lr_target_prior_cor_inference: lr_target_prior_cor_inference

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

lr_target_prior_cor_inference

Description

lr_target_prior_cor_inference Calculate the pearson and spearman expression correlation between ligand-receptor pair pseudobulk expression products and DE gene expression product. Add the NicheNet ligand-target regulatory potential score as well as prior knowledge support for the LR–>Target link.

Usage

lr_target_prior_cor_inference(receivers_oi, abundance_expression_info, celltype_de, grouping_tbl, prioritization_tables, ligand_target_matrix, logFC_threshold = 0.25, p_val_threshold = 0.05, p_val_adj = FALSE, top_n_LR = 2500)

Arguments

receivers_oi	Character vector with the names of the receiver cell types of interest
abundance_expression_info	Output of 'get_abundance_expression_info_separate' or 'get_abundance_expression_info'
celltype_de	Output of 'perform_muscat_de_analysis'
grouping_tbl	Data frame showing the groups of each sample (and batches per sample if applicable) (columns: sample and group; and if applicable all batches of interest)
prioritization_tables	Output of 'generate_prioritization_tables' or sublist in the output of 'multi_nichenet_analysis'
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.
logFC_threshold	For defining the gene set of interest for NicheNet ligand activity: what is the minimum logFC a gene should have to belong to this gene set? Default: 0.25/
p_val_threshold	For defining the gene set of interest for NicheNet ligand activity: what is the maximam p-value a gene should have to belong to this gene set? Default: 0.05.
p_val_adj	For defining the gene set of interest for NicheNet ligand activity: should we look at the p-value corrected for multiple testing? Default: FALSE.
top_n_LR	top nr of LR pairs for which correlation with target genes will be calculated. Is 2500 by default. If you want to calculate correlation for all LR pairs, set this argument to NA.

Value

Tibble with expression correlation and prior knowledge support measures for ligand-receptor to target gene links

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"))
min_cells = 10
metadata_abundance = SummarizedExperiment::colData(sce)[,c(sample_id, group_id, celltype_id)] 
colnames(metadata_abundance) =c("sample_id", "group_id", "celltype_id")
abundance_data = metadata_abundance %>% tibble::as_tibble() %>% dplyr::group_by(sample_id , celltype_id) %>% dplyr::count() %>% dplyr::inner_join(metadata_abundance %>% tibble::as_tibble() %>% dplyr::distinct(sample_id , group_id ))
abundance_data = abundance_data %>% dplyr::mutate(keep = n >= min_cells) %>% dplyr::mutate(keep = factor(keep, levels = c(TRUE,FALSE)))
abundance_data_receiver = process_info_to_ic(abund_data = abundance_data, ic_type = "receiver")
abundance_data_sender = process_info_to_ic(abund_data = abundance_data, ic_type = "sender")

celltype_info = get_avg_frac_exprs_abund(sce = sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id)

receiver_info_ic = process_info_to_ic(info_object = celltype_info, ic_type = "receiver", lr_network = lr_network)
sender_info_ic = process_info_to_ic(info_object = celltype_info, ic_type = "sender", lr_network = lr_network)
senders_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()
receivers_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()
sender_receiver_info = combine_sender_receiver_info_ic(sender_info = sender_info_ic,receiver_info = receiver_info_ic,senders_oi = senders_oi,receivers_oi = receivers_oi,lr_network = lr_network)

abundance_expression_info = list(abund_plot_sample = abund_plot, abund_plot_group = abund_plot_boxplot, abundance_data_receiver = abundance_data_receiver, abundance_data_sender = abundance_data_sender, celltype_info = celltype_info, receiver_info_ic = receiver_info_ic, sender_info_ic = sender_info_ic, sender_receiver_info = sender_receiver_info)

celltype_de = perform_muscat_de_analysis(
   sce = sce,
   sample_id = sample_id,
   celltype_id = celltype_id,
   group_id = group_id,
   batches = batches,
   contrasts = contrasts_oi)
   
sender_receiver_de = combine_sender_receiver_de(
 sender_de = celltype_de,
 receiver_de = celltype_de,
 senders_oi = senders_oi,
 receivers_oi = receivers_oi,
 lr_network = lr_network)
 
ligand_activities_targets_DEgenes = get_ligand_activities_targets_DEgenes(
   receiver_de = celltype_de,
   receivers_oi = receivers_oi,
   receiver_frq_df_group = celltype_info$frq_df_group,
   ligand_target_matrix = ligand_target_matrix)


sender_receiver_tbl = sender_receiver_de %>% dplyr::distinct(sender, receiver)
metadata_combined = SummarizedExperiment::colData(sce) %>% tibble::as_tibble() 
grouping_tbl = metadata_combined[,c(sample_id, group_id)] %>% tibble::as_tibble() %>% dplyr::distinct()
colnames(grouping_tbl) = c("sample","group") 

frac_cutoff = 0.05
prioritization_tables = generate_prioritization_tables(
    sender_receiver_info = sender_receiver_info,
    sender_receiver_de = sender_receiver_de,
    ligand_activities_targets_DEgenes = ligand_activities_targets_DEgenes,
    contrast_tbl = contrast_tbl,
    sender_receiver_tbl = sender_receiver_tbl,
    grouping_tbl = grouping_tbl,
    fraction_cutoff = frac_cutoff, abundance_data_receiver, abundance_data_sender)

receivers_oi = prioritization_tables$group_prioritization_tbl$receiver %>% unique()
lr_target_prior_cor = lr_target_prior_cor_inference(receivers_oi, abundance_expression_info, celltype_de, grouping_tbl, prioritization_tables, ligand_target_matrix)


## End(Not run)

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