tests/testthat/test_cpdbplot.R

In zktuong/ktplots: Plot single-cell data dotplots

data(kidneyimmune)
data(cpdb_output)
data(cpdb_output2)
data(cpdb_output_v5)

test_that("combine_cpdb works 1", {
    p <- combine_cpdb(decon, decon, decon)
    expect_that(class(p), equals("data.frame"))
})


test_that("plot_cpdb works 1", {
    p <- plot_cpdb(
        cell_type1 = "B cell", cell_type2 = "CD4T cell", scdata = kidneyimmune,
        celltype_key = "celltype", means = means, pvals = pvals, splitby_key = "Experiment",
        genes = c("CXCL13", "CD274", "CXCR5"), keep_significant_only = FALSE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb works 2", {
    p <- plot_cpdb(
        cell_type1 = "B cell", cell_type2 = "CD4T cell", scdata = kidneyimmune,
        celltype_key = "celltype", means = means, pvals = pvals, splitby_key = "Experiment",
        gene_family = "chemokines", keep_significant_only = FALSE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb works 3", {
    p <- plot_cpdb(
        cell_type1 = "B cell", cell_type2 = "CD4T cell", scdata = kidneyimmune,
        celltype_key = "celltype", means = means, pvals = pvals, splitby_key = "Experiment",
        gene_family = "chemokines", default_style = FALSE, keep_significant_only = FALSE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb works 4", {
    p <- plot_cpdb(
        cell_type1 = "B cell", cell_type2 = "CD4T cell", scdata = kidneyimmune,
        celltype_key = "celltype", means = means, pvals = pvals, splitby_key = "Experiment",
        gene_family = "chemokines", keep_significant_only = FALSE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb works 5", {
    p <- plot_cpdb(
        cell_type1 = "B cell", cell_type2 = "CD4T cell", scdata = kidneyimmune,
        celltype_key = "celltype", means = means, pvals = pvals, splitby_key = "Experiment",
        gene_family = "chemokines", default_style = FALSE, keep_significant_only = FALSE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb works 6", {
    p <- plot_cpdb(
        cell_type1 = "B cell", cell_type2 = "CD4T cell", scdata = kidneyimmune,
        celltype_key = "celltype", means = means2, pvals = pvals2, gene_family = "custom_family",
        custom_gene_family = list(custom_family = c("CXCL13", "CD274", "CXCR5")),
        keep_significant_only = FALSE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb works 7", {
    p <- plot_cpdb(
        cell_type1 = "B cell", cell_type2 = "CD4T cell", scdata = kidneyimmune,
        celltype_key = "celltype", means = means2, pvals = pvals2, gene_family = "custom_family",
        custom_gene_family = data.frame(custom_family = c("CXCL13", "CD274", "CXCR5")),
        keep_significant_only = FALSE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb works 8", {
    p <- plot_cpdb(
        cell_type1 = "B cell", cell_type2 = "CD4T cell", scdata = kidneyimmune,
        celltype_key = "celltype", means = means2, pvals = pvals2, gene_family = c(
            "chemokines",
            "th1"
        ), keep_significant_only = FALSE
    )
    expect_true(is.ggplot(p))
})

test_that("weird characters are ok", {
    # edit the example objects to simulate Rachel's objects rename B cells to
    # TRC+
    kidneyimmune$celltype <- gsub("B cell", "TRC+", kidneyimmune$celltype)
    colnames(means) <- gsub("B cell", "TRC+", colnames(means))
    colnames(pvals) <- gsub("B cell", "TRC+", colnames(pvals))

    # rename NK cells to LTi-Like ILC3
    kidneyimmune$celltype <- gsub("NK cell", "LTi-Like ILC3", kidneyimmune$celltype)
    colnames(means) <- gsub("NK cell", "LTi-Like ILC3", colnames(means))
    colnames(pvals) <- gsub("NK cell", "LTi-Like ILC3", colnames(pvals))

    # remove the original split.by tags
    colnames(means) <- gsub(
        "Wilms2_|TxK1_|RCC1_|RCC2_|RCC3_|Wilms3_|TxK4_|VHLRCC_|Wilms1_|Teen_|Tx_|TxK3_|TxK2_|PapRCC",
        "", colnames(means)
    )
    colnames(pvals) <- gsub(
        "Wilms2_|TxK1_|RCC1_|RCC2_|RCC3_|Wilms3_|TxK4_|VHLRCC_|Wilms1_|Teen_|Tx_|TxK3_|TxK2_|PapRCC",
        "", colnames(pvals)
    )

