Robin Browaeys 20212-01-12
This vignette guides you in detail through all the steps of a Differential NicheNet analysis. As example expression data of interacting cells, we will here use subset of the liver scRNAseq data generated in the paper from Guilliams et al: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. We took a subset of the data (deposited on https://zenodo.org/deposit/5840787) for demonstration purposes because of the large size of the entire dataset. For exploration and downloading of all the data from the paper, we refer to: Liver Atlas Data Portal. For the code used for all the Differential NicheNet analyses on the entire liver cell atlas dataset, see https://github.com/saeyslab/NicheNet_LiverCellAtlas.
The goal of Differential NicheNet is to predict ligand-receptors pairs that are both differentially expressed and active between different niches of interest.
In this vignette, we will look at cell-cell communication differences between the Kupffer cell niche, the bile duct macrophage niche, and the capsule macrophage niche, with the macrophages in each niche as receiver cell of interest. This means that we are interested in identifying the niche-specific ligands important for the identity of each of these macrophage subtypes.
0. Read in the expression data of interest, and the NicheNet ligand-receptor network and ligand-target matrix
library(nichenetr)
library(RColorBrewer)
library(tidyverse)
library(Seurat) seurat_obj = readRDS(url("https://zenodo.org/record/5840787/files/seurat_obj_subset_integrated_zonation.rds"))
DimPlot(seurat_obj, group.by = "celltype", label = TRUE) 
seurat_obj = SetIdent(seurat_obj, value = "celltype")As you can see, the LSECs, hepatocytes and Stellate cells are each divided in two groups, based on their spatial location (periportal and pericentral).
The used ligand-receptor network and ligand-target matrix can be
downloaded from Zenodo
ligand_target_matrix = readRDS(url("https://zenodo.org/record/7074291/files/ligand_target_matrix_nsga2r_final_mouse.rds"))
ligand_target_matrix[1:5,1:5] # target genes in rows, ligands in columns
## 2300002M23Rik 2610528A11Rik 9530003J23Rik a A2m
## 0610005C13Rik 0.000000e+00 0.000000e+00 1.311297e-05 0.000000e+00 1.390053e-05
## 0610009B22Rik 0.000000e+00 0.000000e+00 1.269301e-05 0.000000e+00 1.345536e-05
## 0610009L18Rik 8.872902e-05 4.977197e-05 2.581909e-04 7.570125e-05 9.802264e-05
## 0610010F05Rik 2.194046e-03 1.111556e-03 3.142374e-03 1.631658e-03 2.585820e-03
## 0610010K14Rik 2.271606e-03 9.360769e-04 3.546140e-03 1.697713e-03 2.632082e-03lr_network = readRDS(url("https://zenodo.org/record/7074291/files/lr_network_mouse_21122021.rds"))
lr_network = lr_network %>% dplyr::rename(ligand = from, receptor = to) %>% distinct(ligand, receptor)
head(lr_network)
## # A tibble: 6 × 2
## ligand receptor
## <chr> <chr>
## 1 2300002M23Rik Ddr1
## 2 2610528A11Rik Gpr15
## 3 9530003J23Rik Itgal
## 4 a Atrn
## 5 a F11r
## 6 a Mc1rEach niche should have at least one “sender/niche” cell population and one “receiver/target” cell population (present in your expression data)
In this case study, we are interested to find differences in cell-cell interactions to hepatic macrophages in three different niches: 1) the Kupffer cell niche, 2) the bile-duct or lipid-associated macrophage niche, and 3) the capsule macrophage niche.
Based on imaging and spatial transcriptomics, the composition of each niche was defined as follows:
The receiver cell population in the Kupffer cell niche is the “KCs” cell type, the sender cell types are: “LSECs_portal”,“Hepatocytes_portal”, and “Stellate cells_portal”. The receiver cell population in the lipid-associated macrophage (MoMac2) niche is the “MoMac2” cell type, the sender cell types are: “Cholangiocytes”, and “Fibroblast 2”. The receiver cell population in the capsule macrophage (MoMac1) niche is the “MoMac1” cell type, the sender cell types are: “Capsule fibroblasts”, and “Mesothelial cells”.
! Important: your receiver cell type should consist of 1 cluster!
niches = list(
"KC_niche" = list(
"sender" = c("LSECs_portal","Hepatocytes_portal","Stellate cells_portal"),
"receiver" = c("KCs")),
"MoMac2_niche" = list(
"sender" = c("Cholangiocytes","Fibroblast 2"),
"receiver" = c("MoMac2")),
"MoMac1_niche" = list(
"sender" = c("Capsule fibroblasts","Mesothelial cells"),
"receiver" = c("MoMac1"))
)In this step, we will determine DE between the different niches for both senders and receivers to define the DE of L-R pairs.
The method to calculate the differential expression is here the standard
Seurat Wilcoxon test, but this can be replaced if wanted by the user
(only requirement: output tables DE_sender_processed and
DE_receiver_processed should be in the same format as shown here).
DE will be calculated for each pairwise sender (or receiver) cell type comparision between the niches (so across niches, not within niche). In our case study, this means e.g. that DE of LSECs_portal ligands will be calculated by DE analysis of LSECs_portal vs Cholangiocytes; LSECs_portal vs Fibroblast 2; LSECs_portal vs Capsule fibroblasts; and LSECs_portal vs Mesothelial cells. We split the cells per cell type instead of merging all cells from the other niche to avoid that the DE analysis will be driven by the most abundant cell types.
assay_oi = "SCT" # other possibilities: RNA,...
