make_ligand_activity_target_plot: make_ligand_activity_target_plot

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_target_plot

Description

make_ligand_activity_target_plot Summary plot showing the activity of prioritized ligands acting on a receiver cell type of interest, together with the predicted target genes and their sample-by-sample expression

Usage

make_ligand_activity_target_plot(group_oi, receiver_oi, prioritized_tbl_oi, prioritization_tables, ligand_activities_targets_DEgenes, contrast_tbl, grouping_tbl, receiver_info, ligand_target_matrix, groups_oi = NULL, plot_legend = TRUE, heights = NULL, widths = NULL)

Arguments

group_oi	Character vector: name of the group of interest
receiver_oi	Character vector of receiver cell type of interest
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).
prioritization_tables	'prioritization_tables' slot of the output of the 'generate_prioritization_tables' or 'multi_nichenet_analysis' function
ligand_activities_targets_DEgenes	Sublist in the output of 'multi_nichenet_analysis'
contrast_tbl	Table to link the contrast definitions to the group ids.
grouping_tbl	'grouping_tbl' slot of the output of the 'multi_nichenet_analysis' function
receiver_info	'celltype_info' or 'receiver_info' slot of the output of the 'multi_nichenet_analysis' function
ligand_target_matrix	Prior knowledge model of ligand-target regulatory potential (matrix with ligands in columns and targets in rows). See https://github.com/saeyslab/nichenetr.
groups_oi	Which groups to show? Default: NULL – will show all groups.
plot_legend	if TRUE (default): show legend on the same figure, if FALSE (recommended): show legend in separate figure
heights	Vector of 2 elements: height of the ligand-activity-target panel, height of the target expression panel. Default NULL: automatically defined based on nr of ligands and samples. If manual change: example format: c(1,1)
widths	Vector of 3 elements: Width of the scaled ligand activity panel, width of the ligand activity panel, width of the ligand-target heatmap panel. Default NULL: automatically defined based on nr of target genes and group-receiver combinations. If manual change: example format: c(1,1,10)

Value

Summary plot showing the activity of prioritized ligands acting on a receiver cell type of interest, together with the predicted target genes and their sample-by-sample expression

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
     
     )
group_oi = "High"
receiver_oi = "Malignant"
prioritized_tbl_oi = output$prioritization_tables$group_prioritization_tbl %>% filter(fraction_expressing_ligand_receptor > 0) %>% filter(group == group_oi & receiver == receiver_oi) %>% top_n(50, prioritization_score) %>% top_n(25, activity_scaled) %>% arrange(-activity_scaled)
combined_plot = make_ligand_activity_target_plot(group_oi, receiver_oi, prioritized_tbl_oi, output$prioritization_tables, output$ligand_activities_targets_DEgenes, contrast_tbl, output$grouping_tbl, output$celltype_info,ligand_target_matrix, plot_legend = FALSE)


## End(Not run)

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