library(trena)
library(RUnit)
#----------------------------------------------------------------------------------------------------
printf <- function(...) print(noquote(sprintf(...)))
#----------------------------------------------------------------------------------------------------
runTests <- function()
{
test_PearsonSolverConstructor()
test_ampAD.mef2c.154tfs.278samples.pearson()
} # runTests
#----------------------------------------------------------------------------------------------------
test_PearsonSolverConstructor <- function()
{
printf("--- test_PearsonSolverConstructor")
mtx <- matrix(1:9,nrow=3)
rownames(mtx) <- c("gene1","gene2","gene3")
solver <- PearsonSolver(mtx,targetGene = "gene1",
candidateRegulators = c("gene2","gene3"))
checkEquals(class(solver)[1], "PearsonSolver")
checkTrue(all(c("PearsonSolver", "Solver") %in% is(solver)))
} # test_PearsonSolverConstructor
#----------------------------------------------------------------------------------------------------
test_ampAD.mef2c.154tfs.278samples.pearson <- function()
{
printf("--- test_ampAD.mef2c.154tfs.278samples.pearson")
# Load matrix and transform via arcsinh
load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
mtx.asinh <- asinh(mtx.sub)
target.gene <- "MEF2C"
tfs <- setdiff(rownames(mtx.asinh), "MEF2C")
#print(fivenum(mtx.asinh) # [1] 0.000000 1.327453 3.208193 4.460219 7.628290)
pearson.solver <- PearsonSolver(mtx.asinh, target.gene, tfs)
tbl <- run(pearson.solver)
# Check for empirical values
checkTrue(nrow(subset(tbl, abs(coefficient) > 0.8)) == 8)
} # test_ampAD.mef2c.154tfs.278samples.pearson
#----------------------------------------------------------------------------------------------------
if(!interactive()) runTests()
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