# If you use convert_to_alias before here, this one won't work
seurat_obj = Seurat::PrepSCTFindMarkers(seurat_obj, assay = "SCT", verbose = FALSE)
seurat_obj = alias_to_symbol_seurat(seurat_obj, organism = "mouse")
DE_sender = calculate_niche_de(seurat_obj = seurat_obj %>% subset(features = lr_network$ligand %>% intersect(rownames(seurat_obj))), niches = niches, type = "sender", assay_oi = assay_oi) # only ligands important for sender cell types
## [1] "Calculate Sender DE between: LSECs_portal and Cholangiocytes" "Calculate Sender DE between: LSECs_portal and Fibroblast 2" "Calculate Sender DE between: LSECs_portal and Capsule fibroblasts"
## [4] "Calculate Sender DE between: LSECs_portal and Mesothelial cells"
## [1] "Calculate Sender DE between: Hepatocytes_portal and Cholangiocytes" "Calculate Sender DE between: Hepatocytes_portal and Fibroblast 2"
## [3] "Calculate Sender DE between: Hepatocytes_portal and Capsule fibroblasts" "Calculate Sender DE between: Hepatocytes_portal and Mesothelial cells"
## [1] "Calculate Sender DE between: Stellate cells_portal and Cholangiocytes" "Calculate Sender DE between: Stellate cells_portal and Fibroblast 2"
## [3] "Calculate Sender DE between: Stellate cells_portal and Capsule fibroblasts" "Calculate Sender DE between: Stellate cells_portal and Mesothelial cells"
## [1] "Calculate Sender DE between: Cholangiocytes and LSECs_portal" "Calculate Sender DE between: Cholangiocytes and Hepatocytes_portal"
## [3] "Calculate Sender DE between: Cholangiocytes and Stellate cells_portal" "Calculate Sender DE between: Cholangiocytes and Capsule fibroblasts"
## [5] "Calculate Sender DE between: Cholangiocytes and Mesothelial cells"
## [1] "Calculate Sender DE between: Fibroblast 2 and LSECs_portal" "Calculate Sender DE between: Fibroblast 2 and Hepatocytes_portal" "Calculate Sender DE between: Fibroblast 2 and Stellate cells_portal"
## [4] "Calculate Sender DE between: Fibroblast 2 and Capsule fibroblasts" "Calculate Sender DE between: Fibroblast 2 and Mesothelial cells"
## [1] "Calculate Sender DE between: Capsule fibroblasts and LSECs_portal" "Calculate Sender DE between: Capsule fibroblasts and Hepatocytes_portal"
## [3] "Calculate Sender DE between: Capsule fibroblasts and Stellate cells_portal" "Calculate Sender DE between: Capsule fibroblasts and Cholangiocytes"
## [5] "Calculate Sender DE between: Capsule fibroblasts and Fibroblast 2"
## [1] "Calculate Sender DE between: Mesothelial cells and LSECs_portal" "Calculate Sender DE between: Mesothelial cells and Hepatocytes_portal"
## [3] "Calculate Sender DE between: Mesothelial cells and Stellate cells_portal" "Calculate Sender DE between: Mesothelial cells and Cholangiocytes"
## [5] "Calculate Sender DE between: Mesothelial cells and Fibroblast 2"
DE_receiver = calculate_niche_de(seurat_obj = seurat_obj %>% subset(features = lr_network$receptor %>% unique()), niches = niches, type = "receiver", assay_oi = assay_oi) # only receptors now, later on: DE analysis to find targets
## # A tibble: 3 × 2
## receiver receiver_other_niche
## <chr> <chr>
## 1 KCs MoMac2
## 2 KCs MoMac1
## 3 MoMac2 MoMac1
## [1] "Calculate receiver DE between: KCs and MoMac2" "Calculate receiver DE between: KCs and MoMac1"
## [1] "Calculate receiver DE between: MoMac2 and KCs" "Calculate receiver DE between: MoMac2 and MoMac1"
## [1] "Calculate receiver DE between: MoMac1 and KCs" "Calculate receiver DE between: MoMac1 and MoMac2"
DE_sender = DE_sender %>% mutate(avg_log2FC = ifelse(avg_log2FC == Inf, max(avg_log2FC[is.finite(avg_log2FC)]), ifelse(avg_log2FC == -Inf, min(avg_log2FC[is.finite(avg_log2FC)]), avg_log2FC)))
DE_receiver = DE_receiver %>% mutate(avg_log2FC = ifelse(avg_log2FC == Inf, max(avg_log2FC[is.finite(avg_log2FC)]), ifelse(avg_log2FC == -Inf, min(avg_log2FC[is.finite(avg_log2FC)]), avg_log2FC)))expression_pct = 0.10
DE_sender_processed = process_niche_de(DE_table = DE_sender, niches = niches, expression_pct = expression_pct, type = "sender")
DE_receiver_processed = process_niche_de(DE_table = DE_receiver, niches = niches, expression_pct = expression_pct, type = "receiver")As mentioned above, DE of ligands from one sender cell type is determined be calculating DE between that cell type, and all the sender cell types of the other niche. To summarize the DE of ligands of that cell type we have several options: we could take the average LFC, but also the minimum LFC compared to the other niche. We recommend using the minimum LFC, because this is the strongest specificity measure of ligand expression, because a high min LFC means that a ligand is more strongly expressed in the cell type of niche 1 compared to all cell types of niche 2 (in contrast to a high average LFC, which does not exclude that one or more cell types in niche 2 also strongly express that ligand).
specificity_score_LR_pairs = "min_lfc"
DE_sender_receiver = combine_sender_receiver_de(DE_sender_processed, DE_receiver_processed, lr_network, specificity_score = specificity_score_LR_pairs)To improve the cell-cell interaction predictions, you can consider spatial information if possible and applicable. Spatial information can come from microscopy data, or from spatial transcriptomics data such as Visium.
There are several ways to incorporate spatial information in the Differential NicheNet pipeline. First, you can only consider cell types as belonging to the same niche if they are in the same spatial location. Another way is including spatial differential expression of ligand-receptor pairs within one cell type in the prioritization framework.
For example: We have a cell type X, located in regions A and B, and we want to study cell-cell communication in region A. We first add only celltypeX of regionA in the niche definition, and then calculate DE between celltypeX-regionA and celltypeX-regionB to give higher prioritization weight to regionA-specific ligands.
In this case study, our region of interest is the periportal region of the liver, because KCs in mouse are predominantly located in the periportal region. Therefore we will give higher weight to ligands that are in the niche cells of KCs higher expressed in the periportal compared to the pericentral region.
We do this as follows, by first defining a ‘spatial info’ dataframe. If there is no spatial information in your data: set the following two parameters to FALSE, and make a mock ‘spatial_info’ data frame.
include_spatial_info_sender = TRUE # if not spatial info to include: put this to false
include_spatial_info_receiver = FALSE # if spatial info to include: put this to true spatial_info = tibble(celltype_region_oi = c("LSECs_portal","Hepatocytes_portal","Stellate cells_portal"),
celltype_other_region = c("LSECs_central","Hepatocytes_central","Stellate cells_central")
) %>%
mutate(niche = "KC_niche", celltype_type = "sender")
specificity_score_spatial = "lfc"# this is how this should be defined if you don't have spatial info
# mock spatial info
if(include_spatial_info_sender == FALSE & include_spatial_info_receiver == FALSE){
spatial_info = tibble(celltype_region_oi = NA, celltype_other_region = NA) %>% mutate(niche = niches %>% names() %>% head(1), celltype_type = "sender")
} if(include_spatial_info_sender == TRUE){
sender_spatial_DE = calculate_spatial_DE(seurat_obj = seurat_obj %>% subset(features = lr_network$ligand %>% unique()), spatial_info = spatial_info %>% filter(celltype_type == "sender"), assay_oi = assay_oi)
sender_spatial_DE_processed = process_spatial_de(DE_table = sender_spatial_DE, type = "sender", lr_network = lr_network, expression_pct = expression_pct, specificity_score = specificity_score_spatial)
# add a neutral spatial score for sender celltypes in which the spatial is not known / not of importance
sender_spatial_DE_others = get_non_spatial_de(niches = niches, spatial_info = spatial_info, type = "sender", lr_network = lr_network)
sender_spatial_DE_processed = sender_spatial_DE_processed %>% bind_rows(sender_spatial_DE_others)
sender_spatial_DE_processed = sender_spatial_DE_processed %>% mutate(scaled_ligand_score_spatial = scale_quantile_adapted(ligand_score_spatial))
} else {
# # add a neutral spatial score for all sender celltypes (for none of them, spatial is relevant in this case)
sender_spatial_DE_processed = get_non_spatial_de(niches = niches, spatial_info = spatial_info, type = "sender", lr_network = lr_network)
sender_spatial_DE_processed = sender_spatial_DE_processed %>% mutate(scaled_ligand_score_spatial = scale_quantile_adapted(ligand_score_spatial))
}
## [1] "Calculate Spatial DE between: LSECs_portal and LSECs_central"
## [1] "Calculate Spatial DE between: Hepatocytes_portal and Hepatocytes_central"
## [1] "Calculate Spatial DE between: Stellate cells_portal and Stellate cells_central"if(include_spatial_info_receiver == TRUE){
receiver_spatial_DE = calculate_spatial_DE(seurat_obj = seurat_obj %>% subset(features = lr_network$receptor %>% unique()), spatial_info = spatial_info %>% filter(celltype_type == "receiver"), assay_oi = assay_oi)
receiver_spatial_DE_processed = process_spatial_de(DE_table = receiver_spatial_DE, type = "receiver", lr_network = lr_network, expression_pct = expression_pct, specificity_score = specificity_score_spatial)
# add a neutral spatial score for receiver celltypes in which the spatial is not known / not of importance
receiver_spatial_DE_others = get_non_spatial_de(niches = niches, spatial_info = spatial_info, type = "receiver", lr_network = lr_network)
receiver_spatial_DE_processed = receiver_spatial_DE_processed %>% bind_rows(receiver_spatial_DE_others)
receiver_spatial_DE_processed = receiver_spatial_DE_processed %>% mutate(scaled_receptor_score_spatial = scale_quantile_adapted(receptor_score_spatial))
} else {
# # add a neutral spatial score for all receiver celltypes (for none of them, spatial is relevant in this case)
receiver_spatial_DE_processed = get_non_spatial_de(niches = niches, spatial_info = spatial_info, type = "receiver", lr_network = lr_network)
receiver_spatial_DE_processed = receiver_spatial_DE_processed %>% mutate(scaled_receptor_score_spatial = scale_quantile_adapted(receptor_score_spatial))
}In this step, we will predict ligand activities of each ligand for each of the receiver cell types across the different niches. This is similar to the ligand activity analysis done in the normal NicheNet pipeline.
