add_extra_criterion: add_extra_criterion

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

add_extra_criterion

Description

add_extra_criterion Update the aggregated prioritization score based on one or more new prioritization criteria. Examples of useful criteria: proteomics data for scoring ligands/receptors for DE at protein level, spatial co-localization of cell types and/or ligand-receptor pairs.

Usage

add_extra_criterion(prioritization_tables, new_criteria_tbl, regular_criteria_tbl, scenario = "regular")

Arguments

prioritization_tables	output of 'generate_prioritization_tables'
new_criteria_tbl	tibble with 3 columns: criterion, weight, regularization_factor. See example code and vignette for usage.
regular_criteria_tbl	tibble with 3 columns: criterion, weight, regularization_factor. See example code and vignette for usage.
scenario	Character vector indicating which prioritization weights should be used during the MultiNicheNet analysis. Currently 3 settings are implemented: "regular" (default), "lower_DE", and "no_frac_LR_expr". The setting "regular" is strongly recommended and gives each criterion equal weight. The setting "lower_DE" is recommended in cases your hypothesis is that the differential CCC patterns in your data are less likely to be driven by DE (eg in cases of differential migration into a niche). It halves the weight for DE criteria, and doubles the weight for ligand activity. "no_frac_LR_expr" is the scenario that will exclude the criterion "fraction of samples expressing the LR pair'. This may be beneficial in case of few samples per group.

Value

prioritization_tables with updated aggregated prioritization score based on the new criteria (same output as 'generate_prioritization_tables')

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)

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)
    
new_criteria_data = readRDS("data/additional_data_modality.rds")
prioritization_tables$group_prioritization_tbl = prioritization_tables$group_prioritization_tbl %>% inner_join(new_criteria_data)
regular_criteria_tbl = tibble(criterion = c("scaled_lfc_ligand","scaled_p_val_ligand_adapted","scaled_lfc_receptor","scaled_p_val_receptor_adapted", "max_scaled_activity", "scaled_pb_ligand", "scaled_pb_receptor", "fraction_expressing_ligand_receptor"), weight = NA, regularization_factor = c(0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1)) # do not change this
new_criteria_tbl = tibble(criterion = c("new_criterion1","new_criterion2"), weight = c(1,1), regularization_factor = c(0.5, 0.5)) # 
prioritization_tables = add_extra_criterion(prioritization_tables, new_criteria_tbl, regular_criteria_tbl, scenario = "regular") 

## End(Not run)

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