Package 'multinichenetr'

Description

add_empirical_pval_fdr Add empirical p-values and adjusted p-values to the DE output of Muscat. Credits to Jeroen Gillis (cf satuRn package)

Usage

add_empirical_pval_fdr(de_output_tidy, plot = FALSE)

Arguments

Value

de_output_tidy dataframe with two columns added: p_emp and p_adj_emp.

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)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
batches = NA
contrasts_oi = c("'High-Low','Low-High'")
senders_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()
receivers_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()
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)
de_output_tidy = muscat::resDS(celltype_de$sce, celltype_de$de_output, bind = "row", cpm = FALSE, frq = FALSE) %>% tibble::as_tibble()
de_output_tidy = add_empirical_pval_fdr(de_output_tidy)

## End(Not run)

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

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)

Description

alias_to_symbol_SCEConvert aliases to official gene symbols in a SingleCellExperiment Object. Makes use of 'nichenetr::convert_alias_to_symbols'

Usage

alias_to_symbol_SCE(sce, organism)

Arguments

Value

SingleCellExperiment Object

Examples

## Not run: 
sce = sce %>% alias_to_symbol_SCE("human")

## End(Not run)

Description

combine_sender_receiver_de Combine Muscat differential expression output for senders and receivers by linkgin ligands to receptors based on the prior knowledge ligand-receptor network.

Usage

combine_sender_receiver_de(sender_de, receiver_de, senders_oi, receivers_oi, lr_network)

Arguments

Value

Data frame combining Muscat DE output for sender and receiver linked to each other through joining by the ligand-receptor network.

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)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
batches = NA
contrasts_oi = c("'High-Low','Low-High'")
senders_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()
receivers_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()
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$de_output_tidy,
 receiver_de = celltype_de$de_output_tidy,
 senders_oi = senders_oi,
 receivers_oi = receivers_oi,
 lr_network = lr_network)

## End(Not run)

Description

combine_sender_receiver_info_ic Link the ligand-expression information of the Sender cell type to the receptor-expression information of the Receiver cell type. Linking via prior knowledge ligand-receptor network.

Usage

combine_sender_receiver_info_ic(sender_info, receiver_info, senders_oi, receivers_oi, lr_network)

Arguments

Value

List with data frames containing ligand-receptor sender-receiver combined expression information (see output of 'get_avg_frac_exprs_abund' and 'process_info_to_ic')

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)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
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)
  

## End(Not run)

Description

compare_normal_emp_pvals Compare nr and rank of DE genes between normal p-values and empirical p-values

Usage

compare_normal_emp_pvals(DE_info, DE_info_emp, adj_pval = FALSE)

Arguments

Value

a list òf plots for each celltype-contrast pair: an upset plot and line plot are drawn.

Examples

## Not run: 
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
batches = NA
covariates = NA
contrasts_oi = c("'High-Low','Low-High'")
senders_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()
receivers_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()
DE_info = get_DE_info(
   sce = sce,
   sample_id = sample_id,
   celltype_id = celltype_id,
   group_id = group_id,
   batches = batches,
   covariates = covariates,
   contrasts = contrasts_oi)
DE_info_emp = get_empirical_pvals(DE_info$celltype_de$de_output_tidy)
comparison_plots = compare_normal_emp_pvals(DE_info, DE_info_emp)

## End(Not run)

Description

fix_frq_df Fix muscat-feature/bug in fraction calculation: in case a there are no cells of a cell type in a sample, that expression fraction will be NA / NaN. Change these NA/NaN to 0.

Usage

fix_frq_df(sce, frq_celltype_samples)

Arguments

Value

Fixed data frame with fraction of cells expressing a gene.

Examples

## Not run: 
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
frq_df = get_muscat_exprs_frac(sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id) %>% .$frq_celltype_samples
if(nrow(frq_df %>% dplyr::filter(is.na(fraction_sample))) > 0 | nrow(frq_df %>% dplyr::filter(is.nan(fraction_sample))) > 0) {
  frq_df = fix_frq_df(sce, frq_df)
  }

## End(Not run)

Description

A data.frame/tibble describing HGNC human gene symbols, their entrez ids and potential aliases.

Usage

geneinfo_alias_human

Format

A data frame/tibble

symbol

human gene symbol

entrez

human gene entrez

alias

human gene alias

Description

A data.frame/tibble describing MGI mouse gene symbols, their entrez ids and potential aliases.

