Nothing
context("compare_list")
test_that("warning if matrix is not binarized", {
pbmc_mm <- matrixize_markers(pbmc_markers)
pbmc_avg <- average_clusters(pbmc_matrix_small,
pbmc_meta,
cluster_col = "classified"
)
pbmc_avgb <- binarize_expr(pbmc_avg)
gene_list_methods <- c("hyper")
expect_warning(results <- lapply(
gene_list_methods,
function(x) {
compare_lists(pbmc_avg,
pbmc_mm,
metric = x
)
}
))
})
test_that("run all gene list functions", {
pbmc_mm <- matrixize_markers(pbmc_markers)
pbmc_avg <- average_clusters(pbmc_matrix_small,
pbmc_meta,
cluster_col = "classified"
)
pbmc_avgb <- binarize_expr(pbmc_avg)
gene_list_methods <- c("spearman", "hyper", "jaccard", "gsea")
results <- lapply(
gene_list_methods,
function(x) {
compare_lists(pbmc_avgb,
pbmc_mm,
metric = x
)
}
)
expect_equal(4, length(results))
})
test_that("gene list function options", {
pbmc_mm <- matrixize_markers(pbmc_markers)
pbmc_avg <- average_clusters(pbmc_matrix_small,
pbmc_meta,
cluster_col = "classified"
)
pbmc_avgb <- binarize_expr(pbmc_avg)
expect_error(suppressWarnings(
res <- compare_lists(
pbmc_avgb,
pbmc_mm,
metric = "hyper",
output_high = FALSE,
n = 5
)
))
})
test_that("run all gene list functions in clustify_lists", {
gene_list_methods <- c("spearman", "hyper", "jaccard", "gsea")
results <- lapply(
gene_list_methods,
function(x) {
clustify_lists(
pbmc_matrix_small,
per_cell = FALSE,
metadata = pbmc_meta,
cluster_col = "classified",
marker = pbmc_markers,
marker_inmatrix = FALSE,
metric = x
)
}
)
expect_equal(4, length(results))
})
test_that("gsea outputs in cor matrix format", {
res <- clustify_lists(
pbmc_matrix_small,
per_cell = FALSE,
metadata = pbmc_meta,
cluster_col = "classified",
marker = pbmc_markers,
marker_inmatrix = FALSE,
metric = "gsea"
)
res2 <- cor_to_call(res)
expect_equal(9, nrow(res2))
})
test_that("seurat object clustify_lists-ing", {
res <- clustify_lists(
s_small,
per_cell = FALSE,
marker = pbmc_markers,
marker_inmatrix = FALSE,
metric = "jaccard",
cluster_col = "res.1",
seurat_out = FALSE,
dr = "tsne"
)
res <- clustify_lists(
s_small,
per_cell = FALSE,
marker = pbmc_markers,
marker_inmatrix = FALSE,
metric = "jaccard",
cluster_col = "res.1",
seurat_out = FALSE,
dr = "tsne"
)
g <- plot_best_call(
res,
seurat_meta(s_small,
dr = "tsne"
),
cluster_col = "res.1",
plot_r = TRUE,
x = "tSNE_1",
y = "tSNE_2"
)
expect_true(ggplot2::is.ggplot(g[[1]]))
})
test_that("clustify_lists inserts seurat metadata correctly", {
res <- clustify_lists(
s_small,
per_cell = FALSE,
marker = pbmc_markers,
marker_inmatrix = FALSE,
metric = "jaccard",
cluster_col = "res.1",
seurat_out = TRUE,
dr = "tsne"
)
res2 <- clustify_lists(
s_small,
per_cell = TRUE,
marker = pbmc_markers,
marker_inmatrix = FALSE,
metric = "jaccard",
cluster_col = "res.1",
seurat_out = TRUE,
dr = "tsne"
)
if ("Seurat" %in% loadedNamespaces()) {
expect_true(class(res) %in% c("seurat"))
} else {
expect_true(is.matrix(res))
}
})
test_that("seurat3 object clustify_lists-ing", {
res <- clustify_lists(
s_small3,
per_cell = FALSE,
marker = pbmc_markers,
marker_inmatrix = FALSE,
metric = "jaccard",
cluster_col = "RNA_snn_res.1",
seurat_out = TRUE,
dr = "tsne"
)
res <- clustify_lists(
s_small3,
per_cell = FALSE,
marker = pbmc_markers,
marker_inmatrix = FALSE,
metric = "jaccard",
cluster_col = "RNA_snn_res.1",
seurat_out = FALSE,
dr = "tsne"
)
g <- plot_best_call(
res,
seurat_meta(s_small3,
dr = "tsne"
),
cluster_col = "RNA_snn_res.1",
plot_r = TRUE,
x = "tSNE_1",
y = "tSNE_2"
)
expect_true(ggplot2::is.ggplot(g[[1]]))
})
test_that("clustify_lists inserts seurat3 metadata correctly", {
res <- clustify_lists(
s_small3,
per_cell = FALSE,
marker = pbmc_markers,
marker_inmatrix = FALSE,
metric = "jaccard",
cluster_col = "RNA_snn_res.1",
seurat_out = TRUE,
dr = "tsne"
)
res2 <- clustify_lists(
s_small3,
per_cell = TRUE,
marker = pbmc_markers,
marker_inmatrix = FALSE,
metric = "jaccard",
cluster_col = "RNA_snn_res.1",
seurat_out = TRUE,
dr = "tsne"
)
if ("Seurat" %in% loadedNamespaces()) {
expect_true(class(res) %in% c("Seurat"))
} else {
expect_true(is.matrix(res))
}
})
test_that("run all gene list functions and then use consensus_call", {
pbmc_mm <- matrixize_markers(pbmc_markers)
pbmc_avg <- average_clusters(pbmc_matrix_small,
pbmc_meta,
cluster_col = "classified"
)
pbmc_avgb <- binarize_expr(pbmc_avg)
gene_list_methods <- c("spearman", "hyper", "jaccard", "gsea")
results <- lapply(
gene_list_methods,
function(x) {
compare_lists(pbmc_avgb,
pbmc_mm,
metric = x
)
}
)
call_list <- lapply(
results,
cor_to_call_rank
)
calls <- call_consensus(call_list)
expect_equal(4, length(results))
})
test_that("run all gene list functions in clustify_lists", {
res <- clustify_lists(
pbmc_matrix_small,
cbmc_m,
metadata = pbmc_meta,
cluster_col = "classified",
metric = "consensus"
)
expect_equal(9, nrow(res))
})
test_that("run all gene list functions in clustify_lists and seurat object", {
res <- clustify_lists(
s_small3,
marker = cbmc_m,
dr = "tsne",
cluster_col = "RNA_snn_res.1",
metric = "consensus",
seurat_out = TRUE
)
expect_true(is.data.frame(res) | "Seurat" %in% class(res))
})
test_that("run all gene list functions in clustify_lists and seurat object", {
res <- clustify_lists(
s_small,
marker = cbmc_m,
dr = "tsne",
cluster_col = "res.1",
metric = "consensus",
seurat_out = TRUE
)
expect_true(is.data.frame(res) | "seurat" %in% class(res))
})
test_that("lists of genes will work with posneg", {
lst_of_markers <- split(pbmc_markers$gene, pbmc_markers$cluster)
res <- clustify_lists(
input = pbmc_matrix_small,
per_cell = FALSE,
cluster_col = "classified",
metadata = pbmc_meta,
marker = lst_of_markers,
marker_inmatrix = TRUE,
metric = "posneg",
seurat_out = FALSE
)
expect_true(ncol(res) == length(lst_of_markers))
})
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