    # transpose and average to get rid of duplicate columns
    means_df <- as.data.frame(t(means[, c(12:ncol(means))]))
    pvals_df <- as.data.frame(t(pvals[, c(12:ncol(pvals))]))
    means_df$dup <- gsub("[.].*", "", row.names(means_df))
    pvals_df$dup <- gsub("[.].*", "", row.names(pvals_df))
    means_df <- split(means_df, means_df$dup)
    pvals_df <- split(pvals_df, pvals_df$dup)
    means_df <- t(do.call(rbind, lapply(means_df, function(x) {
        colMeans(x[1:ncol(x) - 1])
    })))
    pvals_df <- t(do.call(rbind, lapply(pvals_df, function(x) {
        colMeans(x[1:ncol(x) - 1])
    })))

    # concatenate with original first 11 columns
    newmeans <- cbind(means[, 1:11], means_df)
    newpvals <- cbind(pvals[, 1:11], pvals_df)

    # plot_cpdb
    p <- plot_cpdb(
        cell_type1 = "TRC+", cell_type2 = "LTi-Like ILC3", scdata = kidneyimmune,
        celltype_key = "celltype", means = newmeans, pvals = newpvals, genes = c(
            "LTB",
            "LTBR", "KITL", "KIT", "CCR6"
        ), keep_significant_only = FALSE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb2 works 1", {
    p <- plot_cpdb2(
        cell_type1 = "B cell", cell_type2 = "CD4T cell", scdata = kidneyimmune,
        celltype_key = "celltype", means = means2, pvals = pvals2, deconvoluted = decon2,
        desiredInteractions = list(c("CD4T cell", "B cell"), c("B cell", "CD4T cell")),
        interaction_grouping = interaction_annotation, edge_group_colors = c(
            Activating = "#e15759",
            Chemotaxis = "#59a14f", Inhibitory = "#4e79a7", `Intracellular trafficking` = "#9c755f",
            DC_development = "#B07aa1", Unknown = NA
        ), node_group_colors = c(
            `CD4T cell` = "#86bc86",
            `B cell` = "#79706e"
        ), keep_significant_only = TRUE, standard_scale = TRUE,
        remove_self = TRUE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb2 works 2", {
    p <- plot_cpdb2(
        cell_type1 = "B cell", cell_type2 = "CD4T cell", scdata = kidneyimmune,
        celltype_key = "celltype", means = means2, pvals = pvals2, deconvoluted = decon2
    )
    expect_true(is.ggplot(p))
})


test_that("plot_cpdb3 works 1", {
    p <- plot_cpdb3(
        cell_type1 = "B cell", cell_type2 = "CD4T cell", scdata = kidneyimmune,
        celltype_key = "celltype", means = means2, pvals = pvals2, deconvoluted = decon2,
        keep_significant_only = TRUE, standard_scale = TRUE, remove_self = TRUE
    )
    expect_that(class(p), equals("recordedplot"))
})


test_that("plot_cpdb3 2", {
    p <- plot_cpdb3(
        cell_type1 = "B cell", cell_type2 = "CD4T cell", scdata = kidneyimmune,
        celltype_key = "celltype", splitby_key = "Experiment", means = means, pvals = pvals,
        deconvoluted = decon, keep_significant_only = TRUE, standard_scale = TRUE,
        remove_self = TRUE
    )
    expect_that(class(p[[1]]), equals("recordedplot"))
    expect_that(class(p[[2]]), equals("recordedplot"))
    expect_that(class(p[[3]]), equals("recordedplot"))
    expect_that(class(p[[4]]), equals("recordedplot"))
    expect_that(class(p[[5]]), equals("recordedplot"))
    expect_that(class(p[[6]]), equals("recordedplot"))
    expect_true(is.na(p[[7]]))
    expect_that(class(p[[8]]), equals("recordedplot"))
    expect_true(is.na(p[[9]]))
    expect_true(is.na(p[[10]]))
    expect_true(is.na(p[[11]]))
})

test_that("plot_cpdb4 works 1", {
    p <- plot_cpdb4(
        interaction = "CLEC2D-KLRB1", cell_type1 = "NK", cell_type2 = "Mast",
        scdata = kidneyimmune, celltype_key = "celltype", means = means2, pvals = pvals2,
        deconvoluted = decon2, keep_significant_only = TRUE, standard_scale = TRUE,
        remove_self = TRUE
    )
    expect_that(class(p), equals("recordedplot"))
})


test_that("plot_cpdb4 works 2", {
    p <- plot_cpdb4(
        interaction = c("CLEC2D-KLRB1", "CD40-CD40LG"), cell_type1 = "NK|B",
        cell_type2 = "Mast|CD4T", scdata = kidneyimmune, celltype_key = "celltype",
        means = means2, pvals = pvals2, deconvoluted = decon2, desiredInteractions = list(c(
            "NK cell",
            "Mast cell"
        ), c("NK cell", "NKT cell"), c("NKT cell", "Mast cell"), c(
            "B cell",
            "CD4T cell"
        )), keep_significant_only = TRUE
    )
    expect_that(class(p), equals("recordedplot"))
})


