tests/testthat/test-application_prediction.R
In saeyslab/nichenetr: NicheNet: Modeling Intercellular Communication by Linking Ligands to Target Genes
context("NicheNet analysis on Seurat objects")
test_that("Seurat wrapper works", {
options(timeout = 3600)
ligand_target_matrix = readRDS(url("https://zenodo.org/record/7074291/files/ligand_target_matrix_nsga2r_final_mouse.rds"))
lr_network = readRDS(url("https://zenodo.org/record/7074291/files/lr_network_mouse_21122021.rds"))
weighted_networks = readRDS(url("https://zenodo.org/record/7074291/files/weighted_networks_nsga2r_final_mouse.rds"))
seurat_object_lite = readRDS(url("https://zenodo.org/record/3531889/files/seuratObj_test.rds"))
seurat_objs = list(Seurat::UpdateSeuratObject(seurat_object_lite))
# Test for v5
if (grepl("^5", packageVersion("Seurat"))){
seurat_objs[[2]] <- CreateSeuratObject(counts = Seurat::GetAssayData(seurat_object_lite, layer = "counts"),
meta.data = seurat_object_lite@meta.data) %>% SetIdent(value = .$celltype) %>%
NormalizeData()
}
# Needed because Seurat v5.0.3 doesn't work if there's only one gene in the Assay
pcts <- c(0.1, 0.05)
for (i in 1:length(seurat_objs)){
seurat_object_lite <- seurat_objs[[i]]
pct <- pcts[i]
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network,geneset = "up", expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network,geneset = "down", expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "all", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, filter_top_ligands = FALSE, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network,geneset = "up", expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network,geneset = "down", expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "all", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, filter_top_ligands = FALSE, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network,geneset = "up", expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network,geneset = "down", expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = "all", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, filter_top_ligands = FALSE, expression_pct = pct)
expect_type(nichenet_output,"list")
seurat_object_lite@meta.data$aggregate = seurat_object_lite@meta.data$aggregate %>% as.factor()
seurat_object_lite@meta.data$celltype = seurat_object_lite@meta.data$celltype %>% as.factor()
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
lfc_output = get_lfc_celltype(seurat_obj = seurat_object_lite, celltype_oi = "CD8 T", condition_colname = "aggregate", condition_oi = "LCMV", condition_reference = "SS", min.pct = pct, logfc.threshold = 0)
expect_type(lfc_output,"list")
}
})
context("Target gene prediction functions for application")
test_that("Gene-cluster assignments can be converted to settings", {
genes_clusters = c("TGFB1" = 1,"TGFB2" = 1,"TGFB3" = 2)
cluster_settings = lapply(seq(length(unique(genes_clusters))), convert_cluster_to_settings, cluster_vector = genes_clusters, setting_name = "example", setting_from = "BMP2")
expect_type(cluster_settings,"list")
expect_type(cluster_settings[[1]]$response,"logical")
expect_type(cluster_settings[[1]]$from,"character")
expect_type(cluster_settings[[1]]$name,"character")
expect_type(cluster_settings[[2]]$response,"logical")
})
context("Ligand activity prediction for application")
test_that("Ligand activities can be predicted well", {
weighted_networks = construct_weighted_networks(lr_network, sig_network, gr_network,source_weights_df)
ligands = list("TNF","IL4")
ligand_target_matrix = construct_ligand_target_matrix(weighted_networks, ligands, ltf_cutoff = 0, algorithm = "PPR", damping_factor = 0.5, secondary_targets = FALSE)
potential_ligands = c("TNF","IL4")
geneset = c("VCAM1","SOCS3", "IRF1","LTBR","STAT4")
background_expressed_genes = c("VCAM1","SOCS3","IRF1","ICAM1","ID1","ID2","ID3","LTBR",'STAT4')
ligand_activities = predict_ligand_activities(geneset = geneset, background_expressed_genes = background_expressed_genes, ligand_target_matrix = ligand_target_matrix, potential_ligands = potential_ligands)
expect_type(ligand_activities,"list")
expect_type(ligand_activities$test_ligand,"character")
expect_type(ligand_activities$pearson,"double")
active_ligand_target_links_df = potential_ligands %>% lapply(get_weighted_ligand_target_links,geneset = geneset, ligand_target_matrix = ligand_target_matrix, n = 500) %>% bind_rows()
expect_type(active_ligand_target_links_df,"list")
active_ligand_target_links = prepare_ligand_target_visualization(ligand_target_df = active_ligand_target_links_df, ligand_target_matrix = ligand_target_matrix, cutoff = 0.001)
expect_type(active_ligand_target_links,"double")
})
context("Assessment target gene prediction by multi-ligand models")
test_that("Target gene prediction can be predicted by multi-ligand models", {
weighted_networks = construct_weighted_networks(lr_network, sig_network, gr_network,source_weights_df)
ligands = list("TNF","IL4")
ligand_target_matrix = construct_ligand_target_matrix(weighted_networks, ligands, ltf_cutoff = 0, algorithm = "PPR", damping_factor = 0.5, secondary_targets = FALSE)
potential_ligands = c("TNF","IL4")
geneset = c("VCAM1","SOCS3", "IRF1","LTBR","STAT4")
background_expressed_genes = c("VCAM1","SOCS3","IRF1","ICAM1","ID1","ID2","ID3","LTBR",'STAT4')
gene_predictions_list = seq(10) %>% lapply(assess_rf_class_probabilities,2, geneset = geneset,background_expressed_genes = background_expressed_genes,ligands_oi = potential_ligands,ligand_target_matrix = ligand_target_matrix)
expect_type(gene_predictions_list,"list")
expect_type(gene_predictions_list[[1]]$gene,"character")
expect_type(gene_predictions_list[[1]]$prediction,"double")
target_prediction_performances_cv = gene_predictions_list %>% lapply(classification_evaluation_continuous_pred_wrapper) %>% bind_rows() %>% mutate(round=seq(1:nrow(.)))