To calculate ligand activities, we first need to define a geneset of interest for each niche. In this case study, the geneset of interest for the Kupffer cell niche are the genes upregulated in Kupffer cells compared to the capsule and bile duct macrophages. The geneset of interest for the bile duct macrophage niche are the genes upregulated in bile duct macrophages compared to the capsule macrophages and Kupffer cells. And similarly for the capsule macrophage geneset of interest.
Note that you can also define these geneset of interest in a different way! (eg pathway-based geneset etc)
Ligand-target links are inferred in the same way as described in the basic NicheNet vignettes.
lfc_cutoff = 0.15 # recommended for 10x as min_lfc cutoff.
specificity_score_targets = "min_lfc"
DE_receiver_targets = calculate_niche_de_targets(seurat_obj = seurat_obj, niches = niches, lfc_cutoff = lfc_cutoff, expression_pct = expression_pct, assay_oi = assay_oi)
## [1] "Calculate receiver DE between: KCs and MoMac2" "Calculate receiver DE between: KCs and MoMac1"
## [1] "Calculate receiver DE between: MoMac2 and KCs" "Calculate receiver DE between: MoMac2 and MoMac1"
## [1] "Calculate receiver DE between: MoMac1 and KCs" "Calculate receiver DE between: MoMac1 and MoMac2"
DE_receiver_processed_targets = process_receiver_target_de(DE_receiver_targets = DE_receiver_targets, niches = niches, expression_pct = expression_pct, specificity_score = specificity_score_targets)
background = DE_receiver_processed_targets %>% pull(target) %>% unique()
geneset_KC = DE_receiver_processed_targets %>% filter(receiver == niches$KC_niche$receiver & target_score >= lfc_cutoff & target_significant == 1 & target_present == 1) %>% pull(target) %>% unique()
geneset_MoMac2 = DE_receiver_processed_targets %>% filter(receiver == niches$MoMac2_niche$receiver & target_score >= lfc_cutoff & target_significant == 1 & target_present == 1) %>% pull(target) %>% unique()
geneset_MoMac1 = DE_receiver_processed_targets %>% filter(receiver == niches$MoMac1_niche$receiver & target_score >= lfc_cutoff & target_significant == 1 & target_present == 1) %>% pull(target) %>% unique()
# Good idea to check which genes will be left out of the ligand activity analysis (=when not present in the rownames of the ligand-target matrix).
# If many genes are left out, this might point to some issue in the gene naming (eg gene aliases and old gene symbols, bad human-mouse mapping)
geneset_KC %>% setdiff(rownames(ligand_target_matrix))
## [1] "Wfdc17" "AW112010" "2900097C17Rik" "B430306N03Rik" "AC149090.1"
geneset_MoMac2 %>% setdiff(rownames(ligand_target_matrix))
## [1] "Gm21188" "Gm10076" "Rpl41" "Atp5o.1" "H2afy"
geneset_MoMac1 %>% setdiff(rownames(ligand_target_matrix))
## [1] "Gm26522"
length(geneset_KC)
## [1] 443
length(geneset_MoMac2)
## [1] 339
length(geneset_MoMac1)
## [1] 84It is always useful to check the number of genes in the geneset before
doing the ligand activity analysis. We recommend having between 20 and
1000 genes in the geneset of interest, and a background of at least 5000
genes for a proper ligand activity analysis. If you retrieve too many DE
genes, it is recommended to use a higher lfc_cutoff threshold. We
recommend using a cutoff of 0.15 if you have > 2 receiver cells/niches
to compare and use the min_lfc as specificity score. If you have only 2
receivers/niche, we recommend using a higher threshold (such as using
0.25). If you have single-cell data like Smart-seq2 with high sequencing
depth, we recommend to also use higher threshold.
top_n_target = 250
niche_geneset_list = list(
"KC_niche" = list(
"receiver" = "KCs",
"geneset" = geneset_KC,
"background" = background),
"MoMac1_niche" = list(
"receiver" = "MoMac1",
"geneset" = geneset_MoMac1 ,
"background" = background),
"MoMac2_niche" = list(
"receiver" = "MoMac2",
"geneset" = geneset_MoMac2 ,
"background" = background)
)
ligand_activities_targets = get_ligand_activities_targets(niche_geneset_list = niche_geneset_list, ligand_target_matrix = ligand_target_matrix, top_n_target = top_n_target)
## [1] "Calculate Ligand activities for: KCs"
## [1] "Calculate Ligand activities for: MoMac1"
## [1] "Calculate Ligand activities for: MoMac2"5. Calculate (scaled) expression of ligands, receptors and targets across cell types of interest (log expression values and expression fractions)
In this step, we will calculate average (scaled) expression, and fraction of expression, of ligands, receptors, and target genes across all cell types of interest. Now this is here demonstrated via the DotPlot function of Seurat, but this can also be done via other ways of course.
