Nothing
library(trena)
library(RUnit)
#----------------------------------------------------------------------------------------------------
printf <- function(...) print(noquote(sprintf(...)))
#----------------------------------------------------------------------------------------------------
runTests <- function()
{
test_LassoPVSolverConstructor()
test_ampAD.mef2c.154tfs.278samples.lassopv()
} # runTests
#----------------------------------------------------------------------------------------------------
test_LassoPVSolverConstructor <- function()
{
printf("--- test_LassoPvSolverConstructor")
mtx <- matrix(1:9,nrow=3)
rownames(mtx) <- c("gene1","gene2","gene3")
solver <- LassoPVSolver(mtx,targetGene = "gene1",
candidateRegulators = c("gene2","gene3"))
checkEquals(class(solver)[1], "LassoPVSolver")
checkTrue(all(c("LassoPVSolver", "Solver") %in% is(solver)))
}
# test_LassoPVSolverConstructor
#----------------------------------------------------------------------------------------------------
test_ampAD.mef2c.154tfs.278samples.lassopv <- function()
{
printf("--- test_ampAD.mef2c.154tfs.278samples.lassopv")
# 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)
lassopv.solver <- LassoPVSolver(mtx.asinh, target.gene, tfs)
tbl <- run(lassopv.solver)
# Check for significant P-values; make sure they match the empirical value
sig.genes <- tbl$pValues[tbl$pValues < 0.01]
checkEquals(length(sig.genes),30)
checkTrue(max(-log10(sig.genes)) > 54)
} # test_ampAD.mef2c.154tfs.278samples.lassopv
#----------------------------------------------------------------------------------------------------
if(!interactive()) runTests()
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