Usage

geneinfo_alias_mouse

Format

A data frame/tibble

symbol

mouse gene symbol

entrez

mouse gene entrez

alias

mouse gene alias

Description

generate_prioritization_tables Perform the MultiNicheNet prioritization of cell-cell interactions. Combine the following prioritization criteria in a single aggregated prioritization score: differential expression of ligand and receptor, cell-type-condition-specificity of expression of ligand and receptor, NicheNet ligand activity, fraction of samples in a group that express a senderLigand-receiverReceptor pair.

Usage

generate_prioritization_tables(sender_receiver_info, sender_receiver_de, ligand_activities_targets_DEgenes, contrast_tbl, sender_receiver_tbl, grouping_tbl, scenario = "regular", fraction_cutoff, abundance_data_receiver, abundance_data_sender, ligand_activity_down = FALSE)

Arguments

Value

List containing multiple data frames of prioritized senderLigand-receiverReceptor interactions (with sample- and group-based expression information), ligand activities and ligand-target 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)

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)

## End(Not run)

Description

generate_prioritization_tables_condition_specific_celltypes_receiver Perform the MultiNicheNet prioritization of cell-cell interactions. Focus on including condition-specific cell types as receiver cells. This implies no DE information will be used for prioritization of receptors, nor ligand activities for ligands Combine the following prioritization criteria in a single aggregated prioritization score: differential expression of ligand and receptor, cell-type-condition-specificity of expression of ligand and receptor, NicheNet ligand activity, fraction of samples in a group that express a senderLigand-receiverReceptor pair.

Usage

generate_prioritization_tables_condition_specific_celltypes_receiver(sender_receiver_info, sender_receiver_de, ligand_activities_targets_DEgenes, contrast_tbl, sender_receiver_tbl, grouping_tbl, scenario = "regular", fraction_cutoff, abundance_data_receiver, abundance_data_sender, ligand_activity_down = FALSE)

Arguments

Value

List containing multiple data frames of prioritized senderLigand-receiverReceptor interactions (with sample- and group-based expression information), ligand activities and ligand-target 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)

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_condition_specific_celltypes_receiver(
    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)

## End(Not run)

Description

generate_prioritization_tables_condition_specific_celltypes_sender Perform the MultiNicheNet prioritization of cell-cell interactions. Focus on including condition-specific cell types as sender cells. This implies no DE information will be used for prioritization of ligands. Combine the following prioritization criteria in a single aggregated prioritization score: differential expression of ligand and receptor, cell-type-condition-specificity of expression of ligand and receptor, NicheNet ligand activity, fraction of samples in a group that express a senderLigand-receiverReceptor pair.

Usage

generate_prioritization_tables_condition_specific_celltypes_sender(sender_receiver_info, sender_receiver_de, ligand_activities_targets_DEgenes, contrast_tbl, sender_receiver_tbl, grouping_tbl, scenario = "regular", fraction_cutoff, abundance_data_receiver, abundance_data_sender, ligand_activity_down = FALSE)

Arguments

Value

List containing multiple data frames of prioritized senderLigand-receiverReceptor interactions (with sample- and group-based expression information), ligand activities and ligand-target 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)

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_condition_specific_celltypes_sender(
    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)

## End(Not run)

Description

generate_prioritization_tables_sampleAgnostic_multifactorial Perform the MultiNicheNet prioritization of cell-cell interactions – only for analyses that are sample-agnostic/cell-level and require multifactorial analyses. Combine the following prioritization criteria in a single aggregated prioritization score: differential expression of ligand and receptor, cell-type-condition-specificity of expression of ligand and receptor, NicheNet ligand activity, fraction of samples in a group that express a senderLigand-receiverReceptor pair.

Usage

generate_prioritization_tables_sampleAgnostic_multifactorial(sender_receiver_info, sender_receiver_de, ligand_activities_targets_DEgenes, contrast_tbl, sender_receiver_tbl, grouping_tbl, scenario = "regular", fraction_cutoff, abundance_data_receiver, abundance_data_sender, ligand_activity_down = FALSE)

Arguments

Value

List containing multiple data frames of prioritized senderLigand-receiverReceptor interactions (with sample- and group-based expression information), ligand activities and ligand-target 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)

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_sampleAgnostic_multifactorial(
    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)

## End(Not run)

Description

get_abundance_info Visualize cell type abundances.

