tests/testthat/test-trainNMF.R

In MarcElosua/SPOTlight: `SPOTlight`: Spatial Transcriptomics Deconvolution

set.seed(321)
# mock up some single-cell, mixture & marker data
sce <- mockSC(ng = 200, nc = 10, nt = 3)
spe <- mockSP(sce)
mgs <- getMGS(sce)

# Create dummy Seurat object
# sec <- suppressWarnings(SeuratObject::CreateSeuratObject(counts = counts(sce)))
# sep <- suppressWarnings(SeuratObject::CreateSeuratObject(counts = counts(spe)))

# Function to run the checks
.checks <- function(res, sce) {
    mod <- res[[1]]
    mtr <- res[[2]]
    expect_is(res, "list")
    expect_is(mtr, "matrix")
    expect_is(mod, "NMFfit")
    expect_identical(ncol(mtr), length(unique(sce$type)))
    expect_identical(sort(rownames(mtr)), sort(unique(as.character(sce$type))))
    expect_identical(nrow(mtr), ncol(basis(mod)))
    expect_identical(nrow(mtr), nrow(coef(mod)))
}


# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ----  Check trainNMF x, y inputs  -------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# trainNMF with SCE ----
test_that("trainNMF x SCE", {
    res <- trainNMF(
        x = sce,
        y = as.matrix(counts(spe)),
        groups = sce$type,
        mgs = mgs,
        weight_id = "weight",
        group_id = "type",
        gene_id = "gene"
    )
    
    .checks(res, sce)
})

# trainNMF with SPE ----
test_that("trainNMF x SPE", {
    res <- trainNMF(
        x = as.matrix(counts(sce)),
        y = spe,
        groups = sce$type,
        mgs = mgs,
        weight_id = "weight",
        group_id = "type",
        gene_id = "gene"
    )
    
    .checks(res, sce)
})

# trainNMF with SPE ----
# test_that("trainNMF x SEC", {
#     res <- trainNMF(
#         x = sec,
#         y = as.matrix(counts(spe)),
#         groups = sce$type,
#         mgs = mgs,
#         weight_id = "weight",
#         group_id = "type",
#         gene_id = "gene"
#     )
#     
#     .checks(res, sce)
# })

# trainNMF with SEP ----
# test_that("trainNMF x SEP", {
#     res <- trainNMF(
#         x = as.matrix(counts(sce)),
#         y = sep,
#         groups = sce$type,
#         mgs = mgs,
#         weight_id = "weight",
#         group_id = "type",
#         gene_id = "gene",
#         assay_sp = "RNA"
#     )
#     
#     .checks(res, sce)
# })


# trainNMF with sparse matrix sc ----
test_that("trainNMF x dgCMatrix SC", {
    res <- trainNMF(
        x = Matrix::Matrix(counts(sce), sparse = TRUE),
        y = as.matrix(counts(spe)),
        groups = sce$type,
        mgs = mgs,
        weight_id = "weight",
        group_id = "type",
        gene_id = "gene"
    )
    .checks(res, sce)
})

# trainNMF with sparse matrix sp ----
test_that("trainNMF x dgCMatrix SP", {
    res <- trainNMF(
        x = as.matrix(counts(sce)),
        y = Matrix::Matrix(counts(spe), sparse = TRUE),
        groups = sce$type,
        mgs = mgs,
        weight_id = "weight",
        group_id = "type",
        gene_id = "gene"
    )
    
    .checks(res, sce)
})

# trainNMF with sparse matrix sc ----
test_that("trainNMF x DelayedMatrix SC", {
    res <- trainNMF(
        x = DelayedArray::DelayedArray(counts(sce)),
        y = as.matrix(counts(spe)),
        groups = sce$type,
        mgs = mgs,
        weight_id = "weight",
        group_id = "type",
        gene_id = "gene"
    )
    .checks(res, sce)
})

# trainNMF with sparse matrix sp ----
test_that("trainNMF x DelayedMatrix SP", {
    res <- trainNMF(
        x = as.matrix(counts(sce)),
        y = DelayedArray::DelayedArray(counts(sce)),
        groups = sce$type,
        mgs = mgs,
        weight_id = "weight",
        group_id = "type",
        gene_id = "gene"
    )
    
    .checks(res, sce)
})

# trainNMF with matrices in both ----
test_that("trainNMF x matrices", {
    res <- trainNMF(
        x = as.matrix(counts(sce)),
        y = as.matrix(counts(spe)),
        groups = sce$type,
        mgs = mgs,
        weight_id = "weight",
        group_id = "type",
        gene_id = "gene"
    )
    
    .checks(res, sce)
})

# trainNMF with matrices in both and HVG----
test_that("trainNMF x hvg", {
    res <- trainNMF(
        x = as.matrix(counts(sce)),
        y = as.matrix(counts(spe)),
        groups = sce$type,
        mgs = mgs,
        weight_id = "weight",
        group_id = "type",
        gene_id = "gene",
        hvg = row.names(sce)[seq_len(50)]
    )
    
    .checks(res, sce)
})