make_lr_target_scatter_plot: make_lr_target_scatter_plot

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

make_lr_target_scatter_plot

Description

make_lr_target_scatter_plot Plot Ligand-Receptor pseudobulk expression product values vs pseudobulk expression of correlated target genes supported by prior information.

Usage

make_lr_target_scatter_plot(prioritization_tables, ligand_oi, receptor_oi, sender_oi, receiver_oi, receiver_info, grouping_tbl, lr_target_prior_cor_filtered)

Arguments

prioritization_tables	Output of 'generate_prioritization_tables' or sublist in the output of 'multi_nichenet_analysis'
ligand_oi	Character vector of name of the ligand of interest
receptor_oi	Character vector of name of the receptor of interest
sender_oi	Character vector with the names of the sender cell type of interest
receiver_oi	Character vector with the names of the receiver cell type of interest
receiver_info	'celltype_info' or 'receiver_info' slot of the output of the 'multi_nichenet_analysis' function
grouping_tbl	'grouping_tbl' slot of the output of the 'multi_nichenet_analysis' function
lr_target_prior_cor_filtered	Data frame filtered from 'lr_target_prior_cor' (= output of 'multi_nichenet_analysis' or 'lr_target_prior_cor_inference'). Filter should be done to keep onl LR–>Target links that are both supported by prior knowledge and correlation in terms of expression.

Value

ggplot object with plot of LR expression vs target 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
     )
ligand_oi ="IL24"
receptor_oi = "IL22RA1" 
sender_oi = "CAF"
receiver_oi ="Malignant"
lr_target_prior_cor_filtered = output$lr_target_prior_cor %>% filter(scaled_prior_score > 0.50 & (pearson > 0.66 | spearman > 0.66))
lr_target_scatter_plot = make_lr_target_scatter_plot(output$prioritization_tables, ligand_oi, receptor_oi, sender_oi, receiver_oi, output$celltype_info, output$grouping_tbl, lr_target_prior_cor_filtered) 

## End(Not run)

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