In saeyslab/nichenetr: NicheNet: Modeling Intercellular Communication by Linking Ligands to Target Genes

knitr::opts_chunk$set(
  collapse = TRUE,
  # comment = "#>",
  warning = FALSE,
  message = FALSE
)

In this vignette, we will extend the basic NicheNet analysis analysis from Perform NicheNet analysis starting from a Seurat object: step-by-step analysis by incorporating gene expression as part of the prioritization This is a generalization of the Differential NicheNet and MultiNicheNet approach. While the original NicheNet only ranks ligands based on the ligand activity analysis, it is now also possible to prioritize ligands based on cell type and condition specificity of the ligand and receptor.

We will again make use of mouse NICHE-seq data to explore intercellular communication in the T cell area in the inguinal lymph node before and 72 hours after lymphocytic choriomeningitis virus (LCMV) infection [@medaglia_spatial_2017]. The used ligand-target matrix and the Seurat object of the processed NICHE-seq single-cell data can be downloaded from Zenodo.

Make sure you understand the different steps in a NicheNet analysis that are described in the basic vignette before proceeding with this vignette.

Prepare NicheNet analysis

Load required packages, read in the Seurat object with processed expression data of interacting cells and NicheNet's ligand-target prior model, ligand-receptor network and weighted integrated networks.

library(nichenetr) # Please update to v2.0.6
library(Seurat)
library(SeuratObject)
library(tidyverse)

# Read Seurat object
seuratObj <- readRDS(url("https://zenodo.org/record/3531889/files/seuratObj.rds"))
seuratObj <- UpdateSeuratObject(seuratObj)
seuratObj <- alias_to_symbol_seurat(seuratObj, "mouse")

# Load in networks
lr_network <- readRDS(url("https://zenodo.org/record/7074291/files/lr_network_mouse_21122021.rds"))
ligand_target_matrix <- readRDS(url("https://zenodo.org/record/7074291/files/ligand_target_matrix_nsga2r_final_mouse.rds"))
weighted_networks <- readRDS(url("https://zenodo.org/record/7074291/files/weighted_networks_nsga2r_final_mouse.rds"))

lr_network <- lr_network %>% distinct(from, to)

Perform the NicheNet analysis

We will use the sender-focused approach here.

# 1. Define set of potential ligands
receiver <- "CD8 T"
expressed_genes_receiver <- get_expressed_genes(receiver, seuratObj, pct = 0.05)

sender_celltypes <- c("CD4 T", "Treg", "Mono", "NK", "B", "DC")
list_expressed_genes_sender <- sender_celltypes %>% unique() %>% lapply(get_expressed_genes, seuratObj, 0.05)
expressed_genes_sender <- list_expressed_genes_sender %>% unlist() %>% unique()

all_ligands <- unique(lr_network$from)
all_receptors <- unique(lr_network$to)

expressed_ligands <- intersect(all_ligands, expressed_genes_sender)
expressed_receptors <- intersect(all_receptors, expressed_genes_receiver)

potential_ligands <- lr_network %>% filter(from %in% expressed_ligands & to %in% expressed_receptors) %>%
  pull(from) %>% unique()


# 2. Define the gene set of interest
condition_oi <-  "LCMV"
condition_reference <- "SS"

seurat_obj_receiver <- subset(seuratObj, idents = receiver)

DE_table_receiver <-  FindMarkers(object = seurat_obj_receiver,
                                  ident.1 = condition_oi, ident.2 = condition_reference,
                                  group.by = "aggregate", 
                                  min.pct = 0.05) %>% rownames_to_column("gene")

geneset_oi <- DE_table_receiver %>% filter(p_val_adj <= 0.05 & abs(avg_log2FC) >= 0.25) %>% pull(gene)
geneset_oi <- geneset_oi %>% .[. %in% rownames(ligand_target_matrix)]

# 3. Define background genes
background_expressed_genes <- expressed_genes_receiver %>% .[. %in% rownames(ligand_target_matrix)]

