# Tests the options in the various reduced dimension functions

#############################################
# Check the feature selection and scaling work.

test_that("feature selection is operational", {
    out <- scater:::.get_mat_for_reddim(logcounts(normed), subset_row = seq_len(nrow(normed)), scale = FALSE)
    expect_equal(out, t(logcounts(normed)))

    # Ntop selection works.
    out <- scater:::.get_mat_for_reddim(logcounts(normed), ntop = 10, subset_row = NULL, scale = FALSE)
    rv <- DelayedMatrixStats::rowVars(DelayedArray(logcounts(normed)))
    keep <- head(order(rv, decreasing = TRUE), 10)
    expect_equal(out, t(logcounts(normed)[keep, ]))

    out <- scater:::.get_mat_for_reddim(logcounts(normed), ntop = Inf, subset_row = NULL, scale = FALSE)
    o <- order(rv, decreasing = TRUE)
    expect_equal(out, t(logcounts(normed)[o,]))

    # Feature selection works.
    out <- scater:::.get_mat_for_reddim(logcounts(normed), subset_row = 10:1, scale=FALSE)
    expect_equal(out, t(logcounts(normed)[10:1, ]))

    out <- scater:::.get_mat_for_reddim(logcounts(normed), subset_row = rownames(normed)[10:1], scale = FALSE)
    expect_equal(out, t(logcounts(normed)[10:1, ]))
})

test_that("scaling by feature variances work correctly", {
    logcounts(normed)[100,] <- 0
    MAT <- t(logcounts(normed))
    cv <- DelayedMatrixStats::colVars(DelayedArray(MAT))
    novar <- cv < 1e-8
    expect_true(any(novar))

    NOMAT <- MAT[,!novar]
    scaled <- t(t(NOMAT) / sqrt(cv[!novar]))

    out <- scater:::.get_mat_for_reddim(logcounts(normed), subset_row = seq_len(nrow(normed)), scale = TRUE)
    expect_equal(out, scaled)
    out <- scater:::.get_mat_for_reddim(logcounts(normed), subset_row = 10:1, scale = TRUE)
    expect_equal(out, scaled[, 10:1])

    out <- scater:::.get_mat_for_reddim(logcounts(normed), subset_row=NULL, ntop = 10, scale = TRUE) # In combination with non-trivial selection.
    rv <- DelayedMatrixStats::rowVars(DelayedArray(logcounts(normed)))
    expect_equal(out, scaled[,head(order(rv[!novar], decreasing = TRUE), 10)])
})

#############################################
# Check PCA.

test_that("runPCA works as expected", {
    # Using ExactParam() simply to avoid issues due to randomness.
    normedX <- runPCA(normed)
    expect_identical(reducedDimNames(normedX), "PCA")
    fullN <- min(dim(normed), 50L)
    expect_identical(dim(reducedDim(normedX, "PCA")), c(ncol(normed), fullN))

    normedX <- runPCA(normed, ncol(normed))
    expect_equal(sum(attr(reducedDim(normedX), "percentVar")), 100)

    # Checking that it works with a restricted number of components.
    normedX <- runPCA(normed, ncomponents = 4)
    expect_identical(reducedDimNames(normedX), "PCA")
    expect_identical(dim(reducedDim(normedX, "PCA")), c(ncol(normedX), 4L))
    expect_true(sum(attr(reducedDim(normedX), "percentVar")) < 100)
})


test_that("snifter option works with dimred set", {
    # Using ExactParam() simply to avoid issues due to randomness.
    normedX <- runPCA(normed)
    expect_error(normedX <- runTSNE(normedX, dimred = "PCA"), NA)
})

test_that("runPCA responds to changes to various settings", {
    # Testing that various settings give different results.
    normed2 <- runPCA(normed)
    normed3 <- runPCA(normed, scale = TRUE)

