library(moanin)
library(limma)
data(exampleData)
context("moanin::de_analysis.R")
test_that("time-course DE analysis", {
#change group variable to check that works everywhere
colnames(testMeta)<-c("Index","condition","replicate","time")
moanin_model = create_moanin_model(data=testData,
meta=testMeta,
group_variable_name="condition",
time_variable_name="time")
# Create a second moanin model with counts
moanin_model_counts = create_moanin_model(
data=round(exp(testData)), meta=testMeta,
log_transform=TRUE,
group_variable_name="condition",
time_variable_name="time")
# Do only one contrast
contrast_formulas = c("K-M")
contrast = limma::makeContrasts(
contrasts=contrast_formulas,
levels=levels(droplevels(testMeta$condition)))
expect_silent(moanin::DE_timecourse(
moanin_model, contrasts=contrast,
use_voom_weights=FALSE))
expect_silent(moanin::DE_timecourse(
moanin_model_counts, contrasts=contrast,
use_voom_weights=TRUE))
# Now test with several contrasts
contrast_formulas = c("K-M", "C-M")
contrasts = limma::makeContrasts(
contrasts=contrast_formulas,
levels=levels(droplevels(testMeta$condition)))
expect_silent(moanin::DE_timecourse(moanin_model, contrasts=contrast,
use_voom_weights=FALSE))
expect_silent(moanin::DE_timecourse(
moanin_model_counts, contrasts=contrast,
use_voom_weights=TRUE))
# Check that the names of the columns make sense.
de_results = moanin::DE_timecourse(
moanin_model, contrasts=contrast,
use_voom_weights=FALSE)
})
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