features_oi = union(lr_network$ligand, lr_network$receptor) %>% union(ligand_activities_targets$target) %>% setdiff(NA)
dotplot = suppressWarnings(Seurat::DotPlot(seurat_obj %>% subset(idents = niches %>% unlist() %>% unique()), features = features_oi, assay = assay_oi))
exprs_tbl = dotplot$data %>% as_tibble()
exprs_tbl = exprs_tbl %>% rename(celltype = id, gene = features.plot, expression = avg.exp, expression_scaled = avg.exp.scaled, fraction = pct.exp) %>%
mutate(fraction = fraction/100) %>% as_tibble() %>% select(celltype, gene, expression, expression_scaled, fraction) %>% distinct() %>% arrange(gene) %>% mutate(gene = as.character(gene))
exprs_tbl_ligand = exprs_tbl %>% filter(gene %in% lr_network$ligand) %>% rename(sender = celltype, ligand = gene, ligand_expression = expression, ligand_expression_scaled = expression_scaled, ligand_fraction = fraction)
exprs_tbl_receptor = exprs_tbl %>% filter(gene %in% lr_network$receptor) %>% rename(receiver = celltype, receptor = gene, receptor_expression = expression, receptor_expression_scaled = expression_scaled, receptor_fraction = fraction)
exprs_tbl_target = exprs_tbl %>% filter(gene %in% ligand_activities_targets$target) %>% rename(receiver = celltype, target = gene, target_expression = expression, target_expression_scaled = expression_scaled, target_fraction = fraction)exprs_tbl_ligand = exprs_tbl_ligand %>% mutate(scaled_ligand_expression_scaled = scale_quantile_adapted(ligand_expression_scaled)) %>% mutate(ligand_fraction_adapted = ligand_fraction) %>% mutate_cond(ligand_fraction >= expression_pct, ligand_fraction_adapted = expression_pct) %>% mutate(scaled_ligand_fraction_adapted = scale_quantile_adapted(ligand_fraction_adapted))
exprs_tbl_receptor = exprs_tbl_receptor %>% mutate(scaled_receptor_expression_scaled = scale_quantile_adapted(receptor_expression_scaled)) %>% mutate(receptor_fraction_adapted = receptor_fraction) %>% mutate_cond(receptor_fraction >= expression_pct, receptor_fraction_adapted = expression_pct) %>% mutate(scaled_receptor_fraction_adapted = scale_quantile_adapted(receptor_fraction_adapted))In this step, we will score ligand-receptor interactions based on expression strength of the receptor, in such a way that we give higher scores to the most strongly expressed receptor of a certain ligand, in a certain celltype. This will not effect the rank of individual ligands later on, but will help in prioritizing the most important receptors per ligand (next to other factors regarding the receptor - see later).
exprs_sender_receiver = lr_network %>%
inner_join(exprs_tbl_ligand, by = c("ligand")) %>%
inner_join(exprs_tbl_receptor, by = c("receptor")) %>% inner_join(DE_sender_receiver %>% distinct(niche, sender, receiver))
ligand_scaled_receptor_expression_fraction_df = exprs_sender_receiver %>% group_by(ligand, receiver) %>% mutate(rank_receptor_expression = dense_rank(receptor_expression), rank_receptor_fraction = dense_rank(receptor_fraction)) %>% mutate(ligand_scaled_receptor_expression_fraction = 0.5*( (rank_receptor_fraction / max(rank_receptor_fraction)) + ((rank_receptor_expression / max(rank_receptor_expression))) ) ) %>% distinct(ligand, receptor, receiver, ligand_scaled_receptor_expression_fraction) %>% distinct() %>% ungroup() In this step, we will combine all the above calculated information to prioritize ligand-receptor-target links. We scale every property of interest between 0 and 1, and the final prioritization score is a weighted sum of the scaled scores of all the properties of interest.
We provide the user the option to consider the following properties for
prioritization (of which the weights are defined in
prioritizing_weights) :
-
Ligand DE score: niche-specific expression of the ligand: by default, this the minimum logFC between the sender of interest and all the senders of the other niche(s). The higher the min logFC, the higher the niche-specificity of the ligand. Therefore we recommend to give this factor a very high weight.
prioritizing_weightsargument:"scaled_ligand_score". Recommended weight: 5 (at least 1, max 5). -
Scaled ligand expression: scaled expression of a ligand in one sender compared to the other cell types in the dataset. This might be useful to rescue potentially interesting ligands that have a high scaled expression value, but a relatively small min logFC compared to the other niche. One reason why this logFC might be small occurs when (some) genes are not picked up efficiently by the used sequencing technology (or other reasons for low RNA expression of ligands). For example, we have observed that many ligands from the Tgf-beta/BMP family are not picked up efficiently with single-nuclei RNA sequencing compared to single-cell sequencing.
prioritizing_weightsargument:"scaled_ligand_expression_scaled". Recommended weight: 1 (unless technical reason for lower gene detection such as while using Nuc-seq: then recommended to use a higher weight: 2). -
Ligand expression fraction: Ligands that are expressed in a smaller fraction of cells of a cell type than defined by
exprs_cutoff(default: 0.10) will get a lower ranking, proportional to their fraction (eg ligand expressed in 9% of cells will be ranked higher than ligand expressed in 0.5% of cells). We opted for this weighting based on fraction, instead of removing ligands that are not expressed in more cells than this cutoff, because some interesting ligands could be removed that way. Fraction of expression is not taken into account for the prioritization if it is already higher than the cutoff.prioritizing_weightsargument:"ligand_fraction". Recommended weight: 1. -
Ligand spatial DE score: spatial expression specificity of the ligand. If the niche of interest is at a specific tissue location, but some of the sender cell types of that niche are also present in other locations, it can be very informative to further prioritize ligands of that sender by looking how they are DE between the spatial location of interest compared to the other locations.
prioritizing_weightsargument:"scaled_ligand_score_spatial". Recommended weight: 2 (or 0 if not applicable). -
Receptor DE score: niche-specific expression of the receptor: by default, this the minimum logFC between the receiver of interest and all the receiver of the other niche(s). The higher the min logFC, the higher the niche-specificity of the receptor. Based on our experience, we don’t suggest to give this as high importance as the ligand DE, but this might depend on the specific case study.
prioritizing_weightsargument:"scaled_receptor_score". Recommended weight: 0.5 (at least 0.5, and lower than"scaled_ligand_score"). -
Scaled receptor expression: scaled expression of a receptor in one receiver compared to the other cell types in the dataset. This might be useful to rescue potentially interesting receptors that have a high scaled expression value, but a relatively small min logFC compared to the other niche. One reason why this logFC might be small occurs when (some) genes are not picked up efficiently by the used sequencing technology.
prioritizing_weightsargument:"scaled_receptor_expression_scaled". Recommended weight: 0.5. -
Receptor expression fraction: Receptors that are expressed in a smaller fraction of cells of a cell type than defined by
exprs_cutoff(default: 0.10) will get a lower ranking, proportional to their fraction (eg receptor expressed in 9% of cells will be ranked higher than receptor expressed in 0.5% of cells). We opted for this weighting based on fraction, instead of removing receptors that are not expressed in more cells than this cutoff, because some interesting receptors could be removed that way. Fraction of expression is not taken into account for the prioritization if it is already higher than the cutoff.prioritizing_weightsargument:"receptor_fraction". Recommended weight: 1. -
Receptor expression strength: this factor let us give higher weights to the most highly expressed receptor of a ligand in the receiver. This let us rank higher one member of a receptor family if it higher expressed than the other members.
prioritizing_weightsargument:"ligand_scaled_receptor_expression_fraction". Recommended value: 1 (minimum: 0.5). -
Receptor spatial DE score: spatial expression specificity of the receptor. If the niche of interest is at a specific tissue location, but the receiver cell type of that niche is also present in other locations, it can be very informative to further prioritize receptors of that receiver by looking how they are DE between the spatial location of interest compared to the other locations.
prioritizing_weightsargument:"scaled_receptor_score_spatial". Recommended weight: 1 (or 0 if not applicable). -
Absolute ligand activity: to further prioritize ligand-receptor pairs based on their predicted effect of the ligand-receptor interaction on the gene expression in the receiver cell type - absolute ligand activity accords to ‘absolute’ enrichment of target genes of a ligand within the affected receiver genes.
