Nothing
tests/testthat/test-processing.R
In IntLIM: Integration of Omics Data Using Linear Modeling
# If a value other than an IntLimResults object is input, an error should be thrown.
testthat::test_that("Inputting the wrong class causes early termination.", {
# Generate input data.
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputDataGood <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
inputDataBad <- "Hello World"
# Generate result data.
pvals <- matrix(rep(0.2, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
coef <- matrix(rep(8, 9), nrow = 3, ncol = 3)
rsq <- coef <- matrix(rep(0.6, 9), nrow = 3, ncol = 3)
inputResultsGood <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=1)
inputResultsBad <- "Hello World"
testthat::expect_error(IntLIM::ProcessResults(inputResultsBad, inputDataGood),
paste("Results must be an IntLIMResults object"),
ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResultsGood, inputDataBad),
paste("Data must be an IntLIMData object"),
ignore.case = TRUE)
})
# Types for both outcome and independent analyte must be either 1 or 2.
testthat::test_that("Check that outcome and independent analyte types are appropriate.", {
# Generate input data.
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(rep(0.2, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
coef <- matrix(rep(8, 9), nrow = 3, ncol = 3)
rsq <- coef <- matrix(rep(0.6, 9), nrow = 3, ncol = 3)
inputResults1 <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=50,
independent.var.type=2,
covar="",
continuous=1)
inputResults2 <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=-3,
covar="",
continuous=1)
testthat::expect_error(IntLIM::ProcessResults(inputResults1, inputData),
paste("Independent variable and outcome must both",
"be either 1 or 2."),
ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults2, inputData),
paste("Independent variable and outcome must both",
"be either 1 or 2."),
ignore.case = TRUE)
})
# The outcome and independent analytes must both be found in the InputData.
testthat::test_that("An error is thrown if an analyte type is used that is not present in the data.", {
# Generate input data.
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData1 <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=matrix(, nrow = 0, ncol = 0),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = as.data.frame(matrix(, nrow = 0, ncol = 0)),
sampleMetaData = pData)
inputData2 <- methods::new("IntLimData", analyteType1= matrix(, nrow = 0, ncol = 0),
analyteType2=as.matrix(geneData),
analyteType1MetaData = as.data.frame(matrix(, nrow = 0, ncol = 0)),
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(rep(0.2, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
coef <- matrix(rep(8, 9), nrow = 3, ncol = 3)
rsq <- coef <- matrix(rep(0.6, 9), nrow = 3, ncol = 3)
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=1)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData2),
paste("Outcome type is not present in original data"),
ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData1),
paste("Independent data type is not present in",
"original data"),
ignore.case = TRUE)
})
# The type variable must have two levels or be continuous.
testthat::test_that("More than two levels causes an error.", {
# Generate input data.
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c("Low","Medium","Low","Medium","Low","Medium",
"Low","High"))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(rep(0.2, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
coef <- matrix(rep(8, 9), nrow = 3, ncol = 3)
rsq <- coef <- matrix(rep(0.6, 9), nrow = 3, ncol = 3)
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=0)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData), paste(
"IntLim requires two categories only for correlation analysis. Make sure the column",
"Level only has two unique values or is continuous"), ignore.case = TRUE)
})
# Check that out-of-bounds values for interaction coefficient, r-squared,
# and p-value are not allowed.
testthat::test_that("Out of bounds values are not allowed.", {
# Generate input data.
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c("Low", "Medium", "Low", "Medium", "Medium", "Low",
"Low", "Medium"))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(rep(0.2, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
coef <- matrix(rep(8, 9), nrow = 3, ncol = 3)
rsq <- coef <- matrix(rep(0.6, 9), nrow = 3, ncol = 3)
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=0)
# Check boundaries
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData, pvalcutoff = -2), paste(
"P-value must be between 0 and 1"), ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData, pvalcutoff = 2), paste(
"P-value must be between 0 and 1"), ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData, rsquaredCutoff = -2), paste(
"R-squared value must be between 0 and 1"), ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData, rsquaredCutoff = 2), paste(
"R-squared value must be between 0 and 1"), ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData, interactionCoeffPercentile = -2), paste(
"Interaction coefficient percentile must be between 0 and 1"), ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData, interactionCoeffPercentile = 2), paste(
"Interaction coefficient percentile must be between 0 and 1"), ignore.case = TRUE)
})
