symbol='egfr', Variant_Classification='missense')
label_col <- NULL
out <- waterfall_MAF2anno(x, label_col)
expect_equal(sort(colnames(out)), sort(c('sample', 'gene', 'trv_type')))
# check with label column
x <- data.frame(Tumor_Sample_Barcode='samp1', Hugo_Symbol='egfr', Variant_Classification='missense', label='R504K')
label_col <- 'label'
out <- waterfall_MAF2anno(x, label_col)
expect_equal(sort(colnames(out)), sort(c('sample', 'gene', 'trv_type', label_col)))
# Check class
expect_is(out, "data.frame")
})
test_that("waterfall_MGI2anno checks for proper column names",{
# Check without label column
x <- data.frame(incorrect='samp1', gene_name='egfr', trv_type='missense')
label_col <- NULL
expect_error(waterfall_MGI2anno(x, label_col), "Did not detect")
# Check with label column
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense', incorrect='R504K')
label_col <- 'label'
expect_error(waterfall_MGI2anno(x, label_col), "Did not detect")
})
test_that("waterfall_MGI2anno output format is correct", {
# check without label column
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
label_col <- NULL
out <- waterfall_MGI2anno(x, label_col)
expect_equal(sort(colnames(out)), sort(c('sample', 'gene', 'trv_type')))
# check with label column
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense', label='R504K')
label_col <- 'label'
out <- waterfall_MGI2anno(x, label_col)
expect_equal(sort(colnames(out)), sort(c('sample', 'gene', 'trv_type', label_col)))
# Check class
expect_is(out, "data.frame")
})
test_that("waterfall_NA2gene properly assigns NA values in the input to the top gene", {
x <- data.frame(sample=c('a', 'b', NA), gene=c('c', 'c', NA), trv_type=c('missense', 'missense', NA))
out <- waterfall_NA2gene(x)
expect_false(any(is.na(out$gene)))
})
test_that("waterfall_qual returns a list with three elements", {
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- NULL
z <- NULL
file_type <- 'MGI'
label_col <- NULL
out <- waterfall_qual(x, y, z, file_type, label_col)
expect_equal(length(out), 3)
expect_is(out, 'list')
})
test_that("waterfall_qual checks input to file_type is valid", {
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- NULL
z <- NULL
file_type <- 'INCORRECT'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col), "Unrecognized")
})
test_that("waterfall_qual verifies input is of proper class", {
# Check x
x <- c(sample='samp1', gene_name='egfr', trv_type='missense')
y <- NULL
z <- NULL
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
# Check y
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- c(sample='samp1', variable='gender', value='male')
z <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
# Check z
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- NULL
z <- c(sample='samp1', mut_burden='5')
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
})
test_that("waterfall_qual verifies correct columns are in input", {
# Check x
x <- data.frame(INCORRECT='samp1', gene_name='egfr', trv_type='missense',
chromosome_name=1, start=1, stop=1, reference="A",
variant="T")
y <- NULL
z <- NULL
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
# Check y
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- data.frame(incorrect='samp1', variable='gender', value='male')
z <- NULL
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- data.frame(sample='samp1', incorrect='gender', value='male')
z <- NULL
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- data.frame(sample='samp1', variable='gender', incorrect='male')
z <- NULL
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
# Check z
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- NULL
z <- c(incorrect='samp1', mut_burden='5')
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- NULL
z <- c(sample='samp1', incorrect='5')
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
})
test_that("waterfall_rmvSilent removes silent mutations", {
x <- data.frame(sample=rep('a', 3), gene=rep('b', 3), trv_type=c('Silent', 'siLenT', 'missense'))
out <- droplevels(waterfall_rmvSilent(x))
expec <- data.frame(sample=rep('a', 3), gene=c(NA, NA, 'b'), trv_type=c(NA, NA, 'missense'))
expect_equal(out, expec)
})
test_that("waterfall_sampAlt checks for proper class", {
x <- data.frame(sample='1', gene='egfr', trv_type='missense')
samples <- c(1)
expect_warning(waterfall_sampAlt(x, samples), "not a character vector")
})
test_that("waterfall_sampAlt correctly adds in samples not in the original data", {
x <- data.frame(sample='samp1', gene='egfr', trv_type='missense')
samples <- c('samp1', 'samp2')
out <- waterfall_sampAlt(x, samples)
expec <- data.frame(sample=as.factor(c('samp2', 'samp1')), gene=c(NA, 'egfr'), trv_type=c(NA, 'missense'))
expect_equal(out, expec)
})
test_that("waterfall_sampAlt correctly removes samples in the original data", {
x <- data.frame(sample=c('samp1', 'samp2'), gene=c('egfr', 'tp53'), trv_type=c('missense', 'nonsense'))
samples <- c('samp1')
out <- waterfall_sampAlt(x, samples)
expec <- data.frame(sample=as.factor('samp1'), gene='egfr', trv_type='missense')
expect_equal(out, expec)
})
test_that("waterfall_sampSort properly sorts samples based on a hierarchy", {
x <- data.frame(sample=c('a', 'a', 'a', 'b', 'b', 'c'), gene=c('x', 'y', 'z', 'x', 'y', 'z'), trv_type=rep('missense', 6))
out <- waterfall_sampSort(x)
expec <- c('a', 'c', 'b')
expect_equal(out, expec)
# Test that convert to boolean properly works on data frame with one gene
x <- data.frame(sample=c('a', 'b', 'c'), gene=c('x', 'x', 'x'), trv_type=rep('missense', 3))
out <- waterfall_sampSort(x)
expec <- c('a', 'b', 'c')
expect_equal(out, expec)
# Test that samples added in via the plotSamples parameter in waterfall are
# put last
x <- data.frame(sample=c('TESTA', 'A'), gene=c(NA, 'x'), trv_type=c(NA, 'missense'))
out <- waterfall_sampSort(x)
expec <- c('A', 'TESTA')
#expect_equal(out, expec)
})
test_that("waterfall_sampSort removes samples in sampOrder not found in primary input", {
x <- data.frame(sample=c('a', 'a', 'a', 'b', 'b', 'c'), gene=c('x', 'y', 'z', 'x', 'y', 'z'), trv_type=rep('missense', 6))
sample_order <- c("a", "b", "c", "d")
out <- suppressWarnings(waterfall_sampSort(x, sampOrder = sample_order))
expec <- c('a', 'b', 'c')
expect_equal(out, expec)
})
test_that("waterfall_hierarchyTRV leaves only one entry for a gene/mutation", {
# Check MGI file
x <- data.frame(sample=rep(c('a', 'b'), 4), gene=rep(c('c', 'd'), 4), trv_type=rep(c('nonsense', 'missense', 'rna', 'silent'), 2))
file_type <- 'MGI'
variant_class_order <- NULL
out <- waterfall_hierarchyTRV(x, file_type, variant_class_order)
expect_equal(nrow(out), 2)
expect_equal(as.character(out[out$sample == 'a' & out$gene == 'c', 'trv_type']), 'nonsense')
expect_equal(as.character(out[out$sample == 'b' & out$gene == 'd', 'trv_type']), 'missense')
# Check MAF file
x <- data.frame(sample=rep(c('a', 'b'), 4), gene=rep(c('c', 'd'), 4), trv_type=rep(c('Splice_Site', 'IGR', 'Silent', 'Nonsense_Mutation'), 2))
file_type <- 'MAF'
out <- waterfall_hierarchyTRV(x, file_type, variant_class_order)
expect_equal(nrow(out), 2)
expect_equal(as.character(out[out$sample == 'a' & out$gene == 'c', 'trv_type']), 'Splice_Site')
expect_equal(as.character(out[out$sample == 'b' & out$gene == 'd', 'trv_type']), 'Nonsense_Mutation')
})