Usage

get_abundance_info(sce, sample_id, group_id, celltype_id, min_cells = 10, senders_oi, receivers_oi, batches = NA)

Arguments

Value

List containing cell type abundance plots and abundance_data data frame.

Examples

## Not run: 
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
senders_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()  
receivers_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique() 
abundance_celltype_info = get_abundance_info(sce = sce, sample_id = sample_id, group_id = group_id, celltype_id =  celltype_id, min_cells = 10, senders_oi = senders_oi, receivers_oi = receivers_oi)

## End(Not run)

Description

get_avg_pb_exprs Calculate the average and normalized pseudobulk expression of each gene per sample and per group.

Usage

get_avg_pb_exprs(sce, sample_id, celltype_id, group_id, batches = NA, min_cells = 10)

Arguments

Value

List containing data frames with average and normalized pseudobulk of expression per sample and per group.

Examples

## Not run: 
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
celltype_info = get_avg_pb_exprs(sce = sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id)

## End(Not run)

Description

get_DE_info Perform differential expression analysis via Muscat - Pseudobulking approach. Also visualize the p-value distribution. Under the hood, the following function is used: 'perform_muscat_de_analysis'.

Usage

get_DE_info(sce, sample_id, group_id, celltype_id, batches, covariates, contrasts_oi, expressed_df, min_cells = 10, assay_oi_pb = "counts", fun_oi_pb = "sum", de_method_oi = "edgeR", findMarkers = FALSE, contrast_tbl = NULL)

Arguments

Value

List with output of the differential expression analysis in 1) default format('muscat::pbDS()'), and 2) in a tidy table format ('muscat::resDS()') (both in the 'celltype_de' slot); Histogram plot of the p-values is also returned.

Examples

## Not run: 
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
batches = NA
covariates = NA
contrasts_oi = c("'High-Low','Low-High'")
frq_list = get_frac_exprs(sce = sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id)
DE_info = get_DE_info(
   sce = sce,
   sample_id = sample_id,
   celltype_id = celltype_id,
   group_id = group_id,
   batches = batches,
   covariates = covariates,
   contrasts = contrasts_oi,
   expressed_df = frq_list$expressed_df)

## End(Not run)

Description

get_DE_info_sampleAgnostic Perform differential expression analysis via scran::findMarkers approach. Also visualize the p-value distribution.

Usage

get_DE_info_sampleAgnostic(sce, group_id, celltype_id, contrasts_oi, expressed_df, min_cells = 10, contrast_tbl)

Arguments

Value

List with output of the differential expression analysis in 1) default format('muscat::pbDS()'), and 2) in a tidy table format ('muscat::resDS()') (both in the 'celltype_de' slot); Histogram plot of the p-values is also returned.

Examples

## Not run: 
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
batches = NA
covariates = NA
contrasts_oi = c("'High-Low','Low-High'")
contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
frq_list = get_frac_exprs_sampleAgnostic(sce = sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id)
DE_info = get_DE_info_sampleAgnostic(
   sce = sce,
   celltype_id = celltype_id,
   group_id = group_id,
   contrasts = contrasts_oi,
   expressed_df = frq_list$expressed_df,
   contrast_tbl = contrast_tbl)

## End(Not run)

Description

get_empirical_pvals Calculate empirical p-values based on a DE output. Show p-value distribution histograms. Under the hood, the following functions are used: 'add_empirical_pval_fdr' and 'get_FDR_empirical_plots_all'

Usage

get_empirical_pvals(de_output_tidy)

Arguments

Value

'de_output_tidy', but now 2 columns added with the empirical pvalues (normal and adjusted for multiple testing); Histogram plot of the empirical p-values is also returned.

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)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
batches = NA
contrasts_oi = c("'High-Low','Low-High'")
senders_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()
receivers_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()
DE_info = get_DE_info(
   sce = sce,
   sample_id = sample_id,
   celltype_id = celltype_id,
   group_id = group_id,
   batches = batches,
   contrasts = contrasts_oi)
DE_info_emp = get_empirical_pvals(DE_info$celltype_de$de_output_tidy)

## End(Not run)

Description

get_FDR_empirical Add empirical p-values and adjusted p-values to a subset of the DE output table. This is the function that works under the hood of 'add_empirical_pval_fdr'. Credits to Jeroen Gillis (cf satuRn package)

Usage

get_FDR_empirical(de_output_tidy, cluster_id_oi, contrast_oi, plot = FALSE)

Arguments

Value

de_output_tidy dataframe (for the celltype and contrast of interest) with two columns added: p_emp and p_adj_emp.

