Nothing
test_that("check covariates", {
for (i in seq(1, n_datasets)) {
r_mat <- r_mats[[i]]
# first, look at cell-wise stats
cell_covariates <- cov_odms[[i]]@cell_covariates
for (curr_col in colnames(cell_covariates)) {
# n nonzero
if (curr_col == "n_nonzero") {
test <- Matrix::colSums(r_mat >= 1)
} else if (curr_col == "n_umis") {
test <- Matrix::colSums(r_mat)
} else if (curr_col == "p_mito") {
gene_names <- get_feature_names(cov_odms[[i]]@ondisc_matrix)
mt_genes <- grep(pattern = "^MT-", x = gene_names)
mt_counts <- r_mat[mt_genes,]
test <- Matrix::colSums(mt_counts)/Matrix::colSums(r_mat)
}
expect_equal(test, cell_covariates[,curr_col])
}
# next, look at gene-wise stats
feature_covariates <- cov_odms[[i]]@feature_covariates
for (curr_col in colnames(feature_covariates)) {
if (curr_col == "n_nonzero") {
test <- Matrix::rowSums(r_mat >= 1)
} else if (curr_col == "mean_expression") {
test <- Matrix::rowMeans(r_mat)
} else if (curr_col == "coef_of_variation") {
n_cells <- ncol(r_mat)
my_vars <- Matrix::rowSums(r_mat^2)/n_cells - (Matrix::rowSums(r_mat)/n_cells)^2
my_means <- Matrix::rowMeans(r_mat)
test <- sqrt(my_vars)/my_means
}
expect_equal(test, feature_covariates[,curr_col])
}
}
})
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