Robin Browaeys 2022-01-12
Remark: this is a beta version of a new extension of NicheNet, namely Differential NicheNet. Short-term improvements will include scalability, visualization and documentation of this vignette and the underlying functions (january 2022).
The goal of Differential NicheNet is to predict ligand-receptors pairs that are both differentially expressed and active between different niches of interest.
This vignette guides you in detail through all the steps of a Differential NicheNet analysis. As example expression data of interacting cells, we will use data from Puram et al. to explore intercellular communication in the tumor microenvironment in head and neck squamous cell carcinoma (HNSCC) (Puram et al. 2017). More specifically, we will look at cell-cell communication differences between pEMT-high and pEMT-low tumors (pEMT = partial epithelial-mesenschymal transition). In this data, we thus have 2 conditions/niches, but this pipeline is also usable for more conditions/niches.
The used ligand-receptor network and ligand-target matrix can be
downloaded from Zenodo
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) #In this case study, we want to study differences in cell-cell communication patterns between pEMT-high and pEMT-low tumors. The meta data columns that indicate the pEMT status of tumors are ‘pEMT’, and the cell type is indicated in the ‘celltype’ column.
seurat_obj = readRDS(url("https://zenodo.org/record/4675430/files/seurat_obj_hnscc.rds"))
DimPlot(seurat_obj, group.by = "celltype") # user adaptation required on own dataset
DimPlot(seurat_obj, group.by = "pEMT") # user adaptation required on own dataset
We will now also check the number of cells per cell type condition combination
table(seurat_obj@meta.data$celltype, seurat_obj@meta.data$pEMT) # cell types vs conditions # user adaptation required on own dataset
##
## High Low
## CAF 396 104
## Endothelial 105 53
## Malignant 1093 549
## Myeloid 92 7
## myofibroblast 382 61
## T.cell 689 3For the Differential NicheNet, we need to compare at least 2 niches or conditions to each other. In this case, the 2 niches are the pEMT-high-niche and the pEMT-low-niche. We will adapt the names of the cell types based on their niche of origin.
seurat_obj@meta.data$celltype_aggregate = paste(seurat_obj@meta.data$celltype, seurat_obj@meta.data$pEMT,sep = "_") # user adaptation required on own dataset
DimPlot(seurat_obj, group.by = "celltype_aggregate")
seurat_obj@meta.