# Check that everything is returned when there is no filtering.
testthat::test_that("Data is returned appropriately with no filtering.", {
# Generate input data (discrete).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c("Low", "Medium", "Low", "Medium", "Medium", "Low",
"Low", "Medium"))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate input data (continuous).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
inputDataCont <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(rep(0.2, 9), nrow = 3, ncol = 3)
rownames(pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(pvals) <- c("Metab1", "Metab2", "Metab3")
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
rownames(adj_pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(adj_pvals) <- c("Metab1", "Metab2", "Metab3")
coef <- matrix(rep(8, 9), nrow = 3, ncol = 3)
rownames(coef) <- c("Gene1", "Gene2", "Gene3")
colnames(coef) <- c("Metab1", "Metab2", "Metab3")
rsq <- coef <- matrix(rep(0.6, 9), nrow = 3, ncol = 3)
rownames(rsq) <- c("Gene1", "Gene2", "Gene3")
colnames(rsq) <- c("Metab1", "Metab2", "Metab3")
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=0)
# Check results
results <- IntLIM::ProcessResults(inputResults, inputData, pvalcutoff = 1,
interactionCoeffPercentile = 0, rsquaredCutoff = 0)
testthat::expect_identical(sort(unlist(inputResults@interaction.adj.pvalues)), sort(results$FDRadjPval))
testthat::expect_identical(sort(unlist(inputResults@interaction.pvalues)), sort(results$Pval))
testthat::expect_identical(sort(unlist(inputResults@model.rsquared)), sort(results$rsquared))
testthat::expect_identical(sort(unlist(inputResults@interaction.coefficients)), sort(results$interaction_coeff))
inputResults@continuous <- 1
results <- IntLIM::ProcessResults(inputResults, inputDataCont, pvalcutoff = 1,
interactionCoeffPercentile = 0, rsquaredCutoff = 0)
testthat::expect_identical(sort(unlist(inputResults@interaction.adj.pvalues)), sort(results$FDRadjPval))
testthat::expect_identical(sort(unlist(inputResults@interaction.pvalues)), sort(results$Pval))
testthat::expect_identical(sort(unlist(inputResults@model.rsquared)), sort(results$rsquared))
testthat::expect_identical(sort(unlist(inputResults@interaction.coefficients)), sort(results$interaction_coeff))
})
# Check coefficient filtering.
testthat::test_that("Check that coefficients are filtered as expected.", {
# Generate input data (discrete).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c("Low", "Medium", "Low", "Medium", "Medium", "Low",
"Low", "Medium"))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate input data (continuous).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
inputDataC <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(cbind(c(0.0005, 0.001, 0.002), c(0.003, 0.004, 0.005), c(0.006, 0.007, 0.008)),
nrow = 3, ncol = 3)
rownames(pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(pvals) <- c("Metab1", "Metab2", "Metab3")
adj_pvals <- matrix(cbind(c(0.05, 0.1, 0.2), c(0.3, 0.4, 0.5), c(0.6, 0.7, 0.8)),
nrow = 3, ncol = 3)
rownames(adj_pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(adj_pvals) <- c("Metab1", "Metab2", "Metab3")
coef <- matrix(cbind(c(1, 2, 3), c(4, 5, 6), c(7, 8, 9)),
nrow = 3, ncol = 3)
rownames(coef) <- c("Gene1", "Gene2", "Gene3")
colnames(coef) <- c("Metab1", "Metab2", "Metab3")
rsq <- matrix(cbind(c(0.1, 0.2, 0.3), c(0.4, 0.5, 0.6), c(0.7, 0.8, 0.9)),
nrow = 3, ncol = 3)
rownames(rsq) <- c("Gene1", "Gene2", "Gene3")
colnames(rsq) <- c("Metab1", "Metab2", "Metab3")
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,covar="",
continuous=0)
