make_ligand_activity_plots: make_ligand_activity_plots

In saeyslab/multinichenetr: MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data

make_ligand_activity_plots

Description

make_ligand_activity_plots Visualize the ligand activities (normal and scaled) of each group-receiver combination

Usage

make_ligand_activity_plots(prioritization_tables, ligands_oi, contrast_tbl, widths = NULL)

Arguments

prioritization_tables	Output of 'generate_prioritization_tables' or sublist in the output of 'multi_nichenet_analysis'
ligands_oi	Character vector of ligands for which the activities should be visualized
contrast_tbl	Table to link the contrast definitions to the group ids.
widths	Vector of 2 elements: Width of the scaled ligand activity panel, width of the ligand activity panel. Default NULL: automatically defined based number of group-receiver combinations. If manual change: example format: c(3,2)

Value

Heatmap of ligand activities (normal and scaled) of each group-receiver combination

Examples

## 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
     
     )
ligands_oi = output$prioritization_tables$ligand_activities_target_de_tbl %>% inner_join(contrast_tbl) %>% group_by(group, receiver) %>% distinct(ligand, receiver, group, activity) %>% top_n(5, activity) %>% pull(ligand) %>% unique()
plot_oi = make_ligand_activity_plots(output$prioritization_tables, ligands_oi, contrast_tbl)
plot_oi

## End(Not run)

saeyslab/multinichenetr documentation built on Jan. 15, 2025, 7:55 p.m.