test_that("waterfall_hierarchyTRV recognizes an invalid mutation type", {
# Check for MGI
x <- data.frame(sample=rep(c('a', 'b'), 4), gene=rep(c('c', 'd'), 4), trv_type=rep(c('missense', 'rna', 'silent', 'invalid'), 2))
file_type <- 'MGI'
variant_class_order <- NULL
expect_error(waterfall_hierarchyTRV(x, file_type, variant_class_order), "invalid mutation type")
# Check for MAF
x <- data.frame(sample=rep(c('a', 'b'), 4), gene=rep(c('c', 'd'), 4), trv_type=rep(c('Splice_Site', 'IGR', 'Silent', 'invalid'), 2))
file_type <- 'MAF'
expect_error(waterfall_hierarchyTRV(x, file_type, variant_class_order), "invalid mutation type")
})
test_that("TvTi_rmMnuc correctly identifies and removes multi-nucleotide codes", {
x <- data.frame("reference"=c("AA", "T"), "variant"=c("AT", "C"))
expect_equal(nrow(suppressWarnings(TvTi_rmMnuc(x))), 1)
x <- data.frame("reference"=c("gg", "t"), "variant"=c("cc", "g"))
expect_equal(nrow(suppressWarnings(TvTi_rmMnuc(x))), 1)
})
test_that("TvTi_rmMnuc warns if it finds multi-nucleotide codes", {
x <- data.frame("reference"=c("AA", "T"), "variant"=c("AT", "C"))
expect_warning(TvTi_rmMnuc(x), "multi-nucleotides present")
})
test_that("TvTi_calcTransTranvFreq adds in trans/tranv missing from samples", {
x <- data.frame(reference=c("A", "G"),
variant=c("T", "C"),
sample=c("samp1", "samp2"),
trans_tranv=c("A->T or T->A (TV)", "A->G or T->C (TI)"))
expect_equal(nrow(TvTi_calcTransTranvFreq(x)), 12)
})
test_that("TvTi_calcTransTranvFreq correctly calculates the proportions of TR/TV", {
x <- data.frame(reference=c("A", "G"),
variant=c("T", "C"),
sample=c("samp1", "samp1"),
trans_tranv=c("A->T or T->A (TV)", "A->G or T->C (TI)"))
out <- TvTi_calcTransTranvFreq(x)
expect_equal(out[out$trans_tranv == "A->G or T->C (TI)",]$Prop, .5)
x <- data.frame(reference=c("A", "G"),
variant=c("T", "C"),
sample=c("samp1", "samp2"),
trans_tranv=c("A->T or T->A (TV)", "A->G or T->C (TI)"))
out <- TvTi_calcTransTranvFreq(x)
expect_equal(sum(out$Prop), 2)
})
test_that("TvTi_calcTransTranvFreq correctly calculates the frequency of Tv/Ti", {
x <- data.frame(reference=c("A", "G"),
variant=c("T", "C"),
sample=c("samp1", "samp1"),
trans_tranv=c("A->T or T->A (TV)", "A->G or T->C (TI)"))
out <- TvTi_calcTransTranvFreq(x)
expect_equal(out[out$trans_tranv == "A->G or T->C (TI)",]$Freq, 1)
expect_equal(out[out$trans_tranv == "A->T or T->A (TV)",]$Freq, 1)
x <- data.frame(reference=c("A", "G"),
variant=c("T", "C"),
sample=c("samp1", "samp2"),
trans_tranv=c("A->T or T->A (TV)", "A->G or T->C (TI)"))
out <- TvTi_calcTransTranvFreq(x)
expect_equal(sum(out$Freq), 2)
})
test_that("TvTi_convMaf Detects Homozygous events and only counts them once", {
x <- data.frame(Tumor_Sample_Barcode=c("A"), Reference_Allele="C", Tumor_Seq_Allele1=c("T"), Tumor_Seq_Allele2=c("T"))
expect <- data.frame(sample=c("A"), reference=c("C"), variant=c("T"))
expect_equal(TvTi_convMaf(x), expect)
})
test_that("TvTi_convMaf eliminates calls where the reference allele equals the variant allele", {
x <- data.frame(Tumor_Sample_Barcode=c("A"), Reference_Allele="C", Tumor_Seq_Allele1=c("C"), Tumor_Seq_Allele2=c("C"))
expect_equal(nrow(TvTi_convMaf(x)), 0)
x <- data.frame(Tumor_Sample_Barcode=c("A"), Reference_Allele="C", Tumor_Seq_Allele1=c("T"), Tumor_Seq_Allele2=c("C"))
expect_equal(nrow(TvTi_convMaf(x)), 1)
x <- data.frame(Tumor_Sample_Barcode=c("A"), Reference_Allele="C", Tumor_Seq_Allele1=c("C"), Tumor_Seq_Allele2=c("T"))
expect_equal(nrow(TvTi_convMaf(x)), 1)
})
test_that("TvTi_qual correctly identifies invalid input to the fileType parameter", {
x <- data.frame(sample=c("a", "a"), reference=c("a", "a"), variant=c("c", "c"))
y <- NULL
file_type="INCORRECT"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "input to paramter fileType")
})
test_that("TvTi_qual correctly identifies if input to x is not of proper class", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
x <- as.matrix(x)
y <- NULL
file_type="MGI"
expect_warning(TvTi_qual(x, y=y, file_type=file_type), "not an object of class data frame")
})
test_that("TvTi_qual correctly identifies if duplicate rows are present in input to x", {
x <- data.frame(sample=c("a", "a"), reference=c("a", "a"), variant=c("c", "c"))
y <- NULL
file_type="MGI"
expect_warning(TvTi_qual(x, y=y, file_type=file_type), "Detected duplicate rows")
})
test_that("TvTi_qual correctly identifies if input to y is not of proper class", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- c("A->C or T->G (TV)"=1/6, "A->G or T->C (TI)"=1/6, "A->T or T->A (TV)"=1/6, "G->A or C->T (TI)"=1/6, "G->C or C->G (TV)"=1/6, "G->T or C->A (TV)"=1/6)
y <- as.matrix(y)
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "not an object of class data frame")
})
test_that("TvTi_qual Checks for proper column names if input to y is a data frame", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- data.frame(Prop=rep(1/6, 6), incorrect=rep("A->C or T->G (TV)", 6))
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "Did not detect correct column names")
})
test_that("TvTi_qual checks that proportion values are of numeric type", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- c("A->C or T->G (TV)"="1/6", "A->G or T->C (TI)"="1/6", "A->T or T->A (TV)"="1/6", "G->A or C->T (TI)"="1/6", "G->C or C->G (TV)"="1/6", "G->T or C->A (TV)"="1/6")
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "not of type double or numeric")
})
test_that("TvTi_qual recognizes if input to y is not of proper class", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- c("A->C or T->G (TV)"="1/6", "A->G or T->C (TI)"="1/6", "A->T or T->A (TV)"="1/6", "G->A or C->T (TI)"="1/6", "G->C or C->G (TV)"="1/6", "G->T or C->A (TV)"="1/6")
y <- as.matrix(y)
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "input to y is not")
})
test_that("TvTi_qual detects if proper column names are not supplied to x", {
x <- data.frame(incorrect=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- data.frame(Prop=rep(1/6, 6), trans_tranv=rep("A->C or T->G (TV)", 6))
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "not find all columns")
x <- data.frame(Tumor_Sample_Barcode=c("a", "b"), incorrect=c("a", "a"), Tumor_Seq_Allele1=c("a", "a"), Tumor_Seq_Allele2=c("a", "a"))
file_type <- "MAF"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "not find all columns")
})
test_that("TvTi_qual detects unexpected nucleotide codes in input supplied to x", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "I"), variant=c("g", "c"))
y <- data.frame(Prop=rep(1/6, 6), trans_tranv=rep("A->C or T->G", 6))
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "Unrecognized Base")
x <- data.frame(sample=c("a", "b"), reference=c("a", "I"), variant=c("I", "c"))
expect_error(TvTi_qual(x, y=y, file_type=file_type), "Unrecognized Base")
})
test_that("TvTi_qual checks for proper transition/transversion levels in input to y", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- data.frame(Prop=rep(1/6, 6), trans_tranv=rep("A->C or T->G (TV)", 6))
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "all combinations of transitions/transversions")
})
test_that("TvTi_qual checks that proportions supplied in y sum to one", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- c("A->C or T->G (TV)"=1/5, "A->G or T->C (TI)"=1/6, "A->T or T->A (TV)"=1/6, "G->A or C->T (TI)"=1/6, "G->C or C->G (TV)"=1/6, "G->T or C->A (TV)"=1/6)