expect_type(target_prediction_performances_cv,"list")
expect_type(target_prediction_performances_cv$auroc,"double")
top_genes_df = seq(length(gene_predictions_list)) %>% lapply(get_top_predicted_genes,gene_predictions_list) %>% reduce(full_join, by = c("gene","true_target"))
expect_type(top_genes_df,"list")
target_prediction_performances_discrete_cv = gene_predictions_list %>% lapply(calculate_fraction_top_predicted,quantile_cutoff = 0.66) %>% bind_rows()
expect_type(target_prediction_performances_discrete_cv,"list")
target_prediction_performances_fisher_pval = gene_predictions_list %>% lapply(calculate_fraction_top_predicted_fisher,quantile_cutoff = 0.95) %>% unlist() %>% mean()
expect_type(target_prediction_performances_fisher_pval,"double")
})
context("Single-cell ligand activity prediction functions")
test_that("Single-cell ligand activity prediction functions work a bit OK", {
x = matrix(rnorm(200*2, sd = 10, mean = 5), ncol = 2)
x_scaled = scale_quantile(x)
expect_type(x_scaled,"double")
weighted_networks = construct_weighted_networks(lr_network, sig_network, gr_network,source_weights_df)
ligands = list("TNF","BMP2","IL4")
ligand_target_matrix = construct_ligand_target_matrix(weighted_networks, ligands, ltf_cutoff = 0, algorithm = "PPR", damping_factor = 0.5, secondary_targets = FALSE)
potential_ligands = c("TNF","BMP2","IL4")
genes = c("SOCS2","SOCS3","IRF1","ICAM1","ID1","ID2","ID3")
cell_ids = c("cell1","cell2","cell3","cell4")
set.seed(1)
expression_scaled = matrix(rnorm(length(genes)*length(cell_ids), sd = 0.5, mean = 0.5), nrow = length(cell_ids))
rownames(expression_scaled) = cell_ids
colnames(expression_scaled) = genes
settings = convert_single_cell_expression_to_settings(cell_id = cell_ids[1], expression_matrix = expression_scaled, setting_name = "test", setting_from = potential_ligands)
expect_type(settings,"list")
ligand_activities = predict_single_cell_ligand_activities(cell_ids = cell_ids, expression_scaled = expression_scaled, ligand_target_matrix = ligand_target_matrix, potential_ligands = potential_ligands)
expect_type(ligand_activities,"list")
expect_type(ligand_activities$test_ligand,"character")
expect_type(ligand_activities$setting,"character")
expect_type(ligand_activities$pearson,"double")
normalized_ligand_activities = normalize_single_cell_ligand_activities(ligand_activities)
expect_type(normalized_ligand_activities,"list")
cell_scores_tbl = tibble(cell = cell_ids, score = c(1,4,2,3))
# Since changing the metric, these return zeros so standard deviation is zero
normalized_ligand_activities <- cbind(normalized_ligand_activities %>% select(cell), normalized_ligand_activities %>% select(-cell) + matrix(rnorm(12, sd=0.01),4))
regression_analysis_output = single_ligand_activity_score_regression(normalized_ligand_activities,cell_scores_tbl)
expect_type(regression_analysis_output,"list")
cell_scores_tbl = tibble(cell = cell_ids, score = c(TRUE, FALSE, TRUE, FALSE))
classification_analysis_output = single_ligand_activity_score_classification(normalized_ligand_activities,cell_scores_tbl)
expect_type(classification_analysis_output,"list")
})
saeyslab/nichenetr documentation built on April 27, 2024, 9:24 p.m.
