inst/unitTests/test_Solver.R

In PriceLab/trena: Fit transcriptional regulatory networks using gene expression, priors, machine learning

library(trena)
library(RUnit)
#----------------------------------------------------------------------------------------------------
printf <- function(...) print(noquote(sprintf(...)))
#----------------------------------------------------------------------------------------------------
runTests <- function()
{
    test_getAssayData()
    test_getTarget()
    test_getRegulators()
    test_eliminateSelfTFs()
    test_MatrixWarnings()
    test_TargetAndRegulatorWarnings()

} # runTests
#----------------------------------------------------------------------------------------------------
test_getAssayData <- function()
{
    printf("--- test_getAssayData")
    mtx <- matrix(rnorm(9), nrow = 3)
    rownames(mtx) <- c("gene1","gene2","gene3")
    solver <- Solver(mtx, "gene1", c("gene2","gene3"))
    # (29 feb 2020): this next tests fails with
    #   R Under development (unstable) (2020-01-28 r77731)
    # checkEquals(class(getAssayData(solver)), "matrix")
    checkEquals(mtx, getAssayData(solver))

    } # test_getAssayData
#----------------------------------------------------------------------------------------------------
test_getTarget <- function()
{
    printf("--- test_getTarget")
    mtx <- matrix(rnorm(9), nrow = 3)
    rownames(mtx) <- c("gene1","gene2","gene3")
    solver <- Solver(mtx, "gene1", c("gene2","gene3"))
    checkEquals(class(getTarget(solver)), "character")
    checkEquals("gene1", getTarget(solver))

    } # test_getTarget
#----------------------------------------------------------------------------------------------------
test_getRegulators <- function()
{
    printf("--- test_getRegulators")
    mtx <- matrix(9:1, nrow = 3)
    rownames(mtx) <- c("gene1","gene2","gene3")
    solver <- Solver(mtx, "gene1", c("gene2","gene3"))
    checkEquals(length(getRegulators(solver)), 2)
    checkEquals(c("gene2","gene3"), getRegulators(solver))

    } # test_getRegulators
#----------------------------------------------------------------------------------------------------
test_eliminateSelfTFs <- function()
{
   printf("--- test_eliminateSelfTFs")

   set.seed(10045)
   load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))

   mtx.asinh <- asinh(mtx.sub)
   target.gene <- "MEF2C"
   tfs <- rownames(mtx.asinh)

   solver <- PearsonSolver(mtx.asinh, target.gene, tfs)

   checkTrue(target.gene %in% tfs)         # our test case
   tbl.betas <- run(solver)
   checkTrue(!target.gene %in% rownames(tbl.betas))

   solver2 <- LassoSolver(mtx.asinh, target.gene, tfs)
   tbl.betas2 <- run(solver2)
   checkTrue(!target.gene %in% rownames(tbl.betas2))

} # test_eliminateSelfTFs
#----------------------------------------------------------------------------------------------------
test_MatrixWarnings <- function()
{
    printf("--- test_MatrixWarnings")

    # Change warnings to errors
    options(warn = 2)

    # Check that a skewed matrix produces an error
    test.mtx <- matrix(1:10000, nrow = 100)
    test.mtx[1,1] <- 1e7
    rownames(test.mtx) <- paste0("gene",1:100)
    target.gene <- "gene1"
    tfs <- setdiff(rownames(test.mtx),target.gene)
    checkException(Solver(test.mtx,target.gene,tfs), silent = TRUE)

    # Check that a matrix with a row of 0's produces an error for most solvers
    test.mtx[1,] <- 0
    checkException(BayesSpikeSolver(test.mtx, target.gene, tfs), silent = TRUE)
    checkException(LassoPVSolver(test.mtx, target.gene, tfs), silent = TRUE)
    checkException(RandomForestSolver(test.mtx, target.gene, tfs), silent = TRUE)
    checkException(PearsonSolver(test.mtx, target.gene, tfs), silent = TRUE)
    checkException(SpearmanSolver(test.mtx, target.gene, tfs), silent = TRUE)
    checkException(EnsembleSolver(test.mtx, target.gene, tfs), silent = TRUE)

    # Check that a target gene with low expression causes a warning for all solvers
    test.mtx[1,] <- 0.1
    checkException(BayesSpikeSolver(test.mtx, target.gene, tfs), silent = TRUE)
    checkException(LassoPVSolver(test.mtx, target.gene, tfs), silent = TRUE)
    checkException(RandomForestSolver(test.mtx, target.gene, tfs), silent = TRUE)
    checkException(PearsonSolver(test.mtx, target.gene, tfs), silent = TRUE)
    checkException(SpearmanSolver(test.mtx, target.gene, tfs), silent = TRUE)
    checkException(EnsembleSolver(test.mtx, target.gene, tfs), silent = TRUE)
    checkException(LassoSolver(test.mtx, target.gene, tfs), silent = TRUE)
    checkException(RidgeSolver(test.mtx, target.gene, tfs), silent = TRUE)

    # Change warnings back to warnings
    options(warn = 1)

} #test_MatrixWarnings
#----------------------------------------------------------------------------------------------------
test_TargetAndRegulatorWarnings <- function()
{
    printf("--- test_TargetAndRegulatorWarnings")

    # Change warnings to errors
    options(warn = 2)

    # Make fake data
    test.mtx <- matrix(rnorm(10000), nrow = 100)
    rownames(test.mtx) <- paste0("gene",1:100)

    # Test for error with empty target and empty candidates
    checkException(Solver(test.mtx, targetGene = character(0),
                          candidateRegulators = rownames(test.mtx)), silent = TRUE)
    checkException(Solver(test.mtx, targetGene = "gene1",
                          candidateRegulators = character(0)), silent = TRUE)

    options(warn = 1)
} #test_TargetAndRegulatorWarnings
#----------------------------------------------------------------------------------------------------
if(!interactive()) runTests()