visualize_network | R Documentation |
visualize_network
Visualize a network showing the gene regulatory links between ligands from sender cell types to their induced ligands/receptors in receiver cell types. Links are only drawn if the ligand/receptor in the receiver is a potential downstream target of the ligand (based on prior knowledge, and optionally with sufficient correlation in expression across the different samples).
visualize_network(network, colors)
network |
Output of 'infer_intercellular_regulatory_network' |
colors |
Named vector of colors associated to each sender cell type. Vector = color, names = sender names. |
ggplot object with plot of LR–>Target links, together with the tidygraph and igraph objects themselves
## Not run:
library(dplyr)
lr_network = readRDS(url("https://zenodo.org/record/3260758/files/lr_network.rds"))
lr_network = lr_network %>% dplyr::rename(ligand = from, receptor = to) %>% dplyr::distinct(ligand, receptor)
ligand_target_matrix = readRDS(url("https://zenodo.org/record/3260758/files/ligand_target_matrix.rds"))
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
batches = NA
contrasts_oi = c("'High-Low','Low-High'")
contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
output = multi_nichenet_analysis(
sce = sce,
celltype_id = celltype_id,
sample_id = sample_id,
group_id = group_id,
batches = batches,
lr_network = lr_network,
ligand_target_matrix = ligand_target_matrix,
contrasts_oi = contrasts_oi,
contrast_tbl = contrast_tbl
)
lr_target_prior_cor_filtered = output$lr_target_prior_cor %>% filter(scaled_prior_score > 0.50 & (pearson > 0.66 | spearman > 0.66))
prioritized_tbl_oi = output$prioritization_tables$group_prioritization_tbl %>% distinct(id, ligand, receptor, sender, receiver, lr_interaction, group, ligand_receptor_lfc_avg, activity_scaled, fraction_expressing_ligand_receptor, prioritization_score) %>% filter(fraction_expressing_ligand_receptor > 0 & ligand_receptor_lfc_avg > 0) %>% filter(group == group_oi & receiver == receiver_oi) %>% top_n(250, prioritization_score)
prioritized_tbl_oi = prioritized_tbl_oi %>% filter(id %in% lr_target_prior_cor_filtered$id)
prioritized_tbl_oi = prioritized_tbl_oi %>% group_by(ligand, sender, group) %>% top_n(2, prioritization_score)
lr_target_df = lr_target_prior_cor_filtered %>% distinct(group, sender, receiver, ligand, receptor, id, target, direction_regulation)
network = infer_intercellular_regulatory_network(lr_target_df, prioritized_tbl_oi)
graph_plot = visualize_network(network, colors = c("purple","orange"))
graph_plot$plot
## End(Not run)
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