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context("NicheNet analysis on Seurat objects")
test_that("Seurat wrapper works", {
options(timeout = 3600)
ligand_target_matrix = readRDS(url("https://zenodo.org/record/7074291/files/ligand_target_matrix_nsga2r_final_mouse.rds"))
lr_network = readRDS(url("https://zenodo.org/record/7074291/files/lr_network_mouse_21122021.rds"))
weighted_networks = readRDS(url("https://zenodo.org/record/7074291/files/weighted_networks_nsga2r_final_mouse.rds"))
seurat_object_lite = readRDS(url("https://zenodo.org/record/3531889/files/seuratObj_test.rds"))
seurat_objs = list(Seurat::UpdateSeuratObject(seurat_object_lite))
# Test for v5
if (grepl("^5", packageVersion("Seurat"))){
seurat_objs[[2]] <- CreateSeuratObject(counts = Seurat::GetAssayData(seurat_object_lite, layer = "counts"),
meta.data = seurat_object_lite@meta.data) %>% SetIdent(value = .$celltype) %>%
NormalizeData()
}
# Needed because Seurat v5.0.3 doesn't work if there's only one gene in the Assay
pcts <- c(0.1, 0.05)
for (i in 1:length(seurat_objs)){
seurat_object_lite <- seurat_objs[[i]]
pct <- pcts[i]
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network,geneset = "up", expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network,geneset = "down", expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "all", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, filter_top_ligands = FALSE, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network,geneset = "up", expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network,geneset = "down", expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "all", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, filter_top_ligands = FALSE, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network,geneset = "up", expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network,geneset = "down", expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = "all", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, filter_top_ligands = FALSE, expression_pct = pct)
expect_type(nichenet_output,"list")
seurat_object_lite@meta.data$aggregate = seurat_object_lite@meta.data$aggregate %>% as.factor()
seurat_object_lite@meta.data$celltype = seurat_object_lite@meta.data$celltype %>% as.factor()
nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_aggregate_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
nichenet_output = nichenet_seuratobj_cluster_de(seurat_obj = seurat_object_lite, receiver_affected = "CD8 T", receiver_reference = "Mono", sender = "undefined", ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network, expression_pct = pct)
expect_type(nichenet_output,"list")
lfc_output = get_lfc_celltype(seurat_obj = seurat_object_lite, celltype_oi = "CD8 T", condition_colname = "aggregate", condition_oi = "LCMV", condition_reference = "SS", min.pct = pct, logfc.threshold = 0)
expect_type(lfc_output,"list")
}
})
context("Target gene prediction functions for application")
test_that("Gene-cluster assignments can be converted to settings", {
genes_clusters = c("TGFB1" = 1,"TGFB2" = 1,"TGFB3" = 2)
cluster_settings = lapply(seq(length(unique(genes_clusters))), convert_cluster_to_settings, cluster_vector = genes_clusters, setting_name = "example", setting_from = "BMP2")
expect_type(cluster_settings,"list")
expect_type(cluster_settings[[1]]$response,"logical")
expect_type(cluster_settings[[1]]$from,"character")
expect_type(cluster_settings[[1]]$name,"character")
expect_type(cluster_settings[[2]]$response,"logical")
})
context("Ligand activity prediction for application")
test_that("Ligand activities can be predicted well", {
weighted_networks = construct_weighted_networks(lr_network, sig_network, gr_network,source_weights_df)
ligands = list("TNF","IL4")
ligand_target_matrix = construct_ligand_target_matrix(weighted_networks, ligands, ltf_cutoff = 0, algorithm = "PPR", damping_factor = 0.5, secondary_targets = FALSE)
potential_ligands = c("TNF","IL4")
geneset = c("VCAM1","SOCS3", "IRF1","LTBR","STAT4")
background_expressed_genes = c("VCAM1","SOCS3","IRF1","ICAM1","ID1","ID2","ID3","LTBR",'STAT4')
ligand_activities = predict_ligand_activities(geneset = geneset, background_expressed_genes = background_expressed_genes, ligand_target_matrix = ligand_target_matrix, potential_ligands = potential_ligands)
expect_type(ligand_activities,"list")
expect_type(ligand_activities$test_ligand,"character")
expect_type(ligand_activities$pearson,"double")
active_ligand_target_links_df = potential_ligands %>% lapply(get_weighted_ligand_target_links,geneset = geneset, ligand_target_matrix = ligand_target_matrix, n = 500) %>% bind_rows()
expect_type(active_ligand_target_links_df,"list")
active_ligand_target_links = prepare_ligand_target_visualization(ligand_target_df = active_ligand_target_links_df, ligand_target_matrix = ligand_target_matrix, cutoff = 0.001)
expect_type(active_ligand_target_links,"double")
})
context("Assessment target gene prediction by multi-ligand models")
test_that("Target gene prediction can be predicted by multi-ligand models", {
weighted_networks = construct_weighted_networks(lr_network, sig_network, gr_network,source_weights_df)
ligands = list("TNF","IL4")
ligand_target_matrix = construct_ligand_target_matrix(weighted_networks, ligands, ltf_cutoff = 0, algorithm = "PPR", damping_factor = 0.5, secondary_targets = FALSE)
potential_ligands = c("TNF","IL4")
geneset = c("VCAM1","SOCS3", "IRF1","LTBR","STAT4")
background_expressed_genes = c("VCAM1","SOCS3","IRF1","ICAM1","ID1","ID2","ID3","LTBR",'STAT4')
gene_predictions_list = seq(10) %>% lapply(assess_rf_class_probabilities,2, geneset = geneset,background_expressed_genes = background_expressed_genes,ligands_oi = potential_ligands,ligand_target_matrix = ligand_target_matrix)
expect_type(gene_predictions_list,"list")
expect_type(gene_predictions_list[[1]]$gene,"character")
expect_type(gene_predictions_list[[1]]$prediction,"double")
target_prediction_performances_cv = gene_predictions_list %>% lapply(classification_evaluation_continuous_pred_wrapper) %>% bind_rows() %>% mutate(round=seq(1:nrow(.)))