Description

get_FDR_empirical_plots Get diagnostic plots of the empirical null.. This is the function that works under the hood of 'get_FDR_empirical_plots_all'. Credits to Jeroen Gillis (cf satuRn package)

Usage

get_FDR_empirical_plots(de_output_tidy, cluster_id_oi, contrast_oi)

Arguments

Value

plot object

Description

get_FDR_empirical_plots_all Get diagnostic plots of the empirical null. Credits to Jeroen Gillis (cf satuRn package)

Usage

get_FDR_empirical_plots_all(de_output_tidy)

Arguments

Value

list of plots

Description

get_frac_exprs Calculate the average fraction of expression of each gene per sample and per group.

Usage

get_frac_exprs(sce, sample_id, celltype_id, group_id, batches = NA, min_cells = 10, fraction_cutoff = 0.05, min_sample_prop = 0.5)

Arguments

Value

List containing data frames with the fraction of expression per sample and per group.

Examples

## Not run: 
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
frac_info = get_frac_exprs(sce = sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id)

## End(Not run)

Description

get_frac_exprs_sampleAgnostic Calculate the average fraction of expression of each gene per group. All cells from all samples will be pooled per group/condition.

Usage

get_frac_exprs_sampleAgnostic(sce, sample_id, celltype_id, group_id, batches = NA, min_cells = 10, fraction_cutoff = 0.05, min_sample_prop = 0.5)

Arguments

Value

List containing data frames with the fraction of expression per sample and per group.

Examples

## Not run: 
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
frac_info = get_frac_exprs_sampleAgnostic(sce = sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id)

## End(Not run)

Description

get_ligand_activities_targets_DEgenes Predict NicheNet ligand activities and ligand-target links for the receiver cell types. Uses 'nichenetr::predict_ligand_activities()' and 'nichenetr::get_weighted_ligand_target_links()' under the hood.

Usage

get_ligand_activities_targets_DEgenes(receiver_de, receivers_oi,  ligand_target_matrix, logFC_threshold = 0.50, p_val_threshold = 0.05, p_val_adj = FALSE, top_n_target = 250, verbose = FALSE, n.cores = 1)

Arguments

Value

List with two data frames: one data frame containing the ligand activities and ligand-target links, one containing the DE gene information.

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'")
receivers_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique() 
celltype_info = get_avg_frac_exprs_abund(sce = sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id)
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)
receiver_de = celltype_de$de_output_tidy
ligand_activities_targets_DEgenes = get_ligand_activities_targets_DEgenes(
   receiver_de = receiver_de,
   receivers_oi = receivers_oi,
   ligand_target_matrix = ligand_target_matrix)

## End(Not run)

Description

get_ligand_activities_targets_DEgenes_beta BETA-version with new parallelization functionality – Predict NicheNet ligand activities and ligand-target links for the receiver cell types. Uses 'nichenetr::predict_ligand_activities()' and 'nichenetr::get_weighted_ligand_target_links()' under the hood.

Usage

get_ligand_activities_targets_DEgenes_beta(receiver_de, receivers_oi,  ligand_target_matrix, logFC_threshold = 0.50, p_val_threshold = 0.05, p_val_adj = FALSE, top_n_target = 250, verbose = FALSE, n.cores = 1)

Arguments

Value

List with two data frames: one data frame containing the ligand activities and ligand-target links, one containing the DE gene information.

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'")
receivers_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique() 
celltype_info = get_avg_frac_exprs_abund(sce = sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id)
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)
receiver_de = celltype_de$de_output_tidy
ligand_activities_targets_DEgenes = get_ligand_activities_targets_DEgenes_beta(
   receiver_de = receiver_de,
   receivers_oi = receivers_oi,
   ligand_target_matrix = ligand_target_matrix,
   n.cores = 2)

## End(Not run)

Description

get_muscat_exprs_avg Calculate sample- and group-average of gene expression per cell type.