prioritizing_weightsargument:"scaled_activity". Recommended weight: 0, unless absolute enrichment of target genes is of specific interest. -
Normalized ligand activity: to further prioritize ligand-receptor pairs based on their predicted effect of the ligand-receptor interaction on the gene expression in the receiver cell type - normalization of activity is done because we found that some datasets/conditions/niches have higher baseline activity values than others - normalized ligand activity accords to ‘relative’ enrichment of target genes of a ligand within the affected receiver genes.
prioritizing_weightsargument:"scaled_activity_normalized". Recommended weight: at least 1.
prioritizing_weights = c("scaled_ligand_score" = 5,
"scaled_ligand_expression_scaled" = 1,
"ligand_fraction" = 1,
"scaled_ligand_score_spatial" = 2,
"scaled_receptor_score" = 0.5,
"scaled_receptor_expression_scaled" = 0.5,
"receptor_fraction" = 1,
"ligand_scaled_receptor_expression_fraction" = 1,
"scaled_receptor_score_spatial" = 0,
"scaled_activity" = 0,
"scaled_activity_normalized" = 1)output = list(DE_sender_receiver = DE_sender_receiver, ligand_scaled_receptor_expression_fraction_df = ligand_scaled_receptor_expression_fraction_df, sender_spatial_DE_processed = sender_spatial_DE_processed, receiver_spatial_DE_processed = receiver_spatial_DE_processed,
ligand_activities_targets = ligand_activities_targets, DE_receiver_processed_targets = DE_receiver_processed_targets, exprs_tbl_ligand = exprs_tbl_ligand, exprs_tbl_receptor = exprs_tbl_receptor, exprs_tbl_target = exprs_tbl_target)
prioritization_tables = get_prioritization_tables(output, prioritizing_weights)
prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(receiver == niches[[1]]$receiver) %>% head(10)
## # A tibble: 10 × 36
## niche receiver sender ligand_receptor ligand receptor ligand_score ligand_signific… ligand_present ligand_expressi… ligand_expressi… ligand_fraction ligand_score_sp… receptor_score receptor_signif… receptor_present
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 KC_n… KCs Hepat… Apoc3--Lrp1 Apoc3 Lrp1 3.33 1 1 14.8 2.5 0.733 1.20 0.110 0.5 1
## 2 KC_n… KCs Hepat… Apoa2--Lrp1 Apoa2 Lrp1 4.07 1 1 32.7 2.5 0.781 0.482 0.110 0.5 1
## 3 KC_n… KCs Hepat… Apoa1--Lrp1 Apoa1 Lrp1 3.18 1 1 14.7 2.5 0.714 1.03 0.110 0.5 1
## 4 KC_n… KCs Hepat… Serpina1e--Lrp1 Serpi… Lrp1 3.63 1 1 18.4 2.5 0.695 0.609 0.110 0.5 1
## 5 KC_n… KCs Hepat… Apoc3--Tlr2 Apoc3 Tlr2 3.33 1 1 14.8 2.5 0.733 1.20 -0.173 0.5 1
## 6 KC_n… KCs Hepat… Apoa1--Abca1 Apoa1 Abca1 3.18 1 1 14.7 2.5 0.714 1.03 0.197 1 1
## 7 KC_n… KCs Hepat… Hpx--Lrp1 Hpx Lrp1 1.87 1 1 3.44 2.5 0.455 1.23 0.110 0.5 1
## 8 KC_n… KCs Hepat… Serpina1b--Lrp1 Serpi… Lrp1 2.70 1 1 8.13 2.5 0.686 0.560 0.110 0.5 1
## 9 KC_n… KCs Hepat… Fgb--Cdh5 Fgb Cdh5 1.98 1 1 3.62 2.5 0.560 0.913 1.39 1 1
## 10 KC_n… KCs Stell… Ntm--Cd79b Ntm Cd79b 2.65 1 1 8.73 2.5 0.875 0.752 -0.192 0.5 1
## # … with 20 more variables: receptor_expression <dbl>, receptor_expression_scaled <dbl>, receptor_fraction <dbl>, receptor_score_spatial <dbl>, ligand_scaled_receptor_expression_fraction <dbl>,
## # avg_score_ligand_receptor <dbl>, activity <dbl>, activity_normalized <dbl>, scaled_ligand_score <dbl>, scaled_ligand_expression_scaled <dbl>, scaled_receptor_score <dbl>, scaled_receptor_expression_scaled <dbl>,
## # scaled_avg_score_ligand_receptor <dbl>, scaled_ligand_score_spatial <dbl>, scaled_receptor_score_spatial <dbl>, scaled_ligand_fraction_adapted <dbl>, scaled_receptor_fraction_adapted <dbl>, scaled_activity <dbl>,
## # scaled_activity_normalized <dbl>, prioritization_score <dbl>
prioritization_tables$prioritization_tbl_ligand_target %>% filter(receiver == niches[[1]]$receiver) %>% head(10)
## # A tibble: 10 × 19
## niche receiver sender ligand_receptor ligand receptor target target_score target_signific… target_present target_expressi… target_expressi… target_fraction ligand_target_w… activity activity_normal… scaled_activity
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 KC_n… KCs Hepat… Apoc3--Lrp1 Apoc3 Lrp1 Abca1 0.197 1 1 0.979 2.02 0.571 0.00773 0.0143 -0.201 0.0304
## 2 KC_n… KCs Hepat… Apoc3--Lrp1 Apoc3 Lrp1 Hmox1 1.16 1 1 5.23 2.5 0.790 0.00922 0.0143 -0.201 0.0304
## 3 KC_n… KCs Hepat… Apoc3--Lrp1 Apoc3 Lrp1 Il1a 0.152 1 1 0.188 0.279 0.146 0.00814 0.0143 -0.201 0.0304
## 4 KC_n… KCs Hepat… Apoc3--Lrp1 Apoc3 Lrp1 Pten 0.378 1 1 0.719 0.982 0.486 0.00704 0.0143 -0.201 0.0304
## 5 KC_n… KCs Hepat… Apoc3--Lrp1 Apoc3 Lrp1 Sgk1 0.265 1 1 0.629 0.226 0.381 0.00752 0.0143 -0.201 0.0304
## 6 KC_n… KCs Hepat… Apoc3--Lrp1 Apoc3 Lrp1 Stat1 0.273 1 1 1.06 2.02 0.575 0.00684 0.0143 -0.201 0.0304
## 7 KC_n… KCs Hepat… Apoc3--Lrp1 Apoc3 Lrp1 Tcf7l2 0.811 1 1 1.32 1.32 0.656 0.00749 0.0143 -0.201 0.0304
## 8 KC_n… KCs Hepat… Apoc3--Lrp1 Apoc3 Lrp1 Txnip 0.342 1 1 1.42 0.751 0.688 0.00780 0.0143 -0.201 0.0304
## 9 KC_n… KCs Hepat… Apoc3--Lrp1 Apoc3 Lrp1 Vcam1 0.820 1 1 1.36 2.46 0.570 0.0648 0.0143 -0.201 0.0304
## 10 KC_n… KCs Hepat… Apoa2--Lrp1 Apoa2 Lrp1 Abca1 0.197 1 1 0.979 2.02 0.571 0.00723 0.0162 -0.176 0.0343
## # … with 2 more variables: scaled_activity_normalized <dbl>, prioritization_score <dbl>
prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(receiver == niches[[2]]$receiver) %>% head(10)
