Nothing
library(tmod)
data(Egambia)
data(tmod)
context("Statistical tests")
tt <- read.csv( "sample_data.csv", stringsAsFactors=FALSE)[,1]
testRes <- function(res, res_e, test_auc=TRUE) {
expect_equal(nrow(res), nrow(res_e))
expect_equal(ncol(res), ncol(res_e))
expect_identical(res$ID, res_e$ID)
expect_identical(res$Title, res_e$Title)
if(test_auc) {
expect_equal(res$AUC, res_e$AUC)
}
}
test_that("tmodCERNO", {
res <- tmodCERNOtest(tt, mset="LI")
res_e <- read.csv("res_CERNO.csv", stringsAsFactors=FALSE, row.names=1)
testRes(res, res_e)
})
test_that("tmodUtest", {
res <- tmodUtest(tt, mset="LI")
res_e <- read.csv("res_U.csv", stringsAsFactors=FALSE, row.names=1)
testRes(res, res_e)
})
test_that("tmodHGtest", {
fg <- head(tt, 100)
res <- tmodHGtest(fg, tt, mset="LI")
res_e <- read.csv("res_HG.csv", stringsAsFactors=FALSE, row.names=1)
testRes(res, res_e)
})
test_that("tmodLEA", {
res_e <- read.csv("res_LEA.csv", stringsAsFactors=FALSE, row.names=1)
res <- tmodLEASummary(tmodLEA(tt, c("LI.M37.0", "LI.M75", "LI.M3"), mset="LI"))
testRes(res, res_e, test_auc=FALSE)
expect_equal(res$N, res_e$N)
expect_equal(res$fraction, res_e$fraction)
expect_equal(res$LEA, res_e$LEA)
})
test_that("tmodPLAGEtest", {
res_e <- read.csv("res_PLAGE.csv", stringsAsFactors=FALSE, row.names=1)
x <- Egambia[ , -1:-3 ]
l <- Egambia$GENE_SYMBOL
group <- gsub("\\..*", "", colnames(x))
res <- tmodPLAGEtest(l, x, group, mset="LI")
testRes(res, res_e, test_auc=FALSE)
})
test_that("tmodZtest", {
res_e <- read.csv("res_Z.csv", stringsAsFactors=FALSE, row.names=1)
res <- tmodZtest(tt, mset="LI")
testRes(res, res_e, test_auc=FALSE)
})
test_that("tmodGeneSetTest", {
res_e <- read.csv("res_GeneSetTest.csv", stringsAsFactors=FALSE, row.names=1)
x <- Egambia[ , -1:-3 ]
l <- Egambia$GENE_SYMBOL
group <- gsub("\\..*", "", colnames(x))
dd <- rowMeans(x[ , group == "NID" ]) - rowMeans(x[ , group == "TB" ])
dd <- dd / apply(x, 1, sd)
res <- tmodGeneSetTest(l, dd, Nsim=100, mset="LI")
expect_equal(ncol(res), ncol(res_e))
#rr <- merge(res, res_e, by=c("ID", "Title"))
#expect_gte(cor(rr$D.x, rr$D.y), .8)
#expect_gte(cor(rr$P.Value.x, rr$P.Value.y, method="s"), .8)
})
test_that("tmodAUC", {
res_e <- read.csv("res_AUC.csv", stringsAsFactors=FALSE, row.names=1)
res <- data.frame(tmodAUC(tt, 1:length(tt), mset="LI"))
expect_equal(nrow(res), sum(tmod$gs$SourceID == "LI"))
expect_equal(ncol(res), 1)
expect_equal(res[,1], res_e[,1])
})
test_that("tmodDecideTests", {
res_tt <- read.csv("sample_results.csv")
res_e <- read.csv("res_tmodDecideTests.csv", row.names=1)
res <- tmodDecideTests(res_tt$GENE_SYMBOL, lfc=res_tt$logFC, pval=res_tt$adj.P.Val)
expect_equal(length(res), 1)
expect_is(res, "list")
res <- as.data.frame(res[[1]])
expect_equal(res_e, res)
})
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