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "should equal 1")
})
test_that("TvTi_qual identifies if argument supplied to z is not a data frame", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- NULL
z <- data.frame("sample"=c("x"), "variable"=c("y"), "value"=c("Z"))
z <- as.matrix(z)
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, z=z, file_type=file_type), "Did not detect")
})
test_that("TvTi_qual identifies if argument supplied to z contains incorrect column names", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- NULL
z <- data.frame("INCORRECT"=c("x"), "variable"=c("y"), "value"=c("Z"))
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, z=z, file_type=file_type), "Did not detect")
})
test_that("TvTi_qual identifies if samples supplied to argument x and z differ", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- NULL
z <- data.frame("sample"=c("x"), "variable"=c("y"), "value"=c("Z"))
file_type <- "MGI"
expect_warning(TvTi_qual(x, y=y, z=z, file_type=file_type), "Found a sample")
})
test_that("TvTi_rmIndel correctly identifies and removes indels", {
x <- data.frame("reference"=c('-', 'A'), "variant"=c('A', 'A'))
expect_equal(nrow(suppressWarnings(TvTi_rmIndel(x))), 1)
x <- data.frame("reference"=c('A', 'A'), "variant"=c('0', 'A'))
expect_equal(nrow(suppressWarnings(TvTi_rmIndel(x))), 1)
})
test_that("TvTi_rmIndel correctly warns if indels are detected", {
x <- data.frame("reference"=c('-', 'A'), "variant"=c('A', 'A'))
expect_message(TvTi_rmIndel(x), "indels present")
})
test_that("lolliplot_AA2sidechain, converts amino acid codes to sidechain information", {
x <- c("f")
out <- lolliplot_AA2sidechain(x)
expect_equal(out, "Nonpolar")
x <- c("F")
out <- lolliplot_AA2sidechain(x)
expect_equal(out, "Nonpolar")
})
test_that("lolliplot_Codon2AA Converts codons to amino acids", {
x <- c("TTG")
out <- lolliplot_Codon2AA(x)
expect_equal(out, "L")
x <- c("ttg")
out <- lolliplot_Codon2AA(x)
expect_equal(out, "L")
})
test_that("lolliplot_Codon2AA outputs NA if a codon is not recognized", {
x <- c("ZZZ")
out <- lolliplot_Codon2AA(x)
expect_equal(out, NULL)
})
test_that("lolliplot_constructGene properly nest's overlappping genomic features", {
domain_data <- data.frame(desc=c("domainA", "domainB"), start=c(5, 10), end=c(20, 15))
gene <- "test"
length <- 50
out <- lolliplot_constructGene(gene, domain_data, length)
expect_equivalent(out[out$Domain == 'domainA',]$nest, 1)
expect_equivalent(out[out$Domain == 'domainB',]$nest, 2)
expect_equivalent(out[out$Domain == 'test',]$nest, 1)
})
test_that("lolliplot_constructGene properly identifies if a domain exceeds the length of the protein", {
domain_data <- data.frame(desc=c("domainA"), start=c(5), end=c(20))
gene <- "test"
length <- 15
expect_warning(lolliplot_constructGene(gene, domain_data, length), "is exceeding the length")
})
test_that("lolliplot_constructGene properly identifies if a domain is before the start of the protein", {
domain_data <- data.frame(desc=c("domainA"), start=c(0), end=c(10))
gene <- "test"
length <- 15
expect_warning(lolliplot_constructGene(gene, domain_data, length), "less than the start of the protein")
})
test_that("lolliplot_constructGene identifies if a start position is greater than an end position", {
domain_data <- data.frame(desc=c("domainA"), start=c(10), end=c(5))
gene <- "test"
length <- 20
expect_warning(lolliplot_constructGene(gene, domain_data, length), "Found a start position greater")
})
test_that("lolliplot_DNAconv identifies if an incomplete codon is given in the sequence", {
x <- c("AAATT")
expect_warning(lolliplot_DNAconv(x), "not a multiple of three")
})
test_that("lolliplot_DNAconv splits the input character string into codon lengths", {
x <- c("AAATTT")
expect_equal(length(lolliplot_DNAconv(x)), 2)
})
test_that("lolliplot_DNAconv converts condons into residues", {
x <- c("AAA")
expect_equal(lolliplot_DNAconv(x), "K")
})
test_that("lolliplot_DNAconv converts residues into sidechains", {
x <- c("AAA")
expect_equal(lolliplot_DNAconv(x, to="sidechain"), "Basic")
})
test_that("lolliplot_DNAconv throws an warning if input to the to parameter is unrecognized", {
x <- c("AAA")
expect_warning(lolliplot_DNAconv(x, to="incorrect"), "did not recognize")
})
test_that("lolliplot_dodgeCoordX succesfully dodges coordinates as expected", {
x <- data.frame(x=c(1, 2), y=c(1, 1))
out <- lolliplot_dodgeCoordX(x)
expect_true(out[1] < 1)
expect_true(out[2] > 2)
x <- data.frame(x=c(1, 1), y=c(1, 1))
out <- lolliplot_dodgeCoordX(x)
expect_equal(out[1], 1)
})
test_that("lolliplot_dodgeCoordX does not apply a force field if length of vector is 1", {
x <- data.frame(x=c(1), y=c(1))
out <- lolliplot_dodgeCoordX(x)
expect_equal(out, 1)
})
test_that("lolliplot_dodgeCoordY succesfully dodges y coordinates as expected", {
x <- data.frame(coord_x_dodge=c(1, 2))
out <- lolliplot_dodgeCoordY(x, track="top")
expect_equal(out[1], out[2])
x <- data.frame(coord_x_dodge=c(1, 1))
out <- lolliplot_dodgeCoordY(x, track="top")
expect_true(out[1] != out[2])
expect_true(out[1] > 0)
out <- lolliplot_dodgeCoordY(x, track="bottom")
expect_true(out[1] != out[2])
expect_true(out[1] < 0)
})
test_that("lolliplot_mutationObs correctly removes input variants annotated as within an intron", {
x <- data.frame(gene=c("TP53", "TP53"), amino_acid_change=c('e0+2', 'p.K101N'), transcript_name=c("ENST00000269305", "ENST00000269305"))
expect_message(lolliplot_mutationObs(x,
track="top",
fill_value=NULL,
label_column=NULL,
rep.fact=5000,
rep.dist.lmt=500,
attr.fact=.1,
adj.max=.1,
adj.lmt=.5,
iter.max=10000), "not within a residue")
})
test_that("lolliiplot_mutationObs correctly identifies if there are no mutations to plot", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('e0+2'), transcript_name=c("ENST00000269305"))
expect_error(lolliplot_mutationObs(x,
track="top",
fill_value=NULL,
label_column=NULL,
rep.fact=5000,
rep.dist.lmt=500,
attr.fact=.1,
adj.max=.1,
adj.lmt=.5,
iter.max=10000), "Did not detect any residues")
})
test_that("lolliplot_mutationObs correctly extract coordinates from p. notation", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('p.F426A'), transcript_name=c("ENST00000269305"))
out <- lolliplot_mutationObs(x, track="top", fill_value=NULL, label_column=NULL,
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000)
expect_equal(out$mutation_coord, 426)
})
test_that("lolliplot_mutationObs correctly identifies amino acid changes in c. notation", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('c.F4260A'), transcript_name=c("ENST00000269305"))
expect_error(lolliplot_mutationObs(x, track="top", fill_value=NULL, label_column=NULL,
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000), "c. notation is not currently supported")
})
test_that("lolliplot_mutationObs correctly notifies if amino_acid_change notation is not recognized", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('4260'), transcript_name=c("ENST00000269305"))
expect_error(lolliplot_mutationObs(x, track="top", fill_value=NULL, label_column=NULL,
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000), "Could not determine notation type")
})
test_that("lolliplot_mutationObs correctly sets mutations y coord based on the track", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('p.F426A'), transcript_name=c("ENST00000269305"))