expect_type(target_prediction_performances_cv,"list")
expect_type(target_prediction_performances_cv$auroc,"double")
top_genes_df = seq(length(gene_predictions_list)) %>% lapply(get_top_predicted_genes,gene_predictions_list) %>% reduce(full_join, by = c("gene","true_target"))
expect_type(top_genes_df,"list")
target_prediction_performances_discrete_cv = gene_predictions_list %>% lapply(calculate_fraction_top_predicted,quantile_cutoff = 0.66) %>% bind_rows()
expect_type(target_prediction_performances_discrete_cv,"list")
target_prediction_performances_fisher_pval = gene_predictions_list %>% lapply(calculate_fraction_top_predicted_fisher,quantile_cutoff = 0.95) %>% unlist() %>% mean()
expect_type(target_prediction_performances_fisher_pval,"double")
})
context("Single-cell ligand activity prediction functions")
test_that("Single-cell ligand activity prediction functions work a bit OK", {
x = matrix(rnorm(200*2, sd = 10, mean = 5), ncol = 2)
x_scaled = scale_quantile(x)
expect_type(x_scaled,"double")
weighted_networks = construct_weighted_networks(lr_network, sig_network, gr_network,source_weights_df)
ligands = list("TNF","BMP2","IL4")
ligand_target_matrix = construct_ligand_target_matrix(weighted_networks, ligands, ltf_cutoff = 0, algorithm = "PPR", damping_factor = 0.5, secondary_targets = FALSE)
potential_ligands = c("TNF","BMP2","IL4")
genes = c("SOCS2","SOCS3","IRF1","ICAM1","ID1","ID2","ID3")
cell_ids = c("cell1","cell2","cell3","cell4")
set.seed(1)
expression_scaled = matrix(rnorm(length(genes)*length(cell_ids), sd = 0.5, mean = 0.5), nrow = length(cell_ids))
rownames(expression_scaled) = cell_ids
colnames(expression_scaled) = genes
settings = convert_single_cell_expression_to_settings(cell_id = cell_ids[1], expression_matrix = expression_scaled, setting_name = "test", setting_from = potential_ligands)
expect_type(settings,"list")
ligand_activities = predict_single_cell_ligand_activities(cell_ids = cell_ids, expression_scaled = expression_scaled, ligand_target_matrix = ligand_target_matrix, potential_ligands = potential_ligands)
expect_type(ligand_activities,"list")
expect_type(ligand_activities$test_ligand,"character")
expect_type(ligand_activities$setting,"character")
expect_type(ligand_activities$pearson,"double")
normalized_ligand_activities = normalize_single_cell_ligand_activities(ligand_activities)
expect_type(normalized_ligand_activities,"list")
cell_scores_tbl = tibble(cell = cell_ids, score = c(1,4,2,3))
# Since changing the metric, these return zeros so standard deviation is zero
normalized_ligand_activities <- cbind(normalized_ligand_activities %>% select(cell), normalized_ligand_activities %>% select(-cell) + matrix(rnorm(12, sd=0.01),4))
regression_analysis_output = single_ligand_activity_score_regression(normalized_ligand_activities,cell_scores_tbl)
expect_type(regression_analysis_output,"list")
cell_scores_tbl = tibble(cell = cell_ids, score = c(TRUE, FALSE, TRUE, FALSE))
classification_analysis_output = single_ligand_activity_score_classification(normalized_ligand_activities,cell_scores_tbl)
expect_type(classification_analysis_output,"list")
})
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