infer_intercellular_regulatory_network: infer_intercellular_regulatory_network

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

infer_intercellular_regulatory_network

Description

infer_intercellular_regulatory_network Infer a network showing the gene regulatory links between ligands from sender cell types to their induced ligands/receptors in receiver cell types. Links are only drawn if the ligand/receptor in the receiver is a potential downstream target of the ligand (based on prior knowledge, and optionally with sufficient correlation in expression across the different samples).

Usage

infer_intercellular_regulatory_network(lr_target_df, prioritized_tbl_oi)

Arguments

lr_target_df	tibble with columns: group, sender, receiver, ligand, receptor, id, target, direction_regulation
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).

Value

list containing 3 elements: links, nodes, prioritized_lr_interactions. Links is a tibble that can be used to create a network with igraph, together with the node tibble. prioritized_lr_interactions is the subset of the input prioritized_tbl_oi, focusing on interaction elements present in this network, and hereby further prioritizing.

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
     
     )
lr_target_prior_cor_filtered = output$lr_target_prior_cor %>% filter(scaled_prior_score > 0.50 & (pearson > 0.66 | spearman > 0.66))
 prioritized_tbl_oi = output$prioritization_tables$group_prioritization_tbl %>% distinct(id, ligand, receptor, sender, receiver, lr_interaction, group, ligand_receptor_lfc_avg, activity_scaled, fraction_expressing_ligand_receptor,  prioritization_score) %>% filter(fraction_expressing_ligand_receptor > 0 & ligand_receptor_lfc_avg > 0) %>% filter(group == group_oi & receiver == receiver_oi) %>% top_n(250, prioritization_score)
 prioritized_tbl_oi = prioritized_tbl_oi %>% filter(id %in% lr_target_prior_cor_filtered$id)
 prioritized_tbl_oi = prioritized_tbl_oi %>% group_by(ligand, sender, group) %>% top_n(2, prioritization_score)
 lr_target_df = lr_target_prior_cor_filtered  %>% distinct(group, sender, receiver, ligand, receptor, id, target, direction_regulation) 
 network = infer_intercellular_regulatory_network(lr_target_df, prioritized_tbl_oi)

## End(Not run)

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