# Check results (discrete).
results <- IntLIM::ProcessResults(inputResults, inputData, pvalcutoff = 1,
interactionCoeffPercentile = 0.7, rsquaredCutoff = 0)
testthat::expect_equal(max(results$FDRadjPval), 0.8)
testthat::expect_equal(length(results$FDRadjPval), 3)
testthat::expect_equal(max(results$Pval), 0.008)
testthat::expect_equal(length(results$Pval), 3)
testthat::expect_equal(max(results$interaction_coeff), 9)
testthat::expect_equal(length(results$interaction_coeff), 3)
testthat::expect_equal(max(results$rsquared), 0.9)
testthat::expect_equal(length(results$rsquared), 3)
# Check results (continuous).
inputResults@continuous <- 1
results <- IntLIM::ProcessResults(inputResults, inputDataC, pvalcutoff = 1,
interactionCoeffPercentile = 0.7, rsquaredCutoff = 0)
testthat::expect_equal(max(results$FDRadjPval), 0.8)
testthat::expect_equal(length(results$FDRadjPval), 3)
testthat::expect_equal(max(results$Pval), 0.008)
testthat::expect_equal(length(results$Pval), 3)
testthat::expect_equal(max(results$interaction_coeff), 9)
testthat::expect_equal(length(results$interaction_coeff), 3)
testthat::expect_equal(max(results$rsquared), 0.9)
testthat::expect_equal(length(results$rsquared), 3)
})
# Check p-value filtering.
testthat::test_that("Check that p-values are filtered as expected.", {
# Generate input data (discrete).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c("Low", "Medium", "Low", "Medium", "Medium", "Low",
"Low", "Medium"))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate input data (continuous).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
inputDataC <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(cbind(c(0.0005, 0.001, 0.002), c(0.003, 0.004, 0.005), c(0.006, 0.007, 0.008)),
nrow = 3, ncol = 3)
rownames(pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(pvals) <- c("Metab1", "Metab2", "Metab3")
adj_pvals <- matrix(cbind(c(0.05, 0.1, 0.2), c(0.3, 0.4, 0.5), c(0.6, 0.7, 0.8)),
nrow = 3, ncol = 3)
rownames(adj_pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(adj_pvals) <- c("Metab1", "Metab2", "Metab3")
coef <- matrix(cbind(c(1, 2, 3), c(4, 5, 6), c(7, 8, 9)),
nrow = 3, ncol = 3)
rownames(coef) <- c("Gene1", "Gene2", "Gene3")
colnames(coef) <- c("Metab1", "Metab2", "Metab3")
rsq <- matrix(cbind(c(0.1, 0.2, 0.3), c(0.4, 0.5, 0.6), c(0.7, 0.8, 0.9)),
nrow = 3, ncol = 3)
rownames(rsq) <- c("Gene1", "Gene2", "Gene3")
colnames(rsq) <- c("Metab1", "Metab2", "Metab3")
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=0)
# Check results (discrete).
results <- IntLIM::ProcessResults(inputResults, inputData, pvalcutoff = 0.3,
interactionCoeffPercentile = 0, rsquaredCutoff = 0)
testthat::expect_equal(max(results$FDRadjPval), 0.3)
testthat::expect_equal(length(results$FDRadjPval), 4)
testthat::expect_equal(max(results$Pval), 0.003)
testthat::expect_equal(length(results$Pval), 4)
testthat::expect_equal(max(results$interaction_coeff), 4)
testthat::expect_equal(length(results$interaction_coeff), 4)
testthat::expect_equal(max(results$rsquared), 0.4)
testthat::expect_equal(length(results$rsquared), 4)