    fullN <- min(dim(normed), 50L)
    expect_identical(ncol(reducedDim(normed2)), fullN)
    expect_identical(ncol(reducedDim(normed3)), fullN)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    normed3 <- runPCA(normed, ntop = 100)
    expect_identical(ncol(reducedDim(normed3)), fullN)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    normed3 <- runPCA(normed, exprs_values = "counts")
    expect_identical(ncol(reducedDim(normed3)), fullN)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    normed3 <- runPCA(normed, subset_row = 1:100)
    expect_identical(ncol(reducedDim(normed3)), fullN)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))
})

test_that("runPCA handles ntop selection", {
    most_var <- DelayedMatrixStats::rowVars(DelayedArray(logcounts(normed)))
    keep <- head(order(most_var, decreasing = TRUE), 100)
    normed3 <- runPCA(normed, ncomponents = 4, subset_row = keep)
    normed4 <- runPCA(normed, ncomponents = 4, ntop = 100)
    expect_equal(reducedDim(normed3), reducedDim(normed4))
})

test_that("runPCA names its outputs correctly", {
    normedX <- runPCA(normed, ntop = Inf)
    expect_true(!is.null(rownames(normedX)))
    expect_true(!is.null(colnames(normedX)))

    rd <- reducedDim(normedX)
    expect_identical(rownames(rd), colnames(normedX))

    rot <- attr(rd, "rotation")
    v <- DelayedMatrixStats::rowVars(logcounts(normedX))
    expect_identical(rownames(rot), rownames(normedX)[order(v, decreasing = TRUE)])

    # What happens without row names?
    unnamed <- normed
    dimnames(unnamed) <- NULL
    normedX <- runPCA(unnamed, ntop = Inf)

    rd <- reducedDim(normedX)
    rot <- attr(rd, "rotation")
    v <- DelayedMatrixStats::rowVars(logcounts(normedX))
    expect_identical(rownames(rot), as.character(order(v, decreasing = TRUE)))
})

test_that("runPCA handles scaling", {
    # Setting ntop=Inf, otherwise it will pick 'normed_alt' features based on scaled variance.
    normed_alt <- normed
    rescaled <- t(scale(t(logcounts(normed_alt)), scale = TRUE))
    rescaled[is.na(rescaled)] <- 0
    logcounts(normed_alt) <- rescaled

    normed3 <- runPCA(normed_alt, ncomponents = 4, scale = FALSE, ntop = Inf)
    normed4 <- runPCA(normed, ncomponents = 4, scale = TRUE, ntop = Inf)

    r3 <- reducedDim(normed3)
    rot3 <- attr(r3, "rotation")
    attr(r3, "rotation") <- NULL

    r4 <- reducedDim(normed4)
    rot4 <- attr(r4, "rotation")
    attr(r4, "rotation") <- NULL

    expect_equal(r3, r4)

    # Checking that the rotation vectors are correct.
    combined <- union(rownames(rot4), rownames(rot3))
    expect_equal(rot3[combined, ], rot4[combined, ])

    # Checking that percentVar is computed correctly.
    normed3 <- runPCA(normed, scale = TRUE, ncol(normed))
    expect_equal(sum(attr(reducedDim(normed3), "percentVar")), 100)
})

test_that("runPCA behaves with alternative assays", {
    normed_alt <- normed
    assay(normed_alt, "whee") <- logcounts(normed)
    logcounts(normed_alt) <- NULL

    normed3 <- runPCA(normed_alt, ncomponents = 4, exprs_values = "whee")
    normed4 <- runPCA(normed, ncomponents = 4)
    expect_identical(reducedDim(normed3), reducedDim(normed4))
})

test_that("runColDataPCA works as expected for QC metrics", {
    normed$field1 <- runif(ncol(normed))
    normed$field2 <- runif(ncol(normed))
    normed$field3 <- runif(ncol(normed))
    normed$field4 <- runif(ncol(normed))

    vars <- c("field1", "field2", "field3", "field4")
    normed <- runColDataPCA(normed, variables = vars)
    out <- reducedDim(normed, "PCA_coldata")
    ref <- calculatePCA(t(as.matrix(colData(normed)[, vars])), ncomponents = 2, scale = TRUE)
    expect_true(all(abs(abs(colMeans(out / ref)) - 1) < 1e-6))