## # A tibble: 10 × 36
## niche receiver sender ligand_receptor ligand receptor ligand_score ligand_signific… ligand_present ligand_expressi… ligand_expressi… ligand_fraction ligand_score_sp… receptor_score receptor_signif… receptor_present
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 MoMa… MoMac2 Chola… Spp1--Cd44 Spp1 Cd44 6.09 1 1 72.4 2.5 0.943 0 -0.516 0.5 1
## 2 MoMa… MoMac2 Chola… Spp1--Itga4 Spp1 Itga4 6.09 1 1 72.4 2.5 0.943 0 0.168 0.5 1
## 3 MoMa… MoMac2 Chola… Spp1--Itgb5 Spp1 Itgb5 6.09 1 1 72.4 2.5 0.943 0 -0.0856 0 1
## 4 MoMa… MoMac2 Chola… Spp1--Itgav Spp1 Itgav 6.09 1 1 72.4 2.5 0.943 0 -0.105 0 1
## 5 MoMa… MoMac2 Chola… Spp1--Itgb1 Spp1 Itgb1 6.09 1 1 72.4 2.5 0.943 0 -0.359 0.5 1
## 6 MoMa… MoMac2 Chola… Clu--Trem2 Clu Trem2 3.79 1 1 52.0 2.5 0.921 0 1.16 1 1
## 7 MoMa… MoMac2 Chola… Spp1--Itga9 Spp1 Itga9 6.09 1 1 72.4 2.5 0.943 0 -0.620 1 0
## 8 MoMa… MoMac2 Chola… Spp1--Itga5 Spp1 Itga5 6.09 1 1 72.4 2.5 0.943 0 -0.0441 0 0
## 9 MoMa… MoMac2 Chola… Spp1--S1pr1 Spp1 S1pr1 6.09 1 1 72.4 2.5 0.943 0 -0.00416 0 0
## 10 MoMa… MoMac2 Fibro… Lama2--Rpsa Lama2 Rpsa 1.51 1 1 3.19 2.5 0.764 0 0.299 1 1
## # … with 20 more variables: receptor_expression <dbl>, receptor_expression_scaled <dbl>, receptor_fraction <dbl>, receptor_score_spatial <dbl>, ligand_scaled_receptor_expression_fraction <dbl>,
## # avg_score_ligand_receptor <dbl>, activity <dbl>, activity_normalized <dbl>, scaled_ligand_score <dbl>, scaled_ligand_expression_scaled <dbl>, scaled_receptor_score <dbl>, scaled_receptor_expression_scaled <dbl>,
## # scaled_avg_score_ligand_receptor <dbl>, scaled_ligand_score_spatial <dbl>, scaled_receptor_score_spatial <dbl>, scaled_ligand_fraction_adapted <dbl>, scaled_receptor_fraction_adapted <dbl>, scaled_activity <dbl>,
## # scaled_activity_normalized <dbl>, prioritization_score <dbl>
prioritization_tables$prioritization_tbl_ligand_target %>% filter(receiver == niches[[2]]$receiver) %>% head(10)
## # A tibble: 10 × 19
## niche receiver sender ligand_receptor ligand receptor target target_score target_signific… target_present target_expressi… target_expressi… target_fraction ligand_target_w… activity activity_normal… scaled_activity
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 MoMa… MoMac2 Chola… Spp1--Cd44 Spp1 Cd44 Alox5… 0.382 1 1 2.07 2.17 0.874 0.00921 0.0131 -0.133 0.0281
## 2 MoMa… MoMac2 Chola… Spp1--Cd44 Spp1 Cd44 Bax 0.334 1 1 0.897 2.21 0.556 0.0106 0.0131 -0.133 0.0281
## 3 MoMa… MoMac2 Chola… Spp1--Cd44 Spp1 Cd44 Bcl2l… 0.280 1 1 0.453 2.41 0.381 0.00983 0.0131 -0.133 0.0281
## 4 MoMa… MoMac2 Chola… Spp1--Cd44 Spp1 Cd44 Cdkn1a 0.609 1 1 0.801 2.39 0.336 0.0257 0.0131 -0.133 0.0281
## 5 MoMa… MoMac2 Chola… Spp1--Cd44 Spp1 Cd44 Cxcr4 0.374 1 1 0.717 2.5 0.444 0.0695 0.0131 -0.133 0.0281
## 6 MoMa… MoMac2 Chola… Spp1--Cd44 Spp1 Cd44 Dhrs3 0.371 1 1 0.743 0.777 0.514 0.00927 0.0131 -0.133 0.0281
## 7 MoMa… MoMac2 Chola… Spp1--Cd44 Spp1 Cd44 Emp1 0.398 1 1 0.320 1.40 0.168 0.00909 0.0131 -0.133 0.0281
## 8 MoMa… MoMac2 Chola… Spp1--Cd44 Spp1 Cd44 Fn1 0.360 1 1 0.456 -0.285 0.243 0.0133 0.0131 -0.133 0.0281
## 9 MoMa… MoMac2 Chola… Spp1--Cd44 Spp1 Cd44 Gadd4… 0.180 1 1 0.474 2.13 0.276 0.0134 0.0131 -0.133 0.0281
## 10 MoMa… MoMac2 Chola… Spp1--Cd44 Spp1 Cd44 Gdf15 0.479 1 1 0.521 2.5 0.185 0.0126 0.0131 -0.133 0.0281
## # … with 2 more variables: scaled_activity_normalized <dbl>, prioritization_score <dbl>
prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(receiver == niches[[3]]$receiver) %>% head(10)
## # A tibble: 10 × 36
## niche receiver sender ligand_receptor ligand receptor ligand_score ligand_signific… ligand_present ligand_expressi… ligand_expressi… ligand_fraction ligand_score_sp… receptor_score receptor_signif… receptor_present
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 MoMa… MoMac1 Mesot… C3--C3ar1 C3 C3ar1 3.52 1 1 22.6 1.99 0.737 0 -0.122 0.5 1
## 2 MoMa… MoMac1 Capsu… C3--C3ar1 C3 C3ar1 3.42 1 1 20.9 1.80 0.802 0 -0.122 0.5 1
## 3 MoMa… MoMac1 Capsu… Lgals1--Ptprc Lgals1 Ptprc 2.80 1 1 10.1 2.5 0.616 0 0.267 1 1
## 4 MoMa… MoMac1 Capsu… Slpi--Cd4 Slpi Cd4 4.37 1 1 20.0 1.99 0.494 0 0.0239 0 0
## 5 MoMa… MoMac1 Mesot… C3--Itgb2 C3 Itgb2 3.52 1 1 22.6 1.99 0.737 0 -0.294 1 1
## 6 MoMa… MoMac1 Mesot… Slpi--Cd4 Slpi Cd4 4.26 1 1 18.4 1.80 0.432 0 0.0239 0 0
## 7 MoMa… MoMac1 Mesot… C3--Lrp1 C3 Lrp1 3.52 1 1 22.6 1.99 0.737 0 -0.552 1 1
## 8 MoMa… MoMac1 Mesot… C3--Itgax C3 Itgax 3.52 1 1 22.6 1.99 0.737 0 0.0101 0.5 1
## 9 MoMa… MoMac1 Capsu… C3--Itgb2 C3 Itgb2 3.42 1 1 20.9 1.80 0.802 0 -0.294 1 1
## 10 MoMa… MoMac1 Capsu… C3--Lrp1 C3 Lrp1 3.42 1 1 20.9 1.80 0.802 0 -0.552 1 1
## # … with 20 more variables: receptor_expression <dbl>, receptor_expression_scaled <dbl>, receptor_fraction <dbl>, receptor_score_spatial <dbl>, ligand_scaled_receptor_expression_fraction <dbl>,
## # avg_score_ligand_receptor <dbl>, activity <dbl>, activity_normalized <dbl>, scaled_ligand_score <dbl>, scaled_ligand_expression_scaled <dbl>, scaled_receptor_score <dbl>, scaled_receptor_expression_scaled <dbl>,
## # scaled_avg_score_ligand_receptor <dbl>, scaled_ligand_score_spatial <dbl>, scaled_receptor_score_spatial <dbl>, scaled_ligand_fraction_adapted <dbl>, scaled_receptor_fraction_adapted <dbl>, scaled_activity <dbl>,
## # scaled_activity_normalized <dbl>, prioritization_score <dbl>
prioritization_tables$prioritization_tbl_ligand_target %>% filter(receiver == niches[[3]]$receiver) %>% head(10)