# Check results (continuous).
inputResults@continuous <- 1
results <- IntLIM::ProcessResults(inputResults, inputDataC, pvalcutoff = 0.3,
interactionCoeffPercentile = 0, rsquaredCutoff = 0)
testthat::expect_equal(max(results$FDRadjPval), 0.3)
testthat::expect_equal(length(results$FDRadjPval), 4)
testthat::expect_equal(max(results$Pval), 0.003)
testthat::expect_equal(length(results$Pval), 4)
testthat::expect_equal(max(results$interaction_coeff), 4)
testthat::expect_equal(length(results$interaction_coeff), 4)
testthat::expect_equal(max(results$rsquared), 0.4)
testthat::expect_equal(length(results$rsquared), 4)
})
# Check r-squared filtering
testthat::test_that("Check that R-squared values are filtered as expected.", {
# Generate input data (discrete).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c("Low", "Medium", "Low", "Medium", "Medium", "Low",
"Low", "Medium"))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate input data (continuous).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
inputDataC <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(cbind(c(0.0005, 0.001, 0.002), c(0.003, 0.004, 0.005), c(0.006, 0.007, 0.008)),
nrow = 3, ncol = 3)
rownames(pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(pvals) <- c("Metab1", "Metab2", "Metab3")
adj_pvals <- matrix(cbind(c(0.05, 0.1, 0.2), c(0.3, 0.4, 0.5), c(0.6, 0.7, 0.8)),
nrow = 3, ncol = 3)
rownames(adj_pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(adj_pvals) <- c("Metab1", "Metab2", "Metab3")
coef <- matrix(cbind(c(1, 2, 3), c(4, 5, 6), c(7, 8, 9)),
nrow = 3, ncol = 3)
rownames(coef) <- c("Gene1", "Gene2", "Gene3")
colnames(coef) <- c("Metab1", "Metab2", "Metab3")
rsq <- matrix(cbind(c(0.1, 0.2, 0.3), c(0.4, 0.5, 0.6), c(0.7, 0.8, 0.9)),
nrow = 3, ncol = 3)
rownames(rsq) <- c("Gene1", "Gene2", "Gene3")
colnames(rsq) <- c("Metab1", "Metab2", "Metab3")
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=1)
# Check results (discrete).
results <- IntLIM::ProcessResults(inputResults, inputData, pvalcutoff = 1,
interactionCoeffPercentile = 0, rsquaredCutoff = 0.4)
testthat::expect_equal(max(results$FDRadjPval), 0.8)
testthat::expect_equal(length(results$FDRadjPval), 6)
testthat::expect_equal(max(results$Pval), 0.008)
testthat::expect_equal(length(results$Pval), 6)
testthat::expect_equal(max(results$interaction_coeff), 9)
testthat::expect_equal(length(results$interaction_coeff), 6)
testthat::expect_equal(max(results$rsquared), 0.9)
testthat::expect_equal(length(results$rsquared), 6)
# Check results (continuous).
results <- IntLIM::ProcessResults(inputResults, inputDataC, pvalcutoff = 1,
interactionCoeffPercentile = 0, rsquaredCutoff = 0.4)
testthat::expect_equal(max(results$FDRadjPval), 0.8)
testthat::expect_equal(length(results$FDRadjPval), 6)
testthat::expect_equal(max(results$Pval), 0.008)
testthat::expect_equal(length(results$Pval), 6)
testthat::expect_equal(max(results$interaction_coeff), 9)
testthat::expect_equal(length(results$interaction_coeff), 6)
testthat::expect_equal(max(results$rsquared), 0.9)
testthat::expect_equal(length(results$rsquared), 6)
})
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# If a value other than an IntLimResults object is input, an error should be thrown.
testthat::test_that("Inputting the wrong class causes early termination.", {
# Generate input data.
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputDataGood <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
inputDataBad <- "Hello World"
# Generate result data.
pvals <- matrix(rep(0.2, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
coef <- matrix(rep(8, 9), nrow = 3, ncol = 3)
rsq <- coef <- matrix(rep(0.6, 9), nrow = 3, ncol = 3)
inputResultsGood <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=1)
inputResultsBad <- "Hello World"
testthat::expect_error(IntLIM::ProcessResults(inputResultsBad, inputDataGood),
paste("Results must be an IntLIMResults object"),
ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResultsGood, inputDataBad),
paste("Data must be an IntLIMData object"),
ignore.case = TRUE)
})
# Types for both outcome and independent analyte must be either 1 or 2.
testthat::test_that("Check that outcome and independent analyte types are appropriate.", {
# Generate input data.
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(rep(0.2, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
coef <- matrix(rep(8, 9), nrow = 3, ncol = 3)
rsq <- coef <- matrix(rep(0.6, 9), nrow = 3, ncol = 3)
inputResults1 <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=50,
independent.var.type=2,
covar="",
continuous=1)
inputResults2 <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=-3,
covar="",
continuous=1)
testthat::expect_error(IntLIM::ProcessResults(inputResults1, inputData),
paste("Independent variable and outcome must both",
"be either 1 or 2."),
ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults2, inputData),
paste("Independent variable and outcome must both",
"be either 1 or 2."),
ignore.case = TRUE)
})
# The outcome and independent analytes must both be found in the InputData.
testthat::test_that("An error is thrown if an analyte type is used that is not present in the data.", {
