make_sample_lr_prod_plots | R Documentation |
make_sample_lr_prod_plots
Visualize the scaled product of Ligand-Receptor (pseudobulk) expression per sample, and compare the different groups
make_sample_lr_prod_plots(prioritization_tables, prioritized_tbl_oi)
prioritization_tables |
Output of 'generate_prioritization_tables' or sublist in the output of 'multi_nichenet_analysis' |
prioritized_tbl_oi |
Subset of 'prioritization_tables$group_prioritization_tbl': the ligand-receptor interactions shown in this subset will be visualized: recommended to consider the top n LR interactions of a group of interest, based on the prioritization_score (eg n = 50; see vignettes for examples). |
Ligand-Receptor Expression Product Dotplot/Heatmap
## Not run:
library(dplyr)
lr_network = readRDS(url("https://zenodo.org/record/3260758/files/lr_network.rds"))
lr_network = lr_network %>% dplyr::rename(ligand = from, receptor = to) %>% dplyr::distinct(ligand, receptor)
ligand_target_matrix = readRDS(url("https://zenodo.org/record/3260758/files/ligand_target_matrix.rds"))
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
batches = NA
contrasts_oi = c("'High-Low','Low-High'")
contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
output = multi_nichenet_analysis(
sce = sce,
celltype_id = celltype_id,
sample_id = sample_id,
group_id = group_id,
batches = batches,
lr_network = lr_network,
ligand_target_matrix = ligand_target_matrix,
contrasts_oi = contrasts_oi,
contrast_tbl = contrast_tbl
)
group_oi = "High"
prioritized_tbl_oi = output$prioritization_tables$group_prioritization_tbl %>% filter(fraction_expressing_ligand_receptor > 0) %>% filter(group == group_oi) %>% top_n(50, prioritization_score)
plot_oi = make_sample_lr_prod_plots(output$prioritization_tables, prioritized_tbl_oi)
plot_oi
## End(Not run)
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