out <- lolliplot_mutationObs(x, track="bottom", fill_value=NULL, label_column=NULL,
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000)
expect_true(out$coord_y_dodge < 0)
out <- lolliplot_mutationObs(x, track="top", fill_value=NULL, label_column=NULL,
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000)
expect_true(out$coord_y_dodge > 0)
})
test_that("lolliplot_mutationObs correctly sets a specified label column", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('p.F426A'), transcript_name=c("ENST00000269305"), testA="blue", testB="red")
out <- lolliplot_mutationObs(x, track="bottom", fill_value=NULL, label_column="testA",
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000)
expect_equal(out$labels, "blue")
})
test_that("lolliplot_mutationOBs correctly sets a specified fill column", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('p.F426A'), transcript_name=c("ENST00000269305"), testA="blue", testB="red")
out <- lolliplot_mutationObs(x, track="bottom", fill_value="testB", label_column=NULL,
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000)
expect_equal(as.character(out$testB), "red")
})
test_that("lolliplot_qual checks that input to x is a data frame", {
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
x <- as.matrix(x)
y <- NULL
z <- NULL
expect_message(lolliplot_qual(x, y, z), "not a data frame")
})
test_that("lolliplot_qual checks for correct column names in x", {
x <- data.frame(incorrect=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
y <- NULL
z <- NULL
expect_error(lolliplot_qual(x, y, z), "not detect correct columns")
x <- data.frame(transcript_name=c("ENST00000269305"), incorrect=c("TP53"), amino_acid_change=c("p.K440L"))
expect_error(lolliplot_qual(x, y, z), "not detect correct columns")
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), incorrect=c("p.K440L"))
expect_error(lolliplot_qual(x, y, z), "not detect correct columns")
})
test_that("lolliplot_qual checks that only one transcript is present", {
x <- data.frame(transcript_name=c("ENST00000269305", "ENST00000222222"), gene=c("TP53", "TP53"), amino_acid_change=c("p.K440L", "p.T318G"))
y <- NULL
z <- NULL
expect_error(lolliplot_qual(x, y, z), "more than 1 transcript")
})
test_that("lolliplot_qual checks that input to y is a data frame", {
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
y <- data.frame(transcript_name=c("ENST00000269305"), amino_acid_change=c("p.K440L"))
y <- as.matrix(y)
z <- NULL
expect_message(lolliplot_qual(x, y, z), "not a data frame")
})
test_that("lolliplot_qual checks for correct column names in input to y", {
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
y <- data.frame(incorrect=c("ENST00000269305"), amino_acid_change=c("p.K440L"))
z <- NULL
expect_error(lolliplot_qual(x, y, z), "not detect correct")
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
y <- data.frame(transcript_name=c("ENST00000269305"), incorrect=c("p.K440L"))
z <- NULL
expect_error(lolliplot_qual(x, y, z), "not detect correct")
})
test_that("lolliplot_qual checks that input to z is a data frame", {
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
y <- NULL
z <- data.frame(description=c("test"), start=c(5), stop=c(3))
z <- as.matrix(z)
expect_warning(lolliplot_qual(x, y, z), "not a data frame")
})
test_that("lolliplot_qual checks for correct column names in input to y", {
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
y <- NULL
z <- data.frame(incorrect=c("test"), start=c(5), stop=c(3))
expect_error(lolliplot_qual(x, y, z), "not detect correct")
z <- data.frame(description=c("test"), incorrect=c(5), stop=c(3))
expect_error(lolliplot_qual(x, y, z), "not detect correct")
z <- data.frame(description=c("test"), start=c(5), incorrect=c(3))
expect_error(lolliplot_qual(x, y, z), "not detect correct")
})
test_that("lolliplot_reduceLolli successfully reduces the number of lollis to the max limit",{
x <- data.frame(mutation_coord=c(5, 5, 5, 2, 2), label=c("a", "b", "c", "d", "e"))
max <- 2
out <- lolliplot_reduceLolli(x, max=max)
expect_equal(nrow(out[out$mutation_coord == 5,]), 2)
})
test_that("lolliplot_reduceLolli reduces nothing if max is set to NULL", {
x <- data.frame(mutation_coord=c(5, 5, 5, 2, 2), label=c("a", "b", "c", "d", "e"))
max <- NULL
out <- lolliplot_reduceLolli(x, max=max)
expect_equal(nrow(out), 5)
})
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context("Deprecated")
test_that("waterfall levels data correctly and as such data aligns", {
out <- waterfall(brcaMAF, plotGenes=c("PIK3CA", "TP53"), out="data")
expect_equal(levels(out[["main"]]$sample), levels(out[["mutation_count"]]$sample))
})
test_that("waterfall correctly counts mutations in a sample", {
# Obtain output
out <- waterfall(brcaMAF, plotGenes=c("PIK3CA", "TP53"), out="data")
out_A <- out$mutation[out$mutation_count$sample == "TCGA-A1-A0SI-01A-11D-A142-09" &
out$mutation_count$trv_type == "Synonymous",]
out_B <- out$mutation[out$mutation_count$sample == "TCGA-A1-A0SI-01A-11D-A142-09" &
out$mutation_count$trv_type == "Non Synonymous",]
# Calculate expected output
expec <- brcaMAF[brcaMAF$Tumor_Sample_Barcode=="TCGA-A1-A0SI-01A-11D-A142-09",]
expec_A <- nrow(expec[expec$Variant_Classification == "Silent" ,c(1, 9, 16)])
expec_B <- nrow(expec[expec$Variant_Classification != "Silent" ,c(1, 9, 16)])
expect_equal(out_A$mutation_total, expec_A)
expect_equal(out_B$mutation_total, expec_B)
})
test_that("waterfall_calcMutFreq correctly counts mutations", {
x <- data.frame(sample=c('a', 'a', 'a', 'b', 'b'), gene=rep('b', 5), trv_type=rep('silent', 5))
out <- waterfall_calcMutFreq(x)
expect_equal(out[out$sample == 'a' & out$trv_type == 'Synonymous', 'mutation_total'], 3)
expect_equal(out[out$sample == 'b' & out$trv_type == 'Synonymous', 'mutation_total'], 2)
expect_equal(out[out$sample == 'a' & out$trv_type == 'Non Synonymous', 'mutation_total'], 0)
expect_equal(out[out$sample == 'b' & out$trv_type == 'Non Synonymous', 'mutation_total'], 0)
})
test_that("waterfall_calcMutFreq correctly identifies Synonymous, Non Synonymous mutations", {
x <- data.frame(sample=c('a', 'a', 'a'), gene=rep('b', 3), trv_type=c('not silent', 'Silent', 'siLenT'))
out <- waterfall_calcMutFreq(x)
expect_equal(out[out$sample == 'a' & out$trv_type == 'Synonymous', 'mutation_total'], 2)
expect_equal(out[out$sample == 'a' & out$trv_type == 'Non Synonymous', 'mutation_total'], 1)
})
test_that("waterfall_geneAlt checks for proper input", {
x <- data.frame(sample='a', gene='b', trv_type='missense')
genes <- c(1)
expect_warning(waterfall_geneAlt(x, genes), "not a character vector")
})
test_that("waterfall_geneAlt checks that genes are available for removal", {
x <- data.frame(sample='a', gene='b', trv_type='missense')
genes <- c('z')
expect_warning(waterfall_geneAlt(x, genes), "element not found in x")
})
test_that("waterfall_geneAlt successfully removes genes from input", {
x <- data.frame(sample=c('a','a'), gene=c('b','c'), trv_type=rep('missense',2))
genes <- c('b')
out <- waterfall_geneAlt(x, genes)
expect_equal(nrow(out), 1)
})
test_that("waterfall_geneRecurCutoff properly detects if recurrence cutoff exceeds max recurrence", {
x <- data.frame(sample=factor(seq(1:11)), gene=c(rep(c('b', 'c'), 5), 'd'), trv_type=rep('missense', 11))