# Generate input data.
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData1 <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=matrix(, nrow = 0, ncol = 0),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = as.data.frame(matrix(, nrow = 0, ncol = 0)),
sampleMetaData = pData)
inputData2 <- methods::new("IntLimData", analyteType1= matrix(, nrow = 0, ncol = 0),
analyteType2=as.matrix(geneData),
analyteType1MetaData = as.data.frame(matrix(, nrow = 0, ncol = 0)),
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(rep(0.2, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
coef <- matrix(rep(8, 9), nrow = 3, ncol = 3)
rsq <- coef <- matrix(rep(0.6, 9), nrow = 3, ncol = 3)
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=1)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData2),
paste("Outcome type is not present in original data"),
ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData1),
paste("Independent data type is not present in",
"original data"),
ignore.case = TRUE)
})
# The type variable must have two levels or be continuous.
testthat::test_that("More than two levels causes an error.", {
# Generate input data.
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c("Low","Medium","Low","Medium","Low","Medium",
"Low","High"))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(rep(0.2, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
coef <- matrix(rep(8, 9), nrow = 3, ncol = 3)
rsq <- coef <- matrix(rep(0.6, 9), nrow = 3, ncol = 3)
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=0)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData), paste(
"IntLim requires two categories only for correlation analysis. Make sure the column",
"Level only has two unique values or is continuous"), ignore.case = TRUE)
})
# Check that out-of-bounds values for interaction coefficient, r-squared,
# and p-value are not allowed.
testthat::test_that("Out of bounds values are not allowed.", {
# Generate input data.
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c("Low", "Medium", "Low", "Medium", "Medium", "Low",
"Low", "Medium"))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(rep(0.2, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
coef <- matrix(rep(8, 9), nrow = 3, ncol = 3)
rsq <- coef <- matrix(rep(0.6, 9), nrow = 3, ncol = 3)
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=0)
# Check boundaries
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData, pvalcutoff = -2), paste(
"P-value must be between 0 and 1"), ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData, pvalcutoff = 2), paste(
"P-value must be between 0 and 1"), ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData, rsquaredCutoff = -2), paste(
"R-squared value must be between 0 and 1"), ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData, rsquaredCutoff = 2), paste(
"R-squared value must be between 0 and 1"), ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData, interactionCoeffPercentile = -2), paste(
"Interaction coefficient percentile must be between 0 and 1"), ignore.case = TRUE)
testthat::expect_error(IntLIM::ProcessResults(inputResults, inputData, interactionCoeffPercentile = 2), paste(
"Interaction coefficient percentile must be between 0 and 1"), ignore.case = TRUE)
})
# Check that everything is returned when there is no filtering.
testthat::test_that("Data is returned appropriately with no filtering.", {
# Generate input data (discrete).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c("Low", "Medium", "Low", "Medium", "Medium", "Low",
"Low", "Medium"))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate input data (continuous).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
inputDataCont <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(rep(0.2, 9), nrow = 3, ncol = 3)
rownames(pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(pvals) <- c("Metab1", "Metab2", "Metab3")
adj_pvals <- matrix(rep(1, 9), nrow = 3, ncol = 3)
rownames(adj_pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(adj_pvals) <- c("Metab1", "Metab2", "Metab3")
coef <- matrix(rep(8, 9), nrow = 3, ncol = 3)
rownames(coef) <- c("Gene1", "Gene2", "Gene3")
colnames(coef) <- c("Metab1", "Metab2", "Metab3")
rsq <- coef <- matrix(rep(0.6, 9), nrow = 3, ncol = 3)
rownames(rsq) <- c("Gene1", "Gene2", "Gene3")
colnames(rsq) <- c("Metab1", "Metab2", "Metab3")
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=0)
# Check results
results <- IntLIM::ProcessResults(inputResults, inputData, pvalcutoff = 1,
interactionCoeffPercentile = 0, rsquaredCutoff = 0)
testthat::expect_identical(sort(unlist(inputResults@interaction.adj.pvalues)), sort(results$FDRadjPval))
testthat::expect_identical(sort(unlist(inputResults@interaction.pvalues)), sort(results$Pval))
testthat::expect_identical(sort(unlist(inputResults@model.rsquared)), sort(results$rsquared))
testthat::expect_identical(sort(unlist(inputResults@interaction.coefficients)), sort(results$interaction_coeff))
inputResults@continuous <- 1
results <- IntLIM::ProcessResults(inputResults, inputDataCont, pvalcutoff = 1,
interactionCoeffPercentile = 0, rsquaredCutoff = 0)
testthat::expect_identical(sort(unlist(inputResults@interaction.adj.pvalues)), sort(results$FDRadjPval))
testthat::expect_identical(sort(unlist(inputResults@interaction.pvalues)), sort(results$Pval))
testthat::expect_identical(sort(unlist(inputResults@model.rsquared)), sort(results$rsquared))
testthat::expect_identical(sort(unlist(inputResults@interaction.coefficients)), sort(results$interaction_coeff))
})