    # Checking outlier detection works correctly.
    expect_identical(normed$outlier, NULL)
    expect_warning(normed <- runColDataPCA(normed, variables = vars, outliers = TRUE), NA)
    expect_type(normed$outlier, "logical")
    expect_identical(length(normed$outlier), ncol(normed))
})

test_that("runPCA works with irlba code", {
    set.seed(10)
    normedX <- runPCA(normed, ncomponents = 4, BSPARAM = BiocSingular::IrlbaParam())
    expect_identical(reducedDimNames(normedX), "PCA")
    expect_identical(dim(reducedDim(normedX, "PCA")), c(ncol(normedX), 4L))
    expect_identical(length(attr(reducedDim(normedX), "percentVar")), 4L)
    expect_true(sum(attr(reducedDim(normedX), "percentVar")) < 100)

    # Checking that seed setting works.
    set.seed(100)
    normedX2 <- runPCA(normed, ncomponents = 4, BSPARAM = BiocSingular::IrlbaParam())
    set.seed(100)
    normedX3 <- runPCA(normed, ncomponents = 4, BSPARAM = BiocSingular::IrlbaParam())
    expect_false(isTRUE(all.equal(reducedDim(normedX), reducedDim(normedX2))))
    expect_identical(reducedDim(normedX2), reducedDim(normedX3))
})

#############################################
# Check t-SNE.

test_that("runTSNE works as expected", {
    set.seed(100)
    normedX <- runTSNE(normed, ncomponents = 3)
    expect_identical(reducedDimNames(normedX), "TSNE")
    expect_identical(dim(reducedDim(normedX, "TSNE")), c(ncol(normedX), 3L))

    # Testing that setting the seed actually works.
    set.seed(100)
    normed2 <- runTSNE(normed)
    set.seed(100)
    normed3 <- runTSNE(normed)
    expect_equal(reducedDim(normed2), reducedDim(normed3))

    ## Avoid testing on 32bit win
    if (.Machine$sizeof.pointer == 8) {
        # same with snifter
        normed2 <- runTSNE(normed, use_fitsne = TRUE, random_state = 100L)
        normed3 <- runTSNE(normed, use_fitsne = TRUE, random_state = 100L)
        expect_equal(reducedDim(normed2), reducedDim(normed3))
    }

    # Testing that various settings have some effect. 
    set.seed(100)
    normed3 <- runTSNE(normed, scale=TRUE)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runTSNE(normed, ntop = 100)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runTSNE(normed, exprs_values = "counts")
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runTSNE(normed, subset_row = 1:100)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runTSNE(normed, perplexity = 5)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runTSNE(normed, pca = FALSE)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runTSNE(normed, initial_dims = 10)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runTSNE(normed, normalize = FALSE)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runTSNE(normed, theta = 0.1)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))


})

test_that("runTSNE on existing reduced dimension results works as expected", {
    # Function should not respond to any feature settings.
    ## extra seed set because of BiocParallel changes
    set.seed(9)
    normedP <- runPCA(normed, ncomponents = 4)
    set.seed(10)
    normed2 <- runTSNE(normedP, dimred = "PCA")

    set.seed(10)
    normed3 <- runTSNE(normedP, dimred = "PCA", ntop = 20)
    expect_identical(reducedDim(normed2, "TSNE"), reducedDim(normed3, "TSNE"))

    set.seed(10)
    normed3 <- runTSNE(normedP, dimred = "PCA", scale = TRUE)
    expect_identical(reducedDim(normed2, "TSNE"), reducedDim(normed3, "TSNE"))

    set.seed(10)
    normed3 <- runTSNE(normedP, dimred = "PCA", subset_row = 1:20)
    expect_identical(reducedDim(normed2, "TSNE"), reducedDim(normed3, "TSNE"))

    set.seed(10)
    normed3 <- runTSNE(normedP, dimred = "PCA", pca = FALSE)
    expect_identical(reducedDim(normed2, "TSNE"), reducedDim(normed3, "TSNE"))