recurrence_cutoff <- .51
expect_warning(waterfall_geneRecurCutoff(x, recurrence_cutoff), "cutoff specified exceeds")
})
test_that("waterfall_geneRecurCutoff properly removes entries not meeting a recurrence cutoff", {
x <- data.frame(sample=factor(seq(1:11)), gene=c(rep(c('b', 'c'), 5), 'd'), trv_type=rep('missense', 11))
recurrence_cutoff <- .51
out <- suppressWarnings(waterfall_geneRecurCutoff(x, recurrence_cutoff))
expect_equal(nrow(out), 10)
})
test_that("waterfall_geneSort properly orders genes", {
x <- data.frame(sample=rep('a', 10), gene=c(rep(c('b', 'c', 'd'), 3), 'e'), trv_type=rep('missense', 10))
out <- waterfall_geneSort(x)
expec <- c('e', 'b', 'c', 'd')
expect_equal(out, expec)
})
test_that("waterfall_geneSort defers to input to geneOrder if specified by the user", {
x <- data.frame(sample=rep('a', 10), gene=c(rep(c('b', 'c', 'd'), 3), 'e'), trv_type=rep('missense', 10))
gene_order <- c("d", "b", "c", "e")
out <- waterfall_geneSort(x, geneOrder=gene_order)
expec <- rev(gene_order)
expect_equal(out, expec)
gene_order <- c("none", "b", "d")
expec <- c("d", "b")
out <- suppressWarnings(waterfall_geneSort(x, geneOrder=gene_order))
expect_equal(out, expec)
})
test_that("waterfall_geneSort warns if it finds genes supplied to geneOrder not in the primary input", {
# test case where no genes in geneOrder are in primary input
x <- data.frame(sample=rep('a', 10), gene=c(rep(c('b', 'c', 'd'), 3), 'e'), trv_type=rep('missense', 10))
gene_order <- c("none")
expect_warning(waterfall_geneSort(x, geneOrder=gene_order), "Did not find any genes")
# test case where some but not all genes in geneOrder are in primary input
x <- data.frame(sample=rep('a', 10), gene=c(rep(c('b', 'c', 'd'), 3), 'e'), trv_type=rep('missense', 10))
gene_order <- c("none", "b", "d")
expect_warning(waterfall_geneSort(x, geneOrder=gene_order), "The following genes were not found")
})
test_that("waterfall_MAF2anno checks for proper column names",{
# Check without label column
x <- data.frame(Tumor_Sample_Barcode='samp1', incorrect='egfr', Variant_Classification='missense')
label_col <- NULL
expect_error(waterfall_MAF2anno(x, label_col), "Did not detect")
# Check with label column
x <- data.frame(Tumor_Sample_Barcode='samp1', Hugo_Symbol='egfr', Variant_Classification='missense', incorrect='R504K')
label_col <- 'label'
expect_error(waterfall_MAF2anno(x, label_col), "Did not detect")
})
test_that("waterfall_MAF2anno output format is correct", {
# check without label column
x <- data.frame(Tumor_Sample_Barcode='samp1', Hugo_Symbol='egfr', Variant_Classification='missense')
label_col <- NULL
out <- waterfall_MAF2anno(x, label_col)
expect_equal(sort(colnames(out)), sort(c('sample', 'gene', 'trv_type')))
# check with label column
x <- data.frame(Tumor_Sample_Barcode='samp1', Hugo_Symbol='egfr', Variant_Classification='missense', label='R504K')
label_col <- 'label'
out <- waterfall_MAF2anno(x, label_col)
expect_equal(sort(colnames(out)), sort(c('sample', 'gene', 'trv_type', label_col)))
# Check class
expect_is(out, "data.frame")
})
test_that("waterfall_MGI2anno checks for proper column names",{
# Check without label column
x <- data.frame(incorrect='samp1', gene_name='egfr', trv_type='missense')
label_col <- NULL
expect_error(waterfall_MGI2anno(x, label_col), "Did not detect")
# Check with label column
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense', incorrect='R504K')
label_col <- 'label'
expect_error(waterfall_MGI2anno(x, label_col), "Did not detect")
})
test_that("waterfall_MGI2anno output format is correct", {
# check without label column
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
label_col <- NULL
out <- waterfall_MGI2anno(x, label_col)
expect_equal(sort(colnames(out)), sort(c('sample', 'gene', 'trv_type')))
# check with label column
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense', label='R504K')
label_col <- 'label'
out <- waterfall_MGI2anno(x, label_col)
expect_equal(sort(colnames(out)), sort(c('sample', 'gene', 'trv_type', label_col)))
# Check class
expect_is(out, "data.frame")
})
test_that("waterfall_NA2gene properly assigns NA values in the input to the top gene", {
x <- data.frame(sample=c('a', 'b', NA), gene=c('c', 'c', NA), trv_type=c('missense', 'missense', NA))
out <- waterfall_NA2gene(x)
expect_false(any(is.na(out$gene)))
})
test_that("waterfall_qual returns a list with three elements", {
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- NULL
z <- NULL
file_type <- 'MGI'
label_col <- NULL
out <- waterfall_qual(x, y, z, file_type, label_col)
expect_equal(length(out), 3)
expect_is(out, 'list')
})
test_that("waterfall_qual checks input to file_type is valid", {
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- NULL
z <- NULL
file_type <- 'INCORRECT'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col), "Unrecognized")
})
test_that("waterfall_qual verifies input is of proper class", {
# Check x
x <- c(sample='samp1', gene_name='egfr', trv_type='missense')
y <- NULL
z <- NULL
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
# Check y
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- c(sample='samp1', variable='gender', value='male')
z <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
# Check z
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- NULL
z <- c(sample='samp1', mut_burden='5')
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
})
test_that("waterfall_qual verifies correct columns are in input", {
# Check x
x <- data.frame(INCORRECT='samp1', gene_name='egfr', trv_type='missense',
chromosome_name=1, start=1, stop=1, reference="A",
variant="T")
y <- NULL
z <- NULL
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
# Check y
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- data.frame(incorrect='samp1', variable='gender', value='male')
z <- NULL
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- data.frame(sample='samp1', incorrect='gender', value='male')
z <- NULL
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- data.frame(sample='samp1', variable='gender', incorrect='male')
z <- NULL
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
# Check z
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- NULL
z <- c(incorrect='samp1', mut_burden='5')
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
x <- data.frame(sample='samp1', gene_name='egfr', trv_type='missense')
y <- NULL
z <- c(sample='samp1', incorrect='5')
file_type <- 'MGI'
label_col <- NULL
expect_error(waterfall_qual(x, y, z, file_type, label_col),
'Did not detect')
})
test_that("waterfall_rmvSilent removes silent mutations", {
x <- data.frame(sample=rep('a', 3), gene=rep('b', 3), trv_type=c('Silent', 'siLenT', 'missense'))
out <- droplevels(waterfall_rmvSilent(x))
expec <- data.frame(sample=rep('a', 3), gene=c(NA, NA, 'b'), trv_type=c(NA, NA, 'missense'))
expect_equal(out, expec)
})
test_that("waterfall_sampAlt checks for proper class", {
x <- data.frame(sample='1', gene='egfr', trv_type='missense')
samples <- c(1)
expect_warning(waterfall_sampAlt(x, samples), "not a character vector")
})
test_that("waterfall_sampAlt correctly adds in samples not in the original data", {
x <- data.frame(sample='samp1', gene='egfr', trv_type='missense')
samples <- c('samp1', 'samp2')
out <- waterfall_sampAlt(x, samples)