# Check coefficient filtering.
testthat::test_that("Check that coefficients are filtered as expected.", {
# Generate input data (discrete).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c("Low", "Medium", "Low", "Medium", "Medium", "Low",
"Low", "Medium"))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate input data (continuous).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
inputDataC <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(cbind(c(0.0005, 0.001, 0.002), c(0.003, 0.004, 0.005), c(0.006, 0.007, 0.008)),
nrow = 3, ncol = 3)
rownames(pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(pvals) <- c("Metab1", "Metab2", "Metab3")
adj_pvals <- matrix(cbind(c(0.05, 0.1, 0.2), c(0.3, 0.4, 0.5), c(0.6, 0.7, 0.8)),
nrow = 3, ncol = 3)
rownames(adj_pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(adj_pvals) <- c("Metab1", "Metab2", "Metab3")
coef <- matrix(cbind(c(1, 2, 3), c(4, 5, 6), c(7, 8, 9)),
nrow = 3, ncol = 3)
rownames(coef) <- c("Gene1", "Gene2", "Gene3")
colnames(coef) <- c("Metab1", "Metab2", "Metab3")
rsq <- matrix(cbind(c(0.1, 0.2, 0.3), c(0.4, 0.5, 0.6), c(0.7, 0.8, 0.9)),
nrow = 3, ncol = 3)
rownames(rsq) <- c("Gene1", "Gene2", "Gene3")
colnames(rsq) <- c("Metab1", "Metab2", "Metab3")
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,covar="",
continuous=0)
# Check results (discrete).
results <- IntLIM::ProcessResults(inputResults, inputData, pvalcutoff = 1,
interactionCoeffPercentile = 0.7, rsquaredCutoff = 0)
testthat::expect_equal(max(results$FDRadjPval), 0.8)
testthat::expect_equal(length(results$FDRadjPval), 3)
testthat::expect_equal(max(results$Pval), 0.008)
testthat::expect_equal(length(results$Pval), 3)
testthat::expect_equal(max(results$interaction_coeff), 9)
testthat::expect_equal(length(results$interaction_coeff), 3)
testthat::expect_equal(max(results$rsquared), 0.9)
testthat::expect_equal(length(results$rsquared), 3)
# Check results (continuous).
inputResults@continuous <- 1
results <- IntLIM::ProcessResults(inputResults, inputDataC, pvalcutoff = 1,
interactionCoeffPercentile = 0.7, rsquaredCutoff = 0)
testthat::expect_equal(max(results$FDRadjPval), 0.8)
testthat::expect_equal(length(results$FDRadjPval), 3)
testthat::expect_equal(max(results$Pval), 0.008)
testthat::expect_equal(length(results$Pval), 3)
testthat::expect_equal(max(results$interaction_coeff), 9)
testthat::expect_equal(length(results$interaction_coeff), 3)
testthat::expect_equal(max(results$rsquared), 0.9)
testthat::expect_equal(length(results$rsquared), 3)
})