Usage

get_muscat_exprs_avg(sce, sample_id, celltype_id, group_id)

Arguments

Value

Data frame with average gene expression per sample and per group.

Examples

## Not run: 
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
muscat_exprs_avg = get_muscat_exprs_avg(sce = sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id)

## End(Not run)

Description

get_muscat_exprs_frac Calculate sample- and group-average of fraction of cells in a cell type expressing a gene.

Usage

get_muscat_exprs_frac(sce, sample_id, celltype_id, group_id)

Arguments

Value

List with two dataframes: one with fraction of cells in a cell type expressing a gene, averaged per sample; and one averaged per group.

Examples

## Not run: 
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
muscat_exprs_frac = get_muscat_exprs_frac(sce = sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id)

## End(Not run)

Description

get_pseudobulk_logCPM_exprs Calculate the 'library-size' normalized pseudbulk counts per sample for each gene - returned values are similar to logCPM.

Usage

get_pseudobulk_logCPM_exprs(sce, sample_id, celltype_id, group_id, batches = NA, assay_oi_pb = "counts", fun_oi_pb = "sum")

Arguments

Value

Data frame with logCPM-like values of the library-size corrected pseudobulked counts ('pb_sample') per gene per sample. pb_sample = log2( ((pb_raw/effective_library_size) \* 1000000) + 1). effective_library_size = lib.size \* norm.factors (through edgeR::calcNormFactors).

Examples

## Not run: 
library(dplyr)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
pseudobulk_logCPM_exprs = get_pseudobulk_logCPM_exprs(sce = sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id)

## End(Not run)

Description

get_top_n_lr_pairs Get top n ligand-receptor pairs based on MultiNicheNet prioritization. Top pairs can be filtered by group, senders, receivers en based on top_n or cutoff based on the prioritization score.

Usage

get_top_n_lr_pairs(prioritization_tables, top_n, groups_oi = NULL, senders_oi = NULL, receivers_oi = NULL, rank_per_group = TRUE)

Arguments

Value

Tibble that shows the top-ranked ligand-receptor pairs for the groups and cell types of interest

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)
    
top50_tbl = get_top_n_lr_pairs(prioritization_tables, 50)

## End(Not run)

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

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)

Description

Matrix with ligand-target regulatory potential scores

Usage

ligand_target_matrix_test

Format

A matrix

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

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)

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

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)

Description

make_circos_one_group Make a circos plot with top prioritized ligand-receptor interactions for one group of interest.

Usage

make_circos_one_group(prioritized_tbl_oi, colors_sender, colors_receiver)

Arguments

Value

a list with a circos plot for one 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)

Description

make_DEgene_dotplot_pseudobulk Visualize the scaled pseudobulk expression of DE genes per sample, and compare the different groups. Genes in rows, samples in columns

Usage

make_DEgene_dotplot_pseudobulk(genes_oi, celltype_info, prioritization_tables, celltype_oi, grouping_tbl, groups_oi = NULL)

Arguments

Value

Gene expression dotplot list: pseudobulk version and single-cell version

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
     
     )
group_oi = "High"
receiver_oi = "Malignant"
targets_oi = output$ligand_activities_targets_DEgenes$de_genes_df %>% inner_join(contrast_tbl) %>% filter(group == group_oi) %>% arrange(p_val) %>% filter(receiver == receiver_oi) %>% pull(gene) %>% unique()
p_target = make_DEgene_dotplot_pseudobulk(genes_oi = targets_oi, celltype_info = output$celltype_info, prioritization_tables = output$prioritization_tables, celltype_oi = receiver_oi, output$grouping_tbl)

## End(Not run)

Description

make_DEgene_dotplot_pseudobulk_batch Visualize the scaled pseudobulk expression of DE genes per sample, and compare the different groups. Genes in rows, samples in columns

Usage

make_DEgene_dotplot_pseudobulk_batch(genes_oi, celltype_info, prioritization_tables, celltype_oi, batch_oi, grouping_tbl, groups_oi = NULL)

Arguments

Value

Gene expression dotplot list: pseudobulk version and single-cell version

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 = "batch"
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
     
