make_circos_group_comparison: make_circos_group_comparison

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

make_circos_group_comparison

Description

make_circos_group_comparison Make a circos plot with top prioritized ligand-receptor interactions for each group of interest. In each circos, all the possible LR pairs will be shown, but arrows will only be drawn between the ones belonging to the group of interest.

Usage

make_circos_group_comparison(prioritized_tbl_oi, colors_sender, colors_receiver)

Arguments

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).
colors_sender	Named vector of colors associated to each sender cell type. Vector = color, names = sender names. If sender and receiver cell types are the same, recommended that this vector is the same as 'colors_receiver'.
colors_receiver	Named vector of colors associated to each receiver cell type. Vector = color, names = sender names. Vector = color, names = sender names. If sender and receiver cell types are the same, recommended that this vector is the same as 'colors_receiver'.

Value

a list with a circos plot for each group of interest, and a legend showing the color corresponding to each sender/receiver cell type.

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
     
     )

## End(Not run)

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