Nothing
context("Classes and Methods")
library(tibble)
library(DESeq2)
y <- matrix(rnbinom(80L, size = 1L, mu = 10L), nrow = 20L)
d <- DGEList(counts = y, group = rep(1L:2L, each = 2L),
lib.size = rep(c(1000L:1001L), 2L))
rownames(d[["counts"]]) <- paste("gene", 1L:nrow(d[["counts"]]), sep=".")
d <- estimateCommonDisp(d)
de <- exactTest(d)
rawR <- topTags(de, n = Inf)
dds <- makeExampleDESeqDataSet(betaSD = 1)
colData(dds)[["treatment"]] <- sample(colData(dds)[["condition"]], 12)
design(dds) <- ~ condition + treatment
dds <- DESeq(dds)
raw <- results(dds)
res <- degComps(dds, combs = c("condition"))
test_that("DEGSet",{
expect_type(as.DEGSet(raw, extras = list(shrunken = raw)), "list")
expect_error(as.DEGSet(raw, extras = list(shrunken = rawR)),
"DESeqResults")
expect_type(as.DEGSet(rawR, extras = list(shrunken = rawR)), "list")
expect_error(as.DEGSet(rawR, extras = list(shrunken = raw)), "TopTags")
expect_true(abs(deg(res, "raw")[["log2FoldChange"]][[1]]) >
abs(deg(res, "shrunken")[["log2FoldChange"]][[1]]))
expect_match(degDefault(res), "shrunken")
expect_error(deg(res, "fake"))
expect_identical(res[["shrunken"]], deg(res))
expect_true(deg(res, tidy = "tibble") %>% is_tibble)
expect_equal(deg(res, top = 5) %>% nrow, 5)
expect_type(significants(res), "character")
expect_type(significants(res[[1]]), "character")
expect_type(significants(res), "character")
expect_true(significants(res, full = TRUE) %>% is_tibble)
# expect_s4_class(degCorrect(res, "lfdr-stat"), "DEGSet")
expect_true(degMA(res, diff = 4) %>%
class %>% .[[2]] == "ggplot")
expect_true(degMA(res, diff = 4, raw = TRUE) %>%
class %>% .[[2]] == "ggplot")
expect_true(degMA(res, diff = 4, correlation = TRUE) %>%
class %>% .[[2]] == "ggplot")
expect_error(degMA(res, diff = 4, raw = TRUE, correlation = TRUE))
})
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