# 4. Perform NicheNet ligand activity analysis
ligand_activities <- predict_ligand_activities(geneset = geneset_oi,
                                               background_expressed_genes = background_expressed_genes,
                                               ligand_target_matrix = ligand_target_matrix,
                                               potential_ligands = potential_ligands)

ligand_activities <- ligand_activities %>% arrange(-aupr_corrected) %>%
  mutate(rank = rank(desc(aupr_corrected)))

Perform prioritization of ligand-receptor pairs

We will prioritize ligand-receptor pairs based on the following criteria (with their corresponding weight names):

• Upregulation of the ligand in a sender cell type compared to other cell types: de_ligand
• Upregulation of the receptor in a receiver cell type: de_receptor
• Average expression of the ligand in the sender cell type: exprs_ligand
• Average expression of the receptor in the receiver cell type: exprs_receptor
• Condition-specificity of the ligand across all cell types: ligand_condition_specificity
• Condition-specificity of the receptor across all cell types: receptor_condition_specificity

This means that we will have to calculate:

• Differential expression of the ligand/receptor in a sender/receiver cell type
• The average expression of each ligand/receptor in each sender/receiver cell type
• Differential expression of the ligand/receptor between the two conditions

We provide a wrapper function generate_info_tables that will calculate all these values for you. This function returns a list with three dataframes:

• sender_receiver_de: differential expression of the ligand and receptor in the sender-receiver cell type pair. These were first calculated separately (i.e., DE of ligand in sender cell type, DE of receptor in receiver cell type based on FindAllMarkers) and then combined based on possible interactions from the lr_network.
• sender_receiver_info: the average expression of the ligand and receptor in sender-receiver cell type pairs
• lr_condition_de: differential expression of the ligand and receptor between the two conditions across all cell types.

Note that cell type specificity (i.e., the first four conditions) is calculated only in the condition of interest.

The "scenario" argument can be either "case_control" or "one_condition". In "case_control" scenario, condition specificity is calculated.

lr_network_filtered <-  lr_network %>% filter(from %in% expressed_ligands & to %in% expressed_receptors)

info_tables <- generate_info_tables(seuratObj,
                                    celltype_colname = "celltype",
                                    senders_oi = sender_celltypes,
                                    receivers_oi = receiver,
                                    lr_network = lr_network_filtered,
                                    condition_colname = "aggregate",
                                    condition_oi = condition_oi,
                                    condition_reference = condition_reference,
                                    scenario = "case_control")

names(info_tables)
info_tables$sender_receiver_de %>% head()
info_tables$sender_receiver_info %>% head()
info_tables$lr_condition_de %>% head()

Next, we generate the prioritization table. This table contains the rankings of ligand-receptor pairs based on the different criteria. We provide two scenarios: case_control and one_condition. In the "case_control" scenario, all weights are set to 1. If "one_condition", the weights are set to 0 for condition specificity and 1 for the remaining criteria. Users can also provide their own weights using the prioritizing_weights argument.

prior_table <- generate_prioritization_tables(info_tables$sender_receiver_info,
                                              info_tables$sender_receiver_de,
                                              ligand_activities,
                                              info_tables$lr_condition_de,
                                              scenario = "case_control")

prior_table %>% head

As you can see, the resulting table now show the rankings for ligand-receptor interactions of a sender-receiver cell type pair, instead of just the prioritized ligands. Cxcl10 now went up in the rankings due to both the high expression of its potential receptor Dpp4 and its high celltype specificity (scaled_lfc_ligand). You can also see this in the visualizations further below.

We included all columns here, but if you just want relevant columns that were used to calculate the ranking:

prior_table %>% select(c('sender', 'receiver', 'ligand', 'receptor', 'scaled_p_val_ligand_adapted', 'scaled_p_val_receptor_adapted', 'scaled_avg_exprs_ligand', 'scaled_avg_exprs_receptor', 'scaled_p_val_ligand_adapted_group', 'scaled_p_val_receptor_adapted_group', 'scaled_activity'))

Note that we appended the suffix '_group' to columns that refer to differential expression between conditions, e.g., lfc_ligand_group and lfc_receptor_group.