test_that("plot_cpdb4 works 3", {
    p <- plot_cpdb4(
        interaction = "CLEC2D-KLRB1", cell_type1 = "NK", cell_type2 = "Mast",
        scdata = kidneyimmune, celltype_key = "celltype", splitby_key = "Experiment",
        means = means, pvals = pvals, deconvoluted = decon, keep_significant_only = TRUE,
        standard_scale = TRUE, remove_self = TRUE
    )
    for (i in 1:13) {
        expect_that(class(p[[i]]), equals("recordedplot"))
    }
})

test_that("plot_cpdb v5 1", {
    p <- plot_cpdb(
        scdata = sce_v5, cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
        cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC", means = means_v5, pvals = relevant_interactions_v5,
        celltype_key = "cell_labels", genes = c("TGFB2", "CSF1R"), max_size = 6,
        highlight_size = 0.75, degs_analysis = TRUE, standard_scale = TRUE, interaction_scores = interaction_scores_v5,
        scale_alpha_by_interaction_scores = TRUE, min_interaction_score = 20
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb v5 2", {
    p <- plot_cpdb(
        scdata = sce_v5, cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
        cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC", means = means_v5, pvals = relevant_interactions_v5,
        celltype_key = "cell_labels", genes = c("TGFB2", "CSF1R"), max_size = 6,
        highlight_size = 0.75, degs_analysis = TRUE, standard_scale = TRUE, cellsign = cellsign_v5,
        scale_alpha_by_interaction_scores = TRUE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb v5 3", {
    p <- plot_cpdb(
        scdata = sce_v5, cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
        cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC", means = means_v5, pvals = relevant_interactions_v5,
        celltype_key = "cell_labels", max_size = 6, highlight_size = 0.75, degs_analysis = TRUE,
        standard_scale = TRUE, cellsign = cellsign_v5, scale_alpha_by_cellsign = TRUE,
        filter_by_cellsign = TRUE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb v5 4", {
    p <- plot_cpdb(
        scdata = sce_v5, cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
        cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC", means = means_v5, pvals = relevant_interactions_v5,
        celltype_key = "cell_labels", max_size = 6, highlight_size = 0.75, degs_analysis = TRUE,
        standard_scale = TRUE, cellsign = cellsign_v5, scale_alpha_by_cellsign = TRUE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb v5 5", {
    p <- plot_cpdb(
        scdata = sce_v5, cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
        cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC", means = means_v5, pvals = relevant_interactions_v5,
        celltype_key = "cell_labels", genes = c("TGFB2", "CSF1R"), max_size = 6,
        highlight_size = 0.75, degs_analysis = TRUE, standard_scale = TRUE, interaction_scores = interaction_scores_v5,
        scale_alpha_by_interaction_scores = TRUE, min_interaction_score = 20, default_style = FALSE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb v5 6", {
    p <- plot_cpdb(
        scdata = sce_v5, cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
        cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC", means = means_v5, pvals = relevant_interactions_v5,
        celltype_key = "cell_labels", genes = c("TGFB2", "CSF1R"), max_size = 6,
        highlight_size = 0.75, degs_analysis = TRUE, standard_scale = TRUE, cellsign = cellsign_v5,
        scale_alpha_by_interaction_scores = TRUE, default_style = FALSE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb v5 7", {
    p <- plot_cpdb(
        scdata = sce_v5, cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
        cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC", means = means_v5, pvals = relevant_interactions_v5,
        celltype_key = "cell_labels", max_size = 6, highlight_size = 0.75, degs_analysis = TRUE,
        standard_scale = TRUE, cellsign = cellsign_v5, scale_alpha_by_cellsign = TRUE,
        filter_by_cellsign = TRUE, default_style = FALSE
    )
    expect_true(is.ggplot(p))
})

test_that("plot_cpdb v5 8", {
    p <- plot_cpdb(
        scdata = sce_v5, cell_type1 = "PV MYH11|PV STEAP4|PV MMPP11",
        cell_type2 = "EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC", means = means_v5, pvals = relevant_interactions_v5,
        celltype_key = "cell_labels", max_size = 6, highlight_size = 0.75, degs_analysis = TRUE,
        standard_scale = TRUE, cellsign = cellsign_v5, scale_alpha_by_cellsign = TRUE,
        default_style = FALSE
    )
    expect_true(is.ggplot(p))
})