make_sample_lr_prod_activity_batch_plots — make_sample_lr_prod_activity_batch

make_sample_lr_prod_activity_batch_plots Visualize the scaled product of Ligand-Receptor (pseudobulk) expression per sample, and compare the different groups. In addition, show the NicheNet ligand activities in each receiver-celltype combination. On top of this summary plot, a heatmap indicates the batch value for each displayed sample.

Usage

make_sample_lr_prod_activity_batch_plots(prioritization_tables, prioritized_tbl_oi, grouping_tbl, batch_oi, widths = NULL, heights = NULL)

Arguments

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).
grouping_tbl: Data frame linking the sample_id, group_id and batch_oi
batch_oi: Name of the batch that needs to be visualized for each sample
widths: Vector of 4 elements: Width of the LR exprs product panel, width of the scaled ligand activity panel, width of the ligand activity panel & width of the cell-type specificity panel. Default NULL: automatically defined based on nr of samples vs nr of group-receiver combinations. If manual change: example format: c(6,2,2,1)
heights: Vector of 2 elements: Height of the batch panel and height of the ligand-receptor prod+activity panel. Default NULL: automatically defined based on the nr of Ligand-Receptor pairs. If manual change: example format: c(1,5)

Value

Ligand-Receptor Expression Product & Ligand Activities Dotplot/Heatmap, complemented with a heatmap indicating the batch of interest

Examples

if (FALSE) { # \dontrun{
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 = "batch"
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"
batch_oi = "batch"
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_activity_batch_plots(output$prioritization_tables, prioritized_tbl_oi, output$grouping_tbl, batch_oi = batch_oi)
plot_oi
} # }