Step-by-step prioritization

generate_info_tables is a wrapper function that calculates all the information needed for the prioritization. However, in some cases you may need more flexibility on how these values are calculated (but note that you can pass extra arguments to generate_info_tables that will get passed on to FindMarkers, FindAllMarkers, and AverageExpression). Below, we show how we use helper functions calculate_de and get_exprs_avg to calculate the DE and get the average expression used for cell type specificity. process_table_to_ic transforms these different dataframes so they are compatible with the generate_prioritization_tables function.

# Only calculate DE for LCMV condition, with genes that are in the ligand-receptor network
DE_table <- FindAllMarkers(subset(seuratObj, subset = aggregate == "LCMV"),
                           min.pct = 0, logfc.threshold = 0, return.thresh = 1,
                           features = unique(unlist(lr_network_filtered))) 

# Average expression information - only for LCMV condition
expression_info <- get_exprs_avg(seuratObj, "celltype", condition_colname = "aggregate", condition_oi = condition_oi,
                                 features = unique(unlist(lr_network_filtered)))

# Calculate condition specificity - only for datasets with two conditions!
condition_markers <- FindMarkers(object = seuratObj, ident.1 = condition_oi, ident.2 = condition_reference,
                                 group.by = "aggregate", min.pct = 0, logfc.threshold = 0,
                                 features = unique(unlist(lr_network_filtered))) %>% rownames_to_column("gene")

# Combine DE of senders and receivers -> used for prioritization
processed_DE_table <- process_table_to_ic(DE_table, table_type = "celltype_DE", lr_network_filtered,
                                         senders_oi = sender_celltypes, receivers_oi = receiver)

processed_expr_table <- process_table_to_ic(expression_info, table_type = "expression", lr_network_filtered)

processed_condition_markers <- process_table_to_ic(condition_markers, table_type = "group_DE", lr_network_filtered)

And here is how you can define custom weights:

prioritizing_weights = c("de_ligand" = 1,
                          "de_receptor" = 1,
                          "activity_scaled" = 1,
                          "exprs_ligand" = 1,
                          "exprs_receptor" = 1,
                         "ligand_condition_specificity" = 1,
                         "receptor_condition_specificity" = 1)

prior_table <- generate_prioritization_tables(processed_expr_table,
                                              processed_DE_table,
                                              ligand_activities,
                                              processed_condition_markers,
                                              prioritizing_weights)

prior_table %>% head

Prioritizing across multiple receivers

As NicheNet is a receiver-based pipeline, to prioritize ligand-receptor pairs across multiple receivers, we need to perform the NicheNet analysis for each receiver separately. Let's suppose we want to prioritize ligand-receptor pairs across all T cells (CD4, CD8, and Tregs). The CD8 T analysis has already been performed above. We will use the wrapper function to perform a basic NicheNet analysis on the other two:

nichenet_output <- lapply(c("CD4 T", "Treg"), function(receiver_ct){
  nichenet_seuratobj_aggregate(receiver = receiver_ct,
                             seurat_obj = seuratObj,
                             condition_colname = "aggregate",
                             condition_oi = condition_oi,
                             condition_reference = condition_reference,
                             sender = sender_celltypes,
                             ligand_target_matrix = ligand_target_matrix,
                             lr_network = lr_network,
                             weighted_networks = weighted_networks,
                             expression_pct = 0.05)

}) %>% setNames(c("CD4 T", "Treg"))

To generate the dataframes used for prioritization, we will simply change the lr_network_filtered argument to only calculate DE and expression values for ligand-receptor pairs of interest.

info_tables2 <- lapply(names(nichenet_output), function(receiver_ct) {
  generate_info_tables(seuratObj,
                       celltype_colname = "celltype",
                       senders_oi = sender_celltypes,
                        receivers_oi = receiver_ct,
                          lr_network_filtered = lr_network %>%
                            filter(from %in% nichenet_output[[receiver_ct]]$ligand_activities$test_ligand &
                                     to %in% nichenet_output[[receiver_ct]]$background_expressed_genes),
                          condition_colname = "aggregate",
                          condition_oi = condition_oi,
                          condition_reference = condition_reference,
                          scenario = "case_control")
})

We can then combine the results from generate_info_tables using bind_rows, which will concatenate the rows together. For the ligand activities, we will also add an additional column containing the receiver cell type. Note that for the average expression table (sender_receiver_info) and condition specificity (lr_condition_de), we need to remove duplicate rows.