expec <- data.frame(sample=as.factor(c('samp2', 'samp1')), gene=c(NA, 'egfr'), trv_type=c(NA, 'missense'))
expect_equal(out, expec)
})
test_that("waterfall_sampAlt correctly removes samples in the original data", {
x <- data.frame(sample=c('samp1', 'samp2'), gene=c('egfr', 'tp53'), trv_type=c('missense', 'nonsense'))
samples <- c('samp1')
out <- waterfall_sampAlt(x, samples)
expec <- data.frame(sample=as.factor('samp1'), gene='egfr', trv_type='missense')
expect_equal(out, expec)
})
test_that("waterfall_sampSort properly sorts samples based on a hierarchy", {
x <- data.frame(sample=c('a', 'a', 'a', 'b', 'b', 'c'), gene=c('x', 'y', 'z', 'x', 'y', 'z'), trv_type=rep('missense', 6))
out <- waterfall_sampSort(x)
expec <- c('a', 'c', 'b')
expect_equal(out, expec)
# Test that convert to boolean properly works on data frame with one gene
x <- data.frame(sample=c('a', 'b', 'c'), gene=c('x', 'x', 'x'), trv_type=rep('missense', 3))
out <- waterfall_sampSort(x)
expec <- c('a', 'b', 'c')
expect_equal(out, expec)
# Test that samples added in via the plotSamples parameter in waterfall are
# put last
x <- data.frame(sample=c('TESTA', 'A'), gene=c(NA, 'x'), trv_type=c(NA, 'missense'))
out <- waterfall_sampSort(x)
expec <- c('A', 'TESTA')
#expect_equal(out, expec)
})
test_that("waterfall_sampSort removes samples in sampOrder not found in primary input", {
x <- data.frame(sample=c('a', 'a', 'a', 'b', 'b', 'c'), gene=c('x', 'y', 'z', 'x', 'y', 'z'), trv_type=rep('missense', 6))
sample_order <- c("a", "b", "c", "d")
out <- suppressWarnings(waterfall_sampSort(x, sampOrder = sample_order))
expec <- c('a', 'b', 'c')
expect_equal(out, expec)
})
test_that("waterfall_hierarchyTRV leaves only one entry for a gene/mutation", {
# Check MGI file
x <- data.frame(sample=rep(c('a', 'b'), 4), gene=rep(c('c', 'd'), 4), trv_type=rep(c('nonsense', 'missense', 'rna', 'silent'), 2))
file_type <- 'MGI'
variant_class_order <- NULL
out <- waterfall_hierarchyTRV(x, file_type, variant_class_order)
expect_equal(nrow(out), 2)
expect_equal(as.character(out[out$sample == 'a' & out$gene == 'c', 'trv_type']), 'nonsense')
expect_equal(as.character(out[out$sample == 'b' & out$gene == 'd', 'trv_type']), 'missense')
# Check MAF file
x <- data.frame(sample=rep(c('a', 'b'), 4), gene=rep(c('c', 'd'), 4), trv_type=rep(c('Splice_Site', 'IGR', 'Silent', 'Nonsense_Mutation'), 2))
file_type <- 'MAF'
out <- waterfall_hierarchyTRV(x, file_type, variant_class_order)
expect_equal(nrow(out), 2)
expect_equal(as.character(out[out$sample == 'a' & out$gene == 'c', 'trv_type']), 'Splice_Site')
expect_equal(as.character(out[out$sample == 'b' & out$gene == 'd', 'trv_type']), 'Nonsense_Mutation')
})
test_that("waterfall_hierarchyTRV recognizes an invalid mutation type", {
# Check for MGI
x <- data.frame(sample=rep(c('a', 'b'), 4), gene=rep(c('c', 'd'), 4), trv_type=rep(c('missense', 'rna', 'silent', 'invalid'), 2))
file_type <- 'MGI'
variant_class_order <- NULL
expect_error(waterfall_hierarchyTRV(x, file_type, variant_class_order), "invalid mutation type")
# Check for MAF
x <- data.frame(sample=rep(c('a', 'b'), 4), gene=rep(c('c', 'd'), 4), trv_type=rep(c('Splice_Site', 'IGR', 'Silent', 'invalid'), 2))
file_type <- 'MAF'
expect_error(waterfall_hierarchyTRV(x, file_type, variant_class_order), "invalid mutation type")
})
test_that("TvTi_rmMnuc correctly identifies and removes multi-nucleotide codes", {
x <- data.frame("reference"=c("AA", "T"), "variant"=c("AT", "C"))
expect_equal(nrow(suppressWarnings(TvTi_rmMnuc(x))), 1)
x <- data.frame("reference"=c("gg", "t"), "variant"=c("cc", "g"))
expect_equal(nrow(suppressWarnings(TvTi_rmMnuc(x))), 1)
})
test_that("TvTi_rmMnuc warns if it finds multi-nucleotide codes", {
x <- data.frame("reference"=c("AA", "T"), "variant"=c("AT", "C"))
expect_warning(TvTi_rmMnuc(x), "multi-nucleotides present")
})
test_that("TvTi_calcTransTranvFreq adds in trans/tranv missing from samples", {
x <- data.frame(reference=c("A", "G"),
variant=c("T", "C"),
sample=c("samp1", "samp2"),
trans_tranv=c("A->T or T->A (TV)", "A->G or T->C (TI)"))
expect_equal(nrow(TvTi_calcTransTranvFreq(x)), 12)
})
test_that("TvTi_calcTransTranvFreq correctly calculates the proportions of TR/TV", {
x <- data.frame(reference=c("A", "G"),
variant=c("T", "C"),
sample=c("samp1", "samp1"),
trans_tranv=c("A->T or T->A (TV)", "A->G or T->C (TI)"))
out <- TvTi_calcTransTranvFreq(x)
expect_equal(out[out$trans_tranv == "A->G or T->C (TI)",]$Prop, .5)
x <- data.frame(reference=c("A", "G"),
variant=c("T", "C"),
sample=c("samp1", "samp2"),
trans_tranv=c("A->T or T->A (TV)", "A->G or T->C (TI)"))
out <- TvTi_calcTransTranvFreq(x)
expect_equal(sum(out$Prop), 2)
})
test_that("TvTi_calcTransTranvFreq correctly calculates the frequency of Tv/Ti", {
x <- data.frame(reference=c("A", "G"),
variant=c("T", "C"),
sample=c("samp1", "samp1"),
trans_tranv=c("A->T or T->A (TV)", "A->G or T->C (TI)"))
out <- TvTi_calcTransTranvFreq(x)
expect_equal(out[out$trans_tranv == "A->G or T->C (TI)",]$Freq, 1)
expect_equal(out[out$trans_tranv == "A->T or T->A (TV)",]$Freq, 1)
x <- data.frame(reference=c("A", "G"),
variant=c("T", "C"),
sample=c("samp1", "samp2"),
trans_tranv=c("A->T or T->A (TV)", "A->G or T->C (TI)"))
out <- TvTi_calcTransTranvFreq(x)
expect_equal(sum(out$Freq), 2)
})
test_that("TvTi_convMaf Detects Homozygous events and only counts them once", {
x <- data.frame(Tumor_Sample_Barcode=c("A"), Reference_Allele="C", Tumor_Seq_Allele1=c("T"), Tumor_Seq_Allele2=c("T"))
expect <- data.frame(sample=c("A"), reference=c("C"), variant=c("T"))
expect_equal(TvTi_convMaf(x), expect)
})
test_that("TvTi_convMaf eliminates calls where the reference allele equals the variant allele", {
x <- data.frame(Tumor_Sample_Barcode=c("A"), Reference_Allele="C", Tumor_Seq_Allele1=c("C"), Tumor_Seq_Allele2=c("C"))
expect_equal(nrow(TvTi_convMaf(x)), 0)
x <- data.frame(Tumor_Sample_Barcode=c("A"), Reference_Allele="C", Tumor_Seq_Allele1=c("T"), Tumor_Seq_Allele2=c("C"))
expect_equal(nrow(TvTi_convMaf(x)), 1)
x <- data.frame(Tumor_Sample_Barcode=c("A"), Reference_Allele="C", Tumor_Seq_Allele1=c("C"), Tumor_Seq_Allele2=c("T"))
expect_equal(nrow(TvTi_convMaf(x)), 1)
})
test_that("TvTi_qual correctly identifies invalid input to the fileType parameter", {
x <- data.frame(sample=c("a", "a"), reference=c("a", "a"), variant=c("c", "c"))
y <- NULL
file_type="INCORRECT"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "input to paramter fileType")
})
test_that("TvTi_qual correctly identifies if input to x is not of proper class", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
x <- as.matrix(x)
y <- NULL
file_type="MGI"
expect_warning(TvTi_qual(x, y=y, file_type=file_type), "not an object of class data frame")
})
test_that("TvTi_qual correctly identifies if duplicate rows are present in input to x", {
x <- data.frame(sample=c("a", "a"), reference=c("a", "a"), variant=c("c", "c"))
y <- NULL
file_type="MGI"
expect_warning(TvTi_qual(x, y=y, file_type=file_type), "Detected duplicate rows")
})
test_that("TvTi_qual correctly identifies if input to y is not of proper class", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- c("A->C or T->G (TV)"=1/6, "A->G or T->C (TI)"=1/6, "A->T or T->A (TV)"=1/6, "G->A or C->T (TI)"=1/6, "G->C or C->G (TV)"=1/6, "G->T or C->A (TV)"=1/6)
y <- as.matrix(y)