     )
group_oi = "High"
receiver_oi = "Malignant"
targets_oi = output$ligand_activities_targets_DEgenes$de_genes_df %>% inner_join(contrast_tbl) %>% filter(group == group_oi) %>% arrange(p_val) %>% filter(receiver == receiver_oi) %>% pull(gene) %>% unique()
p_target = make_DEgene_dotplot_pseudobulk_batch(genes_oi = targets_oi, celltype_info = output$celltype_info, prioritization_tables = output$prioritization_tables, celltype_oi = receiver_oi, batch_oi = batches, output$grouping_tbl)

## End(Not run)

Description

make_DEgene_dotplot_pseudobulk_reversed Visualize the scaled pseudobulk expression of DE genes per sample, and compare the different groups. Genes and sample positions are reversed compared to 'make_DEgene_dotplot_pseudobulk': genes in columns, samples in rows.

Usage

make_DEgene_dotplot_pseudobulk_reversed(genes_oi, celltype_info, prioritization_tables, celltype_oi, grouping_tbl, groups_oi = NULL, target_regulation_df = NULL)

Arguments

Value

Gene expression dotplot list: pseudobulk version and single-cell version

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
     
     )
group_oi = "High"
receiver_oi = "Malignant"
targets_oi = output$ligand_activities_targets_DEgenes$de_genes_df %>% inner_join(contrast_tbl) %>% filter(group == group_oi) %>% arrange(p_val) %>% filter(receiver == receiver_oi) %>% pull(gene) %>% unique()
p_target = make_DEgene_dotplot_pseudobulk_reversed(genes_oi = targets_oi, celltype_info = output$celltype_info, prioritization_tables = output$prioritization_tables, celltype_oi = receiver_oi, output$grouping_tbl)

## End(Not run)

Description

make_ggraph_ligand_target_links Make a network showing the gene regulatory links between ligands from sender cell types to their induced ligands/receptors in receiver cell types. Lins are only drawn if the ligand/receptor in the receiver is a potential downstream target of the ligand based on prior knowledge and sufficient correlation in expression across the different samples.

Usage

make_ggraph_ligand_target_links(lr_target_prior_cor_filtered, prioritized_tbl_oi, colors)

Arguments

Value

ggplot object with plot of LR–>Target links, the graph object itself, and the prioritized LR 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"))
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)
graph_plot = make_ggraph_ligand_target_links(lr_target_prior_cor_filtered = lr_target_prior_cor_filtered, prioritized_tbl_oi = prioritized_tbl_oi, colors = c("blue","red"))
graph_plot$plot

## End(Not run)

Description

make_ggraph_signaling_path Visualize the Ligand-Receptor to target signaling paths

Usage

make_ggraph_signaling_path(signaling_graph_list, colors, ligands_all, receptors_all, targets_all)

Arguments

Value

ggraph and tidygraph objec of signaling paths between predefined LR–>Target links

Examples

## Not run: 
library(dplyr)
weighted_networks = readRDS(url("https://zenodo.org/record/3260758/files/weighted_networks.rds"))
ligand_tf_matrix = readRDS(url("https://zenodo.org/record/3260758/files/ligand_tf_matrix.rds"))
lr_network = readRDS(url("https://zenodo.org/record/3260758/files/lr_network.rds"))
sig_network = readRDS(url("https://zenodo.org/record/3260758/files/signaling_network.rds"))
gr_network = readRDS(url("https://zenodo.org/record/3260758/files/gr_network.rds"))
ligands_all = "COL1A1" # this can be a list of multiple ligands if required
receptors_all = "ITGB1"
targets_all = c("S100A1","SERPINE1")
active_signaling_network = nichenetr::get_ligand_signaling_path_with_receptor(ligand_tf_matrix = ligand_tf_matrix, ligands_all = ligands_all, receptors_all = receptors_all, targets_all = targets_all, weighted_networks = weighted_networks, top_n_regulators = 2)
data_source_network = nichenetr::infer_supporting_datasources(signaling_graph_list = active_signaling_network,lr_network = lr_network, sig_network = sig_network, gr_network = gr_network)
active_signaling_network_min_max = active_signaling_network
active_signaling_network_min_max$sig = active_signaling_network_min_max$sig %>% mutate(weight = ((weight-min(weight))/(max(weight)-min(weight))) + 0.75)
active_signaling_network_min_max$gr = active_signaling_network_min_max$gr %>% mutate(weight = ((weight-min(weight))/(max(weight)-min(weight))) + 0.75)
colors = c("ligand" = "indianred2", "receptor" = "orange", "target" = "steelblue2", "mediator" = "grey75")
ggraph_signaling_path = make_ggraph_signaling_path(active_signaling_network_min_max, colors)#' colors = c("ligand" = "indianred2", "receptor" = "orange", "target" = "steelblue2", "mediator" = "grey25")