## # A tibble: 10 × 19
## niche receiver sender ligand_receptor ligand receptor target target_score target_signific… target_present target_expressi… target_expressi… target_fraction ligand_target_w… activity activity_normal… scaled_activity
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 MoMa… MoMac1 Mesot… C3--C3ar1 C3 C3ar1 Btg2 0.615 1 1 1.51 1.06 0.542 0.00560 0.0107 -0.0770 0.0232
## 2 MoMa… MoMac1 Mesot… C3--C3ar1 C3 C3ar1 Ccnd2 0.505 1 1 0.490 -0.0500 0.288 0.00652 0.0107 -0.0770 0.0232
## 3 MoMa… MoMac1 Mesot… C3--C3ar1 C3 C3ar1 Cdk6 0.221 1 1 0.320 0.299 0.232 0.00583 0.0107 -0.0770 0.0232
## 4 MoMa… MoMac1 Mesot… C3--C3ar1 C3 C3ar1 H2-D1 0.318 1 1 7.59 1.68 0.990 0.00511 0.0107 -0.0770 0.0232
## 5 MoMa… MoMac1 Mesot… C3--C3ar1 C3 C3ar1 Il1b 0.956 1 1 3.74 2.5 0.632 0.00798 0.0107 -0.0770 0.0232
## 6 MoMa… MoMac1 Mesot… C3--C3ar1 C3 C3ar1 Jun 0.765 1 1 1.93 0.120 0.620 0.00826 0.0107 -0.0770 0.0232
## 7 MoMa… MoMac1 Capsu… C3--C3ar1 C3 C3ar1 Btg2 0.615 1 1 1.51 1.06 0.542 0.00560 0.0107 -0.0770 0.0232
## 8 MoMa… MoMac1 Capsu… C3--C3ar1 C3 C3ar1 Ccnd2 0.505 1 1 0.490 -0.0500 0.288 0.00652 0.0107 -0.0770 0.0232
## 9 MoMa… MoMac1 Capsu… C3--C3ar1 C3 C3ar1 Cdk6 0.221 1 1 0.320 0.299 0.232 0.00583 0.0107 -0.0770 0.0232
## 10 MoMa… MoMac1 Capsu… C3--C3ar1 C3 C3ar1 H2-D1 0.318 1 1 7.59 1.68 0.990 0.00511 0.0107 -0.0770 0.0232
## # … with 2 more variables: scaled_activity_normalized <dbl>, prioritization_score <dbl>
prioritization_tables$prioritization_tbl_ligand_receptor = prioritization_tables$prioritization_tbl_ligand_receptor %>% mutate(receiver = factor(receiver, levels = c("KCs","MoMac1","MoMac2")), niche = factor(niche, levels = c("KC_niche","MoMac1_niche","MoMac2_niche")))
prioritization_tables$prioritization_tbl_ligand_target = prioritization_tables$prioritization_tbl_ligand_target %>% mutate(receiver = factor(receiver, levels = c("KCs","MoMac1","MoMac2")), niche = factor(niche, levels = c("KC_niche","MoMac1_niche","MoMac2_niche"))) Before visualization, we need to define the most important ligand-receptor pairs per niche. We will do this by first determining for which niche the highest score is found for each ligand/ligand-receptor pair. And then getting the top 50 ligands per niche.
top_ligand_niche_df = prioritization_tables$prioritization_tbl_ligand_receptor %>% select(niche, sender, receiver, ligand, receptor, prioritization_score) %>% group_by(ligand) %>% top_n(1, prioritization_score) %>% ungroup() %>% select(ligand, receptor, niche) %>% rename(top_niche = niche)
top_ligand_receptor_niche_df = prioritization_tables$prioritization_tbl_ligand_receptor %>% select(niche, sender, receiver, ligand, receptor, prioritization_score) %>% group_by(ligand, receptor) %>% top_n(1, prioritization_score) %>% ungroup() %>% select(ligand, receptor, niche) %>% rename(top_niche = niche)
ligand_prioritized_tbl_oi = prioritization_tables$prioritization_tbl_ligand_receptor %>% select(niche, sender, receiver, ligand, prioritization_score) %>% group_by(ligand, niche) %>% top_n(1, prioritization_score) %>% ungroup() %>% distinct() %>% inner_join(top_ligand_niche_df) %>% filter(niche == top_niche) %>% group_by(niche) %>% top_n(50, prioritization_score) %>% ungroup() # get the top50 ligands per nicheNow we will look first at the top ligand-receptor pairs for KCs (here, we will take the top 2 scoring receptors per prioritized ligand)
receiver_oi = "KCs"
filtered_ligands = ligand_prioritized_tbl_oi %>% filter(receiver == receiver_oi) %>% pull(ligand) %>% unique()
prioritized_tbl_oi = prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(ligand %in% filtered_ligands) %>% select(niche, sender, receiver, ligand, receptor, ligand_receptor, prioritization_score) %>% distinct() %>% inner_join(top_ligand_receptor_niche_df) %>% group_by(ligand) %>% filter(receiver == receiver_oi) %>% top_n(2, prioritization_score) %>% ungroup() Visualization: minimum LFC compared to other niches
lfc_plot = make_ligand_receptor_lfc_plot(receiver_oi, prioritized_tbl_oi, prioritization_tables$prioritization_tbl_ligand_receptor, plot_legend = FALSE, heights = NULL, widths = NULL)
lfc_plot
Show the spatialDE as additional information
lfc_plot_spatial = make_ligand_receptor_lfc_spatial_plot(receiver_oi, prioritized_tbl_oi, prioritization_tables$prioritization_tbl_ligand_receptor, ligand_spatial = include_spatial_info_sender, receptor_spatial = include_spatial_info_receiver, plot_legend = FALSE, heights = NULL, widths = NULL)
lfc_plot_spatial
From this plot, you can see that some KC-niche ligands like Il34 (by Stellate cells) are higher expressed in the periportal stellate cells vs the pericentral ones. This can be interesting information knowing that KCs are mainly located periportally. However, the fact that other ligands are not preferentially expressed by periportal cell does not mean they cannot be interesting.