    set.seed(10)
    normed3 <- runTSNE(normedP, dimred = "PCA", initial_dims = 10)
    expect_identical(reducedDim(normed2, "TSNE"), reducedDim(normed3, "TSNE"))

    set.seed(10)
    normed3 <- runTSNE(normedP, dimred = "PCA", n_dimred = 3)
    expect_false(isTRUE(all.equal(reducedDim(normed2, "TSNE"), reducedDim(normed3, "TSNE"))))
})

test_that("runTSNE works with externally computed nearest neighbor results", {
    skip_on_os("windows") # https://github.com/jkrijthe/Rtsne/commit/f3f42504eeac627e4d886b1489ee289f8f9d082b#comments

    normedP <- runPCA(normed, ncomponents = 20)

    # Need to set the random seed to avoid different RNG states after the NN search. 
    set.seed(20) 
    init <- matrix(rnorm(ncol(normedP)*2), ncol = 2)

    ref <- runTSNE(normedP, dimred = "PCA", Y_init = init)
    alt <- runTSNE(normedP, dimred = "PCA", Y_init = init, external_neighbors = TRUE)
    expect_identical(reducedDim(ref, "TSNE"), reducedDim(alt, "TSNE"))

    ref <- runTSNE(normedP, dimred = "PCA", Y_init = init, perplexity = 8.6)
    alt <- runTSNE(normedP, dimred = "PCA", Y_init = init, perplexity = 8.6, external_neighbors = TRUE)
    expect_identical(reducedDim(ref, "TSNE"), reducedDim(alt, "TSNE"))

    ref <- runTSNE(normedP, dimred = "PCA", Y_init = init, theta = 0.1)
    alt <- runTSNE(normedP, dimred = "PCA", Y_init = init, theta = 0.1, external_neighbors = TRUE)
    expect_identical(reducedDim(ref, "TSNE"), reducedDim(alt, "TSNE"))

    ref <- runTSNE(normedP, dimred = "PCA", Y_init = init, normalize = FALSE)
    alt <- runTSNE(normedP, dimred = "PCA", Y_init = init, normalize = FALSE, external_neighbors = TRUE)
    expect_identical(reducedDim(ref, "TSNE"), reducedDim(alt, "TSNE"))

    # Works with alternative neighbor searching options.
    ref <- runTSNE(normedP, dimred = "PCA", Y_init = init)
    alt <- runTSNE(normedP, dimred = "PCA", Y_init = init, external_neighbors = TRUE, BNPARAM = BiocNeighbors::VptreeParam())
    expect_identical(reducedDim(ref, "TSNE"), reducedDim(alt, "TSNE"))

    ref <- runTSNE(normedP, dimred = "PCA", Y_init = init)
    alt <- runTSNE(normedP, dimred = "PCA", Y_init = init, external_neighbors = TRUE, BPPARAM = safeBPParam(2))
    expect_identical(reducedDim(ref, "TSNE"), reducedDim(alt, "TSNE"))
})

#############################################
# Check UMAP.

test_that("runUMAP works as expected", {
    set.seed(100)
    normedX <- runUMAP(normed, ncomponents = 3)
    expect_identical(reducedDimNames(normedX), "UMAP")
    expect_identical(dim(reducedDim(normedX, "UMAP")), c(ncol(normedX), 3L))

    # Testing that setting the seed actually works.
    set.seed(100)
    normed2 <- runUMAP(normed)
    set.seed(100)
    normed3 <- runUMAP(normed)
    expect_equal(reducedDim(normed2), reducedDim(normed3))