# Add CD8 T to list
info_tables2[[3]] <- info_tables

# bind rows of each element of info_tables using pmap
info_tables_combined <- purrr::pmap(info_tables2, bind_rows)

# Combine ligand activities and add receiver information
ligand_activities_combined <- bind_rows(nichenet_output$`CD4 T`$ligand_activities %>% mutate(receiver = "CD4 T"),
                                        nichenet_output$Treg$ligand_activities %>% mutate(receiver = "Treg"),
                                        ligand_activities %>% mutate(receiver = "CD8 T"))

prior_table_combined <- generate_prioritization_tables(
  sender_receiver_info = info_tables_combined$sender_receiver_info %>% distinct,
  sender_receiver_de = info_tables_combined$sender_receiver_de,
  ligand_activities = ligand_activities_combined,
  lr_condition_de = info_tables_combined$lr_condition_de %>% distinct,
  scenario = "case_control")

head(prior_table_combined)

Extra visualization of ligand-receptor pairs

In addition to the usual heatmap visualizations, we provide a function make_circos_lr to visualize the ligand-receptor pairs in a circos plot. This was originally written for the (now deprecated) Differential NicheNet vignettes. The function takes in a prioritization table and a named vector for the color of senders and receivers. We first specify the number of top ligand-receptor pairs to show with n.

# Get top n ligand-receptor pairs
prior_table_oi <- prior_table_combined %>% slice_max(prioritization_score, n = 50)

# Define colors for senders and receivers
senders_receivers <- prior_table_oi %>% select(sender, receiver) %>% unlist %>% unique %>% sort
celltype_colors <- RColorBrewer::brewer.pal(length(senders_receivers), name = 'Set3') %>%
  magrittr::set_names(senders_receivers)

circos_plot <- make_circos_lr(prior_table_oi,
               colors_sender = celltype_colors, colors_receiver = celltype_colors)

circos_plot

Furthermore, we provide the function make_mushroom_plot which allows you to display expression of ligand-receptor pairs in a specific receiver. By default, the fill gradient shows the LFC between cell types, while the size of the semicircle corresponds to the scaled mean expression. You can also choose to show the rankings of each ligand-receptor-sender pair with show_rankings, as well as show all data points for context (show_all_datapoints). true_color_range = TRUE will adjust the limits of the color gradient to the min-max of the values, instead of the limit being from 0 to 1. Note that the numbers displayed here are the rankings within the chosen cell type and not across all receiver cell types (in case of multiple receivers).

receiver_oi <- "CD8 T"
legend_adjust <- c(0.7, 0.7)
make_mushroom_plot(prior_table_combined %>% filter(receiver == receiver_oi),
                   top_n = 30, 
                   true_color_range = TRUE,
                   show_rankings = TRUE,
                   show_all_datapoints = TRUE) +
  theme(legend.justification = legend_adjust,
        axis.title.x = element_text(hjust = 0.25))

Furthermore, you can change the "size" and "fill" values to certain columns from the prioritization table (those with the _ligand or _receptor suffix).

print(paste0("Column names that you can use are: ", paste0(prior_table %>% select(ends_with(c("_ligand", "_receptor", "_sender", "_receiver"))) %>% colnames() %>%
  str_remove("_ligand|_receptor|_sender|_receiver") %>% unique, collapse = ", ")))

# Change size and color columns
make_mushroom_plot(prior_table, top_n = 30, size = "pct_expressed", color = "scaled_avg_exprs") +
  theme(legend.justification = legend_adjust,
        axis.title.x = element_text(hjust = 0.25))

sessionInfo()

References

saeyslab/nichenetr documentation built on April 27, 2024, 9:24 p.m.