# Check p-value filtering.
testthat::test_that("Check that p-values are filtered as expected.", {
# Generate input data (discrete).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c("Low", "Medium", "Low", "Medium", "Medium", "Low",
"Low", "Medium"))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate input data (continuous).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
inputDataC <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(cbind(c(0.0005, 0.001, 0.002), c(0.003, 0.004, 0.005), c(0.006, 0.007, 0.008)),
nrow = 3, ncol = 3)
rownames(pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(pvals) <- c("Metab1", "Metab2", "Metab3")
adj_pvals <- matrix(cbind(c(0.05, 0.1, 0.2), c(0.3, 0.4, 0.5), c(0.6, 0.7, 0.8)),
nrow = 3, ncol = 3)
rownames(adj_pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(adj_pvals) <- c("Metab1", "Metab2", "Metab3")
coef <- matrix(cbind(c(1, 2, 3), c(4, 5, 6), c(7, 8, 9)),
nrow = 3, ncol = 3)
rownames(coef) <- c("Gene1", "Gene2", "Gene3")
colnames(coef) <- c("Metab1", "Metab2", "Metab3")
rsq <- matrix(cbind(c(0.1, 0.2, 0.3), c(0.4, 0.5, 0.6), c(0.7, 0.8, 0.9)),
nrow = 3, ncol = 3)
rownames(rsq) <- c("Gene1", "Gene2", "Gene3")
colnames(rsq) <- c("Metab1", "Metab2", "Metab3")
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=0)
# Check results (discrete).
results <- IntLIM::ProcessResults(inputResults, inputData, pvalcutoff = 0.3,
interactionCoeffPercentile = 0, rsquaredCutoff = 0)
testthat::expect_equal(max(results$FDRadjPval), 0.3)
testthat::expect_equal(length(results$FDRadjPval), 4)
testthat::expect_equal(max(results$Pval), 0.003)
testthat::expect_equal(length(results$Pval), 4)
testthat::expect_equal(max(results$interaction_coeff), 4)
testthat::expect_equal(length(results$interaction_coeff), 4)
testthat::expect_equal(max(results$rsquared), 0.4)
testthat::expect_equal(length(results$rsquared), 4)
# Check results (continuous).
inputResults@continuous <- 1
results <- IntLIM::ProcessResults(inputResults, inputDataC, pvalcutoff = 0.3,
interactionCoeffPercentile = 0, rsquaredCutoff = 0)
testthat::expect_equal(max(results$FDRadjPval), 0.3)
testthat::expect_equal(length(results$FDRadjPval), 4)
testthat::expect_equal(max(results$Pval), 0.003)
testthat::expect_equal(length(results$Pval), 4)
testthat::expect_equal(max(results$interaction_coeff), 4)
testthat::expect_equal(length(results$interaction_coeff), 4)
testthat::expect_equal(max(results$rsquared), 0.4)
testthat::expect_equal(length(results$rsquared), 4)
})
# Check r-squared filtering
testthat::test_that("Check that R-squared values are filtered as expected.", {
# Generate input data (discrete).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c("Low", "Medium", "Low", "Medium", "Medium", "Low",
"Low", "Medium"))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
geneData <- data.frame("P1"=c(46.1,20.2,59.3), "P2"=c(11.1,34.2,19.3),
"P3"=c(28.1,71.2,94.3), "P4"=c(51.1,91.2,32.3),
"P5"=c(73.1,26.2,40.3), "P6"=c(91.1,99.2,12.3),
"P7"=c(38.1,44.2,60.3), "P8"=c(91.1,93.2,63.3))
rownames(geneData) <- c("Gene1", "Gene2", "Gene3")
metabData <- data.frame("P1"=c(60.1,32.2,81.3), "P2"=c(68.1,58.2,45.3),
"P3"=c(30.1,61.2,67.3), "P4"=c(36.1,7.2,79.3),
"P5"=c(5.1,87.2,91.3), "P6"=c(5.1,87.2,91.3),
"P7"=c(99.1,10.2,85.3), "P8"=c(51.1,14.2,76.3))
rownames(metabData) <- c("Metab1", "Metab2", "Metab3")
metabMetaData <- data.frame("id"=c("Metab1", "Metab2", "Metab3"), "metabname"=
c("Metab1", "Metab2", "Metab3"))
geneMetaData <- data.frame("id"=c("Gene1", "Gene2", "Gene3"), "genename"=
c("Gene1", "Gene2", "Gene3"))
inputData <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate input data (continuous).