    # Testing that various settings have some effect.
    set.seed(100)
    normed3 <- runUMAP(normed, scale = TRUE)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runUMAP(normed, ntop = 100)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runUMAP(normed, exprs_values = "counts")
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runUMAP(normed, subset_row = 1:100)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))
})

test_that("runUMAP on existing reduced dimension results works as expected", {
    # Function should not respond to any feature settings.
    set.seed(9)
    normedP <- runPCA(normed, ncomponents = 4)
    set.seed(10)
    normed2 <- runUMAP(normedP, dimred = "PCA")

    set.seed(10)
    normed3 <- runUMAP(normedP, dimred = "PCA", ntop = 20)
    expect_identical(reducedDim(normed2, "UMAP"), reducedDim(normed3, "UMAP"))

    set.seed(10)
    normed3 <- runUMAP(normedP, dimred = "PCA", scale = TRUE)
    expect_identical(reducedDim(normed2, "UMAP"), reducedDim(normed3, "UMAP"))

    set.seed(10)
    normed3 <- runUMAP(normedP, dimred = "PCA", subset_row = 1:20)
    expect_identical(reducedDim(normed2, "UMAP"), reducedDim(normed3, "UMAP"))

    set.seed(10)
    normed3 <- runUMAP(normedP, dimred = "PCA", n_dimred = 3)
    expect_false(isTRUE(all.equal(reducedDim(normed2, "UMAP"), reducedDim(normed3, "UMAP"))))
})


test_that("multi-modal UMAP works as expected", {
    stuff <- matrix(rnorm(10000), ncol = 50)
    things <- list(stuff, stuff[, 1:5], stuff[, 1:20])
    metrics <- scater:::.compute_multi_modal_metrics(things)
    expect_identical(unname(lengths(metrics)), vapply(things, ncol, 0L))
    expect_identical(unname(unlist(metrics)), seq_len(sum(vapply(things, ncol, 0L))))

    output <- calculateMultiUMAP(things)
    expect_identical(nrow(output), nrow(stuff))
    expect_identical(ncol(output), 2L)

    set.seed(9999)
    output <- calculateMultiUMAP(things, n_components = 10)
    expect_identical(nrow(output), nrow(stuff))
    expect_identical(ncol(output), 10L)

    # Same result for SCEs.
    sce <- SingleCellExperiment(list(X = t(stuff)), reducedDims = list(Y = stuff[,1:5]), altExps = list(Z = SummarizedExperiment(t(stuff[, 1:20]))))

    set.seed(9999)
    output2 <- runMultiUMAP(sce, exprs_values = 1, dimred = 1, altexp = 1, altexp_exprs_values = 1, n_components = 10)
    expect_identical(output, reducedDim(output2, "MultiUMAP"))
})

#############################################
# Check NMF.

test_that("runNMF works as expected", {
    normedX <- runNMF(normed, ncomponents = 3)
    expect_identical(reducedDimNames(normedX), "NMF")
    expect_identical(dim(reducedDim(normedX, "NMF")), c(ncol(normedX), 3L))

    # Testing that various settings work.
    set.seed(100)
    normed2 <- runNMF(normed)

    set.seed(100)
    normed3 <- runNMF(normed, scale = TRUE)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runNMF(normed, ntop = 100)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runNMF(normed, exprs_values = "counts")
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    set.seed(100)
    normed3 <- runNMF(normed, subset_row = 1:100)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    # Testing out the use of existing reduced dimensions
    # (this should not respond to any feature settings).
    normedP <- runPCA(normed, ncomponents = 4)
    reducedDim(normedP, "PCA") <- abs(reducedDim(normedP, "PCA"))

    set.seed(100)
    normed2 <- runNMF(normedP, dimred = "PCA")

    set.seed(100)
    normed3 <- runNMF(normedP, dimred = "PCA", ntop = 20)
    expect_identical(reducedDim(normed2, "NMF"), reducedDim(normed3, "NMF"))

    set.seed(100)
    normed3 <- runNMF(normedP, dimred = "PCA", scale = TRUE)
    expect_identical(reducedDim(normed2, "NMF"), reducedDim(normed3, "NMF"))

    set.seed(100)
    normed3 <- runNMF(normedP, dimred = "PCA", subset_row = 1:20)
    expect_identical(reducedDim(normed2, "NMF"), reducedDim(normed3, "NMF"))
})

#############################################
# Check MDS.