## End(Not run)

Description

make_ligand_activity_plots Visualize the ligand activities (normal and scaled) of each group-receiver combination

Usage

make_ligand_activity_plots(prioritization_tables, ligands_oi, contrast_tbl, widths = NULL)

Arguments

Value

Heatmap of ligand activities (normal and scaled) of each group-receiver combination

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
     
     )
ligands_oi = output$prioritization_tables$ligand_activities_target_de_tbl %>% inner_join(contrast_tbl) %>% group_by(group, receiver) %>% distinct(ligand, receiver, group, activity) %>% top_n(5, activity) %>% pull(ligand) %>% unique()
plot_oi = make_ligand_activity_plots(output$prioritization_tables, ligands_oi, contrast_tbl)
plot_oi

## End(Not run)

Description

make_ligand_activity_target_plot Summary plot showing the activity of prioritized ligands acting on a receiver cell type of interest, together with the predicted target genes and their sample-by-sample expression

Usage

make_ligand_activity_target_plot(group_oi, receiver_oi, prioritized_tbl_oi, prioritization_tables, ligand_activities_targets_DEgenes, contrast_tbl, grouping_tbl, receiver_info, ligand_target_matrix, groups_oi = NULL, plot_legend = TRUE, heights = NULL, widths = NULL)

Arguments

Value

Summary plot showing the activity of prioritized ligands acting on a receiver cell type of interest, together with the predicted target genes and their sample-by-sample 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
     
     )
group_oi = "High"
receiver_oi = "Malignant"
prioritized_tbl_oi = output$prioritization_tables$group_prioritization_tbl %>% filter(fraction_expressing_ligand_receptor > 0) %>% filter(group == group_oi & receiver == receiver_oi) %>% top_n(50, prioritization_score) %>% top_n(25, activity_scaled) %>% arrange(-activity_scaled)
combined_plot = make_ligand_activity_target_plot(group_oi, receiver_oi, prioritized_tbl_oi, output$prioritization_tables, output$ligand_activities_targets_DEgenes, contrast_tbl, output$grouping_tbl, output$celltype_info,ligand_target_matrix, plot_legend = FALSE)


## End(Not run)

Description

make_ligand_receptor_violin_plot Plot combining a violin plot of of the ligand of interest in the sender cell type of interest, and a violin plot of the receptor of interest in the receiver cell type of interest.

Usage

make_ligand_receptor_violin_plot(sce, ligand_oi, receptor_oi, sender_oi, receiver_oi, group_oi, group_id, sample_id, celltype_id, batch_oi = NA, background_groups = NULL)

Arguments

Value

Plot combining a violin plot of of the ligand of interest in the sender cell type of interest, and a violin plot of the receptor of interest in the receiver cell type of interest.

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 = "DLL1"
receptor_oi = "NOTCH3"
group_oi = "High"
sender_oi = "Malignant"
receiver_oi = "myofibroblast"
p_violin = make_ligand_receptor_violin_plot(sce = sce, ligand_oi = ligand_oi, receptor_oi = receptor_oi, group_oi = group_oi, group_id = group_id, sender_oi = sender_oi, receiver_oi = receiver_oi, sample_id = sample_id, celltype_id = celltype_id)


## End(Not run)

Description

make_lite_output Reduce the size of the MultiNicheNet output object (for memory efficiency), by only keeping expression information for present ligands, receptors, and genes DE in at least one probed condition.

Usage

make_lite_output(multinichenet_output, top_n_LR = 2500)

Arguments

Value

multinichenet output list (= result of 'multi_nichenet_analysis()'), but now filtered such that expression information is only returned for present ligands, receptors, and genes DE in at least one probed condition.

Examples

## Not run: 
library(dplyr)
multinichenet_output = multinichenet_output %>% make_lite_output()


## End(Not run)

Description

make_lite_output_condition_specific Reduce the size of the MultiNicheNet output object (for memory efficiency), by only keeping expression information for present ligands, receptors, and genes DE in at least one probed condition. This function is specific for an object that contains the prioritization tables of the condition-specific cell type workflow as well.