pData <- data.frame("Feat1"=c(47.1,26.2,84.3,98.4,43.5,82.6,13.7,87.8),
"Feat2"=c(37.1,40.2,80.3,83.4,6.5,12.6,43.7,75.8),
"Feat3"=c(14.1,74.2,11.3,19.4,73.5,55.6,18.7,91.8),
"Level"=c(62.1,44.2,42.3,14.4,58.5,95.6,91.7,1.8))
rownames(pData) <- c("P1", "P2", "P3", "P4", "P5", "P6",
"P7", "P8")
inputDataC <- methods::new("IntLimData", analyteType1=as.matrix(metabData),
analyteType2=as.matrix(geneData),
analyteType1MetaData = metabMetaData,
analyteType2MetaData = geneMetaData,
sampleMetaData = pData)
# Generate result data.
pvals <- matrix(cbind(c(0.0005, 0.001, 0.002), c(0.003, 0.004, 0.005), c(0.006, 0.007, 0.008)),
nrow = 3, ncol = 3)
rownames(pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(pvals) <- c("Metab1", "Metab2", "Metab3")
adj_pvals <- matrix(cbind(c(0.05, 0.1, 0.2), c(0.3, 0.4, 0.5), c(0.6, 0.7, 0.8)),
nrow = 3, ncol = 3)
rownames(adj_pvals) <- c("Gene1", "Gene2", "Gene3")
colnames(adj_pvals) <- c("Metab1", "Metab2", "Metab3")
coef <- matrix(cbind(c(1, 2, 3), c(4, 5, 6), c(7, 8, 9)),
nrow = 3, ncol = 3)
rownames(coef) <- c("Gene1", "Gene2", "Gene3")
colnames(coef) <- c("Metab1", "Metab2", "Metab3")
rsq <- matrix(cbind(c(0.1, 0.2, 0.3), c(0.4, 0.5, 0.6), c(0.7, 0.8, 0.9)),
nrow = 3, ncol = 3)
rownames(rsq) <- c("Gene1", "Gene2", "Gene3")
colnames(rsq) <- c("Metab1", "Metab2", "Metab3")
inputResults <- methods::new("IntLimResults",
interaction.pvalues=pvals,
interaction.adj.pvalues=adj_pvals,
interaction.coefficients=coef,
model.rsquared = rsq,
covariate.pvalues = data.frame(matrix(, nrow = 0, ncol = 0)),
covariate.coefficients = data.frame(matrix(, nrow = 0, ncol = 0)),
corr=data.frame(matrix(, nrow = 0, ncol = 0)),
filt.results=data.frame(matrix(, nrow = 0, ncol = 0)),
warnings=list(),
stype="Level",
outcome=1,
independent.var.type=2,
covar="",
continuous=1)
# Check results (discrete).
results <- IntLIM::ProcessResults(inputResults, inputData, pvalcutoff = 1,
interactionCoeffPercentile = 0, rsquaredCutoff = 0.4)
testthat::expect_equal(max(results$FDRadjPval), 0.8)
testthat::expect_equal(length(results$FDRadjPval), 6)
testthat::expect_equal(max(results$Pval), 0.008)
testthat::expect_equal(length(results$Pval), 6)
testthat::expect_equal(max(results$interaction_coeff), 9)
testthat::expect_equal(length(results$interaction_coeff), 6)
testthat::expect_equal(max(results$rsquared), 0.9)
testthat::expect_equal(length(results$rsquared), 6)
# Check results (continuous).
results <- IntLIM::ProcessResults(inputResults, inputDataC, pvalcutoff = 1,
interactionCoeffPercentile = 0, rsquaredCutoff = 0.4)
testthat::expect_equal(max(results$FDRadjPval), 0.8)
testthat::expect_equal(length(results$FDRadjPval), 6)
testthat::expect_equal(max(results$Pval), 0.008)
testthat::expect_equal(length(results$Pval), 6)
testthat::expect_equal(max(results$interaction_coeff), 9)
testthat::expect_equal(length(results$interaction_coeff), 6)
testthat::expect_equal(max(results$rsquared), 0.9)
testthat::expect_equal(length(results$rsquared), 6)
})
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