library(decoupleR)
# Directories -------------------------------------------------------------
# Inputs
input_dir <- system.file("testdata", "inputs", package = "decoupleR")
# Outputs
expected_dir <- system.file("testdata", "outputs", package = "decoupleR")
# Data to run -------------------------------------------------------------
mat <- file.path(input_dir, "mat.rds") %>%
readRDS()
net <- file.path(input_dir, "net.rds") %>%
readRDS()
# Common expressions ------------------------------------------------------
# Available statistics
statistics <- c(
'udt',
'mdt',
'aucell',
'wmean',
'wsum',
'ulm',
'mlm',
'viper',
'gsva',
'ora',
'fgsea'
)
# Arguments for statistics; same order as statistics vector.
args <- list(
udt = list(),
mdt = list(trees=1000),
aucell = list(nproc=1, aucMaxRank=3),
wmean = list(),
wsum = list(),
ulm = list(),
viper = list(),
gsva = list(),
ora = list(n_up=3, n_bottom=3),
fgsea = list()
)
partial_decouple <- purrr::partial(
.f = decouple,
mat = mat,
network = net,
.source = source,
.target = target,
statistics = statistics,
minsize = 0,
args = args
)
# decouple() --------------------------------------------------------------
test_that("decouple same results as independent functions", {
# Choose the same defaults as in the section on generating expected results.
res_decouple_defaults <- suppressWarnings(partial_decouple(
show_toy_call = FALSE,
include_time = TRUE
) %>%
dplyr::select(-.data$run_id, -statistic_time) %>%
dplyr::filter(statistic != 'consensus') %>%
dplyr::arrange(.data$statistic, .data$source, .data$condition))
exp_decouple_defaults <- file.path(
expected_dir,
"decouple",
"output-decouple.rds"
) %>%
readRDS() %>%
dplyr::arrange(.data$statistic, .data$source, .data$condition)
expect_equal(res_decouple_defaults, exp_decouple_defaults, tolerance=0.1)
})
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