Usage

make_lite_output_condition_specific(multinichenet_output, top_n_LR = 2500)

Arguments

Value

multinichenet output list (= result of 'multi_nichenet_analysis()'), but now filtered such that expression information is only returned for present ligands, receptors, and genes DE in at least one probed condition.

Examples

## Not run: 
library(dplyr)
multinichenet_output = multinichenet_output %>% make_lite_output_condition_specific()


## End(Not run)

Description

make_lr_target_correlation_plot Plot Ligand-Receptor expression, Ligand-Receptor–>Target gene links that are both supported by prior knowledge and have correlation in expression, and Target expression

Usage

make_lr_target_correlation_plot(prioritization_tables, prioritized_tbl_oi, lr_target_prior_cor_filtered, grouping_tbl, receiver_info, receiver_oi, plot_legend = TRUE, heights = NULL, widths = NULL)

Arguments

Value

ggplot object with a combined 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
     )
group_oi = "High"
receiver_oi = "Malignant"
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) 
lr_target_prior_cor_filtered = output$lr_target_prior_cor %>% filter(scaled_prior_score > 0.50 & (pearson > 0.66 | spearman > 0.66))
prioritized_tbl_oi = prioritized_tbl_oi %>% filter(id %in% lr_target_prior_cor_filtered$id)
prioritized_tbl_oi = prioritized_tbl_oi %>% group_by(ligand, sender, group) %>% top_n(2, prioritization_score)
lr_target_correlation_plot = make_lr_target_correlation_plot(output$prioritization_tables, prioritized_tbl_oi, lr_target_prior_cor_filtered, output$grouping_tbl, output$celltype_info, receiver_oi) 

## End(Not run)

Description

make_lr_target_prior_cor_heatmap Plot Ligand-Receptor–>Target gene links that are both supported by prior knowledge and have correlation in expression

Usage

make_lr_target_prior_cor_heatmap(lr_target_prior_cor_filtered, add_grid = TRUE)

Arguments

Value

ggplot object with plot of LR–>Target 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"))
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))
lr_target_prior_cor_heatmap = make_lr_target_prior_cor_heatmap(lr_target_prior_cor_filtered) 

## End(Not run)

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

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)

Description

make_sample_lr_prod_activity_batch_plots Visualize the scaled product of Ligand-Receptor (pseudobulk) expression per sample, and compare the different groups. In addition, show the NicheNet ligand activities in each receiver-celltype combination. On top of this summary plot, a heatmap indicates the batch value for each displayed sample.

Usage

make_sample_lr_prod_activity_batch_plots(prioritization_tables, prioritized_tbl_oi, grouping_tbl, batch_oi, widths = NULL, heights = NULL)

Arguments

Value

Ligand-Receptor Expression Product & Ligand Activities Dotplot/Heatmap, complemented with a heatmap indicating the batch of interest

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 = "batch"
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
     
     )
group_oi = "High"
batch_oi = "batch"
prioritized_tbl_oi = output$prioritization_tables$group_prioritization_tbl %>% filter(fraction_expressing_ligand_receptor > 0) %>% filter(group == group_oi) %>% top_n(50, prioritization_score) 
plot_oi = make_sample_lr_prod_activity_batch_plots(output$prioritization_tables, prioritized_tbl_oi, output$grouping_tbl, batch_oi = batch_oi)
plot_oi

## End(Not run)

Description

make_sample_lr_prod_activity_plots Visualize the scaled product of Ligand-Receptor (pseudobulk) expression per sample, and compare the different groups. In addition, show the NicheNet ligand activities in each receiver-celltype combination.

Usage

make_sample_lr_prod_activity_plots(prioritization_tables, prioritized_tbl_oi, widths = NULL)

Arguments

Value

Ligand-Receptor Expression Product & Ligand Activities Dotplot/Heatmap

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
     
     )
group_oi = "High"
prioritized_tbl_oi = output$prioritization_tables$group_prioritization_tbl %>% filter(fraction_expressing_ligand_receptor > 0) %>% filter(group == group_oi) %>% top_n(50, prioritization_score) 
plot_oi = make_sample_lr_prod_activity_plots(output$prioritization_tables, prioritized_tbl_oi)
plot_oi

## End(Not run)