Active target gene inference - cf Default NicheNet
Now: visualization of ligand activity and ligand-target links
exprs_activity_target_plot = make_ligand_activity_target_exprs_plot(receiver_oi, prioritized_tbl_oi, prioritization_tables$prioritization_tbl_ligand_receptor, prioritization_tables$prioritization_tbl_ligand_target, output$exprs_tbl_ligand, output$exprs_tbl_target, lfc_cutoff, ligand_target_matrix, plot_legend = FALSE, heights = NULL, widths = NULL)
exprs_activity_target_plot$combined_plot
# exprs_activity_target_plot$legends # for legendsIn this plot, we see that only a few ligands in the ‘scaled ligand
activity’ column has a high expression. This is due to the presence of
some really well-performing outliers which can mask other also potentially interesting ligands. For
the sake of visualization, we will change the color scale to only those
within 1.5*interquartile range of the values (cf. whiskers in the
boxplot) by setting scaled_ligand_activity_limits = "IQR". Any outliers
will be “squished” to the limits.
exprs_activity_target_plot = make_ligand_activity_target_exprs_plot(receiver_oi, prioritized_tbl_oi, prioritization_tables$prioritization_tbl_ligand_receptor,
prioritization_tables$prioritization_tbl_ligand_target, output$exprs_tbl_ligand, output$exprs_tbl_target, lfc_cutoff, ligand_target_matrix,
scaled_ligand_activity_limits = "IQR", plot_legend = FALSE, heights = NULL, widths = NULL)
exprs_activity_target_plot$combined_plot
In this plot, some strongly DE ligand-receptor pairs in the KC niche,
have also high scaled ligand activity on KCs - making them strong
predictions for further validation.
important: ligand-receptor pairs with both high differential expression and ligand activity (=target gene enrichment) are very interesting predictions as key regulators of your intercellular communication process of interest !
If this plot contains too much information because we look at many hits (top 50 ligands), you can make this plot of course for less ligands as well, eg for the top20.
filtered_ligands = ligand_prioritized_tbl_oi %>% filter(receiver == receiver_oi) %>% top_n(20, prioritization_score) %>% pull(ligand) %>% unique()
prioritized_tbl_oi = prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(ligand %in% filtered_ligands) %>% select(niche, sender, receiver, ligand, receptor, ligand_receptor, prioritization_score) %>% distinct() %>% inner_join(top_ligand_receptor_niche_df) %>% group_by(ligand) %>% filter(receiver == receiver_oi) %>% top_n(2, prioritization_score) %>% ungroup()
exprs_activity_target_plot = make_ligand_activity_target_exprs_plot(receiver_oi, prioritized_tbl_oi, prioritization_tables$prioritization_tbl_ligand_receptor, prioritization_tables$prioritization_tbl_ligand_target, output$exprs_tbl_ligand, output$exprs_tbl_target, lfc_cutoff, ligand_target_matrix, scaled_ligand_activity_limits = "IQR", plot_legend = FALSE, heights = NULL, widths = NULL)
exprs_activity_target_plot$combined_plot
Because a top50 is too much to visualize in a circos plot, we will only visualize the top 15.
filtered_ligands = ligand_prioritized_tbl_oi %>% filter(receiver == receiver_oi) %>% top_n(15, prioritization_score) %>% pull(ligand) %>% unique()
prioritized_tbl_oi = prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(ligand %in% filtered_ligands) %>% select(niche, sender, receiver, ligand, receptor, ligand_receptor, prioritization_score) %>% distinct() %>% inner_join(top_ligand_receptor_niche_df) %>% group_by(ligand) %>% filter(receiver == receiver_oi) %>% top_n(2, prioritization_score) %>% ungroup()
colors_sender = brewer.pal(n = prioritized_tbl_oi$sender %>% unique() %>% sort() %>% length(), name = 'Spectral') %>% magrittr::set_names(prioritized_tbl_oi$sender %>% unique() %>% sort())
colors_receiver = c("lavender") %>% magrittr::set_names(prioritized_tbl_oi$receiver %>% unique() %>% sort())
circos_output = make_circos_lr(prioritized_tbl_oi, colors_sender, colors_receiver)
# circos_output$p_circosreceiver_oi = "MoMac1"
filtered_ligands = ligand_prioritized_tbl_oi %>% filter(receiver == receiver_oi) %>% top_n(50, prioritization_score) %>% pull(ligand) %>% unique()
prioritized_tbl_oi = prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(ligand %in% filtered_ligands) %>% select(niche, sender, receiver, ligand, receptor, ligand_receptor, prioritization_score) %>% distinct() %>% inner_join(top_ligand_receptor_niche_df) %>% group_by(ligand) %>% filter(receiver == receiver_oi) %>% top_n(2, prioritization_score) %>% ungroup()
lfc_plot = make_ligand_receptor_lfc_plot(receiver_oi, prioritized_tbl_oi, prioritization_tables$prioritization_tbl_ligand_receptor, plot_legend = FALSE, heights = NULL, widths = NULL)
lfc_plot
receiver_oi = "MoMac2"
filtered_ligands = ligand_prioritized_tbl_oi %>% filter(receiver == receiver_oi) %>% top_n(50, prioritization_score) %>% pull(ligand) %>% unique()
prioritized_tbl_oi = prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(ligand %in% filtered_ligands) %>% select(niche, sender, receiver, ligand, receptor, ligand_receptor, prioritization_score) %>% distinct() %>% inner_join(top_ligand_receptor_niche_df) %>% group_by(ligand) %>% filter(receiver == receiver_oi) %>% top_n(2, prioritization_score) %>% ungroup()
lfc_plot = make_ligand_receptor_lfc_plot(receiver_oi, prioritized_tbl_oi, prioritization_tables$prioritization_tbl_ligand_receptor, plot_legend = FALSE, heights = NULL, widths = NULL)
lfc_plot
In the default NicheNet pipeline, expressed ligand-receptor pairs are prioritized based on their ligand activity alone. Here, in the Differential NicheNet pipeline, we also draw information based on differential expression of the L-R pairs compared to other niches (and if applicable: other spatial locations.)
Because we here focus on differential expression of ligand-receptor pairs, and by using the default prioritizations weights more on DE than activity, we tend to find many different hits than with the default NicheNet pipeline. With Differential NicheNet, we tend to find more high-DE, low-activity hits, whereas with default NicheNet we find more low-DE, high-activity hits.
It should be noted that some of the high-DE, low-activity hits might be really important because they just have low NicheNet activities due to limitations in the NicheNet activity prediction (eg improper/incomplete prior knowledge within NicheNet for that ligand), but some of them might also be high in DE but not in activity because they don’t have strong signaling effects (eg ligands involved in cell adhesion only).
For the opposite pairs with low-DE and high-activity that are not strongly prioritized by Differential NicheNet, the following should be considered: 1) some ligands are regulated post-transcriptionally, and that the high predicted activities might still reflect true signaling; 2) high predicted activity values might be due to limitations of NicheNet (inaccurate prior knowledge) and these lowDE ligands are not important in the biological process of interest (although a highDE family member of this ligand may! since signaling between family members tends to be very similar); 3) high activity in one condition might be due to downregulation in the other condition, leading to high activity but low DE. Currently, ligand activities are automatically calculated on upregulated genes per condition, but downregulated genes could also be a sign of ligand activity.
When Ligand-Receptor pairs have both high DE and high activity, we can consider them to be very good candidates in regulating the process of interest, and we recommend testing these candidates for further experimental validation.
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat Methods (2019) doi:10.1038/s41592-019-0667-5
Guilliams et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell (2022) doi:10.1016/j.cell.2021.12.018