test_that("runMDS works as expected", {
    normedX <- runMDS(normed, ncomponents = 3)
    expect_identical(reducedDimNames(normedX), "MDS")
    expect_identical(dim(reducedDim(normedX, "MDS")), c(ncol(normedX), 3L))

    # Testing that various settings work.
    normed2 <- runMDS(normed)
    normed3 <- runMDS(normed, scale = TRUE)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    normed3 <- runMDS(normed, ntop = 100)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    normed3 <- runMDS(normed, exprs_values = "counts")
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    normed3 <- runMDS(normed, subset_row = 1:100)
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    normed3 <- runMDS(normed, method = "manhattan")
    expect_false(isTRUE(all.equal(reducedDim(normed2), reducedDim(normed3))))

    # Testing out the use of existing reduced dimensions
    # (this should not respond to any feature settings).
    normedP <- runPCA(normed, ncomponents = 4)
    normed2 <- runMDS(normedP, dimred = "PCA")

    normed3 <- runMDS(normedP, dimred = "PCA", ntop = 20)
    expect_identical(reducedDim(normed2, "MDS"), reducedDim(normed3, "MDS"))

    normed3 <- runMDS(normedP, dimred = "PCA", scale = TRUE)
    expect_identical(reducedDim(normed2, "MDS"), reducedDim(normed3, "MDS"))

    normed3 <- runMDS(normedP, dimred = "PCA", subset_row = 1:20)
    expect_identical(reducedDim(normed2, "MDS"), reducedDim(normed3, "MDS"))

    # This does, in fact, happen to be equal,
    # due to the relationship between MDS and PCA.
    # This is not identifiable by the sign, hence the finagling.
    normed3 <- runMDS(normedP, dimred = "PCA", n_dimred = 3)
    fold <- reducedDim(normed2, "MDS") / reducedDim(normed3, "MDS")
    expect_equal(abs(colSums(fold)), rep(nrow(fold), ncol(fold)))
})

#############################################
# Check defences against sparse matrices.

test_that("run* functions work with sparse matrices", {
    library(Matrix)
    counts(normed) <- as(counts(normed), "dgCMatrix")
    logcounts(normed) <- as(logcounts(normed), "dgCMatrix")

    expect_error(runPCA(normed), NA)
    expect_error(runPCA(normed, BSPARAM = BiocSingular::IrlbaParam()), NA)
    expect_error(runTSNE(normed), NA)
    expect_error(runNMF(normed), NA)
    expect_error(runUMAP(normed), NA)
    expect_error(runMDS(normed), NA)
})

test_that("projectReducedDim works as expected", {
    example_sce <- mockSCE()
    example_sce <- logNormCounts(example_sce)
    example_sce <- runUMAP(example_sce)
    example_sce <- runPCA(example_sce)

    example_sce_new <- mockSCE()
    example_sce_new <- logNormCounts(example_sce_new)
    example_sce_new <- runPCA(example_sce_new)

    projected <- projectReducedDim(
        reducedDim(example_sce, "PCA"),
        reducedDim(example_sce_new, "PCA"),
        reducedDim(example_sce, "UMAP")
    )
    projected_sce <- projectReducedDim(
        example_sce_new,
        example_sce,
        "UMAP"
    )
    expect_equal(projected, reducedDim(projected_sce, "UMAP"))
    expect_true(
        min(projected) > min(reducedDim(example_sce, "UMAP")),
        max(projected) < max(reducedDim(example_sce, "UMAP")),
    )

})


test_that("plotReducedDim works with point_shape", {
    example_sce <- mockSCE()
    example_sce <- logNormCounts(example_sce)
    example_sce <- runUMAP(example_sce)
    p1 <- plotReducedDim(example_sce, "UMAP", point_shape = 15)
    expect_equal(p1$layers[[1]]$aes_params$shape, 15)
    p2 <- plotReducedDim(example_sce, "UMAP", point_shape = 12)
    expect_equal(p2$layers[[1]]$aes_params$shape, 12)
})

