context("test-s4_objects")
test_that("Support for SummarizedExperiment works", {
experimental_design <- rep(LETTERS[1:2], each=4)
data <- generate_synthetic_data(n_rows = 35, experimental_design,
rho=18, zeta=-1.2,
nu=10, eta=0.3, mu0=20, sigma20=10)
library(SummarizedExperiment)
sample_info <- data.frame(
condition = experimental_design,
replicate = proDD:::as_replicate(experimental_design),
row.names = colnames(data$X)
)
protein_info <- data.frame(
ID = seq_len(35),
changed = data$changed,
variance = data$sigmas2,
mu_a = data$mus[, "A"],
mu_b = data$mus[, "B"],
row.names = rownames(data$X)
)
se <- SummarizedExperiment(data$X, colData = sample_info, rowData = protein_info)
norm_se <- median_normalization(se)
expect_equal(assay(norm_se), median_normalization(data$X))
set.seed(1)
se_params <- fit_parameters(se, "condition")
set.seed(1)
std_params <- fit_parameters(data$X, experimental_design)
expect_equal(se_params, std_params, tolerance = 1e-3)
se_res <- sample_protein_means(norm_se, se_params, nchains=2, verbose = FALSE)
se_dist <- dist_approx(se, se_params)
expect_equal(se_dist, dist_approx(data$X, std_params), tolerance = 1e-3)
})
test_that("Support for MSnSet works", {
experimental_design <- rep(LETTERS[1:2], each=4)
data <- generate_synthetic_data(n_rows = 35, experimental_design,
rho=18, zeta=-1.2,
nu=10, eta=0.3, mu0=20, sigma20=10)
library(MSnbase)
sample_info_adf <- AnnotatedDataFrame(data.frame(
condition = experimental_design,
replicate = as_replicate(experimental_design),
row.names = colnames(data$X)
))
protein_info_adf <- AnnotatedDataFrame(data.frame(
ID = seq_len(35),
changed = data$changed,
variance = data$sigmas2,
mu_a = data$mus[, "A"],
mu_b = data$mus[, "B"],
row.names = rownames(data$X)
))
ms <- MSnSet(exprs = data$X, pData = sample_info_adf, fData = protein_info_adf)
norm_ms <- median_normalization(ms)
expect_equal(exprs(norm_ms), median_normalization(data$X))
set.seed(1)
ms_params <- fit_parameters(ms, "condition")
set.seed(1)
std_params <- fit_parameters(data$X, experimental_design)
expect_equal(ms_params, std_params, tolerance = 1e-3)
ms_res <- sample_protein_means(norm_ms, ms_params, nchains=2, verbose = FALSE)
ms_dist <- dist_approx(ms, ms_params)
expect_equal(ms_dist, dist_approx(data$X, std_params), tolerance = 1e-3)
})
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