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "not an object of class data frame")
})
test_that("TvTi_qual Checks for proper column names if input to y is a data frame", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- data.frame(Prop=rep(1/6, 6), incorrect=rep("A->C or T->G (TV)", 6))
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "Did not detect correct column names")
})
test_that("TvTi_qual checks that proportion values are of numeric type", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- c("A->C or T->G (TV)"="1/6", "A->G or T->C (TI)"="1/6", "A->T or T->A (TV)"="1/6", "G->A or C->T (TI)"="1/6", "G->C or C->G (TV)"="1/6", "G->T or C->A (TV)"="1/6")
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "not of type double or numeric")
})
test_that("TvTi_qual recognizes if input to y is not of proper class", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- c("A->C or T->G (TV)"="1/6", "A->G or T->C (TI)"="1/6", "A->T or T->A (TV)"="1/6", "G->A or C->T (TI)"="1/6", "G->C or C->G (TV)"="1/6", "G->T or C->A (TV)"="1/6")
y <- as.matrix(y)
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "input to y is not")
})
test_that("TvTi_qual detects if proper column names are not supplied to x", {
x <- data.frame(incorrect=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- data.frame(Prop=rep(1/6, 6), trans_tranv=rep("A->C or T->G (TV)", 6))
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "not find all columns")
x <- data.frame(Tumor_Sample_Barcode=c("a", "b"), incorrect=c("a", "a"), Tumor_Seq_Allele1=c("a", "a"), Tumor_Seq_Allele2=c("a", "a"))
file_type <- "MAF"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "not find all columns")
})
test_that("TvTi_qual detects unexpected nucleotide codes in input supplied to x", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "I"), variant=c("g", "c"))
y <- data.frame(Prop=rep(1/6, 6), trans_tranv=rep("A->C or T->G", 6))
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "Unrecognized Base")
x <- data.frame(sample=c("a", "b"), reference=c("a", "I"), variant=c("I", "c"))
expect_error(TvTi_qual(x, y=y, file_type=file_type), "Unrecognized Base")
})
test_that("TvTi_qual checks for proper transition/transversion levels in input to y", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- data.frame(Prop=rep(1/6, 6), trans_tranv=rep("A->C or T->G (TV)", 6))
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "all combinations of transitions/transversions")
})
test_that("TvTi_qual checks that proportions supplied in y sum to one", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- c("A->C or T->G (TV)"=1/5, "A->G or T->C (TI)"=1/6, "A->T or T->A (TV)"=1/6, "G->A or C->T (TI)"=1/6, "G->C or C->G (TV)"=1/6, "G->T or C->A (TV)"=1/6)
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, file_type=file_type), "should equal 1")
})
test_that("TvTi_qual identifies if argument supplied to z is not a data frame", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- NULL
z <- data.frame("sample"=c("x"), "variable"=c("y"), "value"=c("Z"))
z <- as.matrix(z)
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, z=z, file_type=file_type), "Did not detect")
})
test_that("TvTi_qual identifies if argument supplied to z contains incorrect column names", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- NULL
z <- data.frame("INCORRECT"=c("x"), "variable"=c("y"), "value"=c("Z"))
file_type <- "MGI"
expect_error(TvTi_qual(x, y=y, z=z, file_type=file_type), "Did not detect")
})
test_that("TvTi_qual identifies if samples supplied to argument x and z differ", {
x <- data.frame(sample=c("a", "b"), reference=c("a", "t"), variant=c("g", "c"))
y <- NULL
z <- data.frame("sample"=c("x"), "variable"=c("y"), "value"=c("Z"))
file_type <- "MGI"
expect_warning(TvTi_qual(x, y=y, z=z, file_type=file_type), "Found a sample")
})
test_that("TvTi_rmIndel correctly identifies and removes indels", {
x <- data.frame("reference"=c('-', 'A'), "variant"=c('A', 'A'))
expect_equal(nrow(suppressWarnings(TvTi_rmIndel(x))), 1)
x <- data.frame("reference"=c('A', 'A'), "variant"=c('0', 'A'))
expect_equal(nrow(suppressWarnings(TvTi_rmIndel(x))), 1)
})
test_that("TvTi_rmIndel correctly warns if indels are detected", {
x <- data.frame("reference"=c('-', 'A'), "variant"=c('A', 'A'))
expect_message(TvTi_rmIndel(x), "indels present")
})
test_that("lolliplot_AA2sidechain, converts amino acid codes to sidechain information", {
x <- c("f")
out <- lolliplot_AA2sidechain(x)
expect_equal(out, "Nonpolar")
x <- c("F")
out <- lolliplot_AA2sidechain(x)
expect_equal(out, "Nonpolar")
})
test_that("lolliplot_Codon2AA Converts codons to amino acids", {
x <- c("TTG")
out <- lolliplot_Codon2AA(x)
expect_equal(out, "L")
x <- c("ttg")
out <- lolliplot_Codon2AA(x)
expect_equal(out, "L")
})
test_that("lolliplot_Codon2AA outputs NA if a codon is not recognized", {
x <- c("ZZZ")
out <- lolliplot_Codon2AA(x)
expect_equal(out, NULL)
})
test_that("lolliplot_constructGene properly nest's overlappping genomic features", {
domain_data <- data.frame(desc=c("domainA", "domainB"), start=c(5, 10), end=c(20, 15))
gene <- "test"
length <- 50
out <- lolliplot_constructGene(gene, domain_data, length)
expect_equivalent(out[out$Domain == 'domainA',]$nest, 1)
expect_equivalent(out[out$Domain == 'domainB',]$nest, 2)
expect_equivalent(out[out$Domain == 'test',]$nest, 1)
})
test_that("lolliplot_constructGene properly identifies if a domain exceeds the length of the protein", {
domain_data <- data.frame(desc=c("domainA"), start=c(5), end=c(20))
gene <- "test"
length <- 15
expect_warning(lolliplot_constructGene(gene, domain_data, length), "is exceeding the length")
})
test_that("lolliplot_constructGene properly identifies if a domain is before the start of the protein", {
domain_data <- data.frame(desc=c("domainA"), start=c(0), end=c(10))
gene <- "test"
length <- 15
expect_warning(lolliplot_constructGene(gene, domain_data, length), "less than the start of the protein")
})
test_that("lolliplot_constructGene identifies if a start position is greater than an end position", {
domain_data <- data.frame(desc=c("domainA"), start=c(10), end=c(5))
gene <- "test"
length <- 20
expect_warning(lolliplot_constructGene(gene, domain_data, length), "Found a start position greater")
})
test_that("lolliplot_DNAconv identifies if an incomplete codon is given in the sequence", {
x <- c("AAATT")
expect_warning(lolliplot_DNAconv(x), "not a multiple of three")
})
test_that("lolliplot_DNAconv splits the input character string into codon lengths", {
x <- c("AAATTT")
expect_equal(length(lolliplot_DNAconv(x)), 2)
})
test_that("lolliplot_DNAconv converts condons into residues", {
x <- c("AAA")
expect_equal(lolliplot_DNAconv(x), "K")
})
test_that("lolliplot_DNAconv converts residues into sidechains", {
x <- c("AAA")
expect_equal(lolliplot_DNAconv(x, to="sidechain"), "Basic")
})
test_that("lolliplot_DNAconv throws an warning if input to the to parameter is unrecognized", {
x <- c("AAA")
expect_warning(lolliplot_DNAconv(x, to="incorrect"), "did not recognize")
})
test_that("lolliplot_dodgeCoordX succesfully dodges coordinates as expected", {
x <- data.frame(x=c(1, 2), y=c(1, 1))
out <- lolliplot_dodgeCoordX(x)
expect_true(out[1] < 1)
expect_true(out[2] > 2)
x <- data.frame(x=c(1, 1), y=c(1, 1))
out <- lolliplot_dodgeCoordX(x)
expect_equal(out[1], 1)
})
test_that("lolliplot_dodgeCoordX does not apply a force field if length of vector is 1", {
x <- data.frame(x=c(1), y=c(1))
out <- lolliplot_dodgeCoordX(x)
expect_equal(out, 1)
})
test_that("lolliplot_dodgeCoordY succesfully dodges y coordinates as expected", {
x <- data.frame(coord_x_dodge=c(1, 2))
out <- lolliplot_dodgeCoordY(x, track="top")
expect_equal(out[1], out[2])
x <- data.frame(coord_x_dodge=c(1, 1))
out <- lolliplot_dodgeCoordY(x, track="top")
expect_true(out[1] != out[2])
expect_true(out[1] > 0)
out <- lolliplot_dodgeCoordY(x, track="bottom")
expect_true(out[1] != out[2])
expect_true(out[1] < 0)
})
test_that("lolliplot_mutationObs correctly removes input variants annotated as within an intron", {
x <- data.frame(gene=c("TP53", "TP53"), amino_acid_change=c('e0+2', 'p.K101N'), transcript_name=c("ENST00000269305", "ENST00000269305"))
expect_message(lolliplot_mutationObs(x,
track="top",
fill_value=NULL,
label_column=NULL,
rep.fact=5000,
rep.dist.lmt=500,
attr.fact=.1,
adj.max=.1,
adj.lmt=.5,
iter.max=10000), "not within a residue")
})
test_that("lolliiplot_mutationObs correctly identifies if there are no mutations to plot", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('e0+2'), transcript_name=c("ENST00000269305"))
expect_error(lolliplot_mutationObs(x,
track="top",
fill_value=NULL,
label_column=NULL,
rep.fact=5000,
rep.dist.lmt=500,
attr.fact=.1,
adj.max=.1,
adj.lmt=.5,
iter.max=10000), "Did not detect any residues")
})
test_that("lolliplot_mutationObs correctly extract coordinates from p. notation", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('p.F426A'), transcript_name=c("ENST00000269305"))
out <- lolliplot_mutationObs(x, track="top", fill_value=NULL, label_column=NULL,
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000)
expect_equal(out$mutation_coord, 426)
})
test_that("lolliplot_mutationObs correctly identifies amino acid changes in c. notation", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('c.F4260A'), transcript_name=c("ENST00000269305"))
expect_error(lolliplot_mutationObs(x, track="top", fill_value=NULL, label_column=NULL,
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000), "c. notation is not currently supported")
})
test_that("lolliplot_mutationObs correctly notifies if amino_acid_change notation is not recognized", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('4260'), transcript_name=c("ENST00000269305"))
expect_error(lolliplot_mutationObs(x, track="top", fill_value=NULL, label_column=NULL,
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000), "Could not determine notation type")
})
test_that("lolliplot_mutationObs correctly sets mutations y coord based on the track", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('p.F426A'), transcript_name=c("ENST00000269305"))
out <- lolliplot_mutationObs(x, track="bottom", fill_value=NULL, label_column=NULL,
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000)
expect_true(out$coord_y_dodge < 0)
out <- lolliplot_mutationObs(x, track="top", fill_value=NULL, label_column=NULL,
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000)
expect_true(out$coord_y_dodge > 0)
})
test_that("lolliplot_mutationObs correctly sets a specified label column", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('p.F426A'), transcript_name=c("ENST00000269305"), testA="blue", testB="red")
out <- lolliplot_mutationObs(x, track="bottom", fill_value=NULL, label_column="testA",
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000)
expect_equal(out$labels, "blue")
})
test_that("lolliplot_mutationOBs correctly sets a specified fill column", {
x <- data.frame(gene=c("TP53"), amino_acid_change=c('p.F426A'), transcript_name=c("ENST00000269305"), testA="blue", testB="red")
out <- lolliplot_mutationObs(x, track="bottom", fill_value="testB", label_column=NULL,
rep.fact=5000, rep.dist.lmt=500, attr.fact=.1,
adj.max=.1, adj.lmt=.5, iter.max=10000)
expect_equal(as.character(out$testB), "red")
})
test_that("lolliplot_qual checks that input to x is a data frame", {
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
x <- as.matrix(x)
y <- NULL
z <- NULL
expect_message(lolliplot_qual(x, y, z), "not a data frame")
})
test_that("lolliplot_qual checks for correct column names in x", {
x <- data.frame(incorrect=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
y <- NULL
z <- NULL
expect_error(lolliplot_qual(x, y, z), "not detect correct columns")
x <- data.frame(transcript_name=c("ENST00000269305"), incorrect=c("TP53"), amino_acid_change=c("p.K440L"))
expect_error(lolliplot_qual(x, y, z), "not detect correct columns")
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), incorrect=c("p.K440L"))
expect_error(lolliplot_qual(x, y, z), "not detect correct columns")
})
test_that("lolliplot_qual checks that only one transcript is present", {
x <- data.frame(transcript_name=c("ENST00000269305", "ENST00000222222"), gene=c("TP53", "TP53"), amino_acid_change=c("p.K440L", "p.T318G"))
y <- NULL
z <- NULL
expect_error(lolliplot_qual(x, y, z), "more than 1 transcript")
})
test_that("lolliplot_qual checks that input to y is a data frame", {
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
y <- data.frame(transcript_name=c("ENST00000269305"), amino_acid_change=c("p.K440L"))
y <- as.matrix(y)
z <- NULL
expect_message(lolliplot_qual(x, y, z), "not a data frame")
})
test_that("lolliplot_qual checks for correct column names in input to y", {
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
y <- data.frame(incorrect=c("ENST00000269305"), amino_acid_change=c("p.K440L"))
z <- NULL
expect_error(lolliplot_qual(x, y, z), "not detect correct")
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
y <- data.frame(transcript_name=c("ENST00000269305"), incorrect=c("p.K440L"))
z <- NULL
expect_error(lolliplot_qual(x, y, z), "not detect correct")
})
test_that("lolliplot_qual checks that input to z is a data frame", {
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
y <- NULL
z <- data.frame(description=c("test"), start=c(5), stop=c(3))
z <- as.matrix(z)
expect_warning(lolliplot_qual(x, y, z), "not a data frame")
})
test_that("lolliplot_qual checks for correct column names in input to y", {
x <- data.frame(transcript_name=c("ENST00000269305"), gene=c("TP53"), amino_acid_change=c("p.K440L"))
y <- NULL
z <- data.frame(incorrect=c("test"), start=c(5), stop=c(3))
expect_error(lolliplot_qual(x, y, z), "not detect correct")
z <- data.frame(description=c("test"), incorrect=c(5), stop=c(3))
expect_error(lolliplot_qual(x, y, z), "not detect correct")
z <- data.frame(description=c("test"), start=c(5), incorrect=c(3))
expect_error(lolliplot_qual(x, y, z), "not detect correct")
})
test_that("lolliplot_reduceLolli successfully reduces the number of lollis to the max limit",{
x <- data.frame(mutation_coord=c(5, 5, 5, 2, 2), label=c("a", "b", "c", "d", "e"))
max <- 2
out <- lolliplot_reduceLolli(x, max=max)
expect_equal(nrow(out[out$mutation_coord == 5,]), 2)
})
test_that("lolliplot_reduceLolli reduces nothing if max is set to NULL", {
x <- data.frame(mutation_coord=c(5, 5, 5, 2, 2), label=c("a", "b", "c", "d", "e"))
max <- NULL
out <- lolliplot_reduceLolli(x, max=max)
expect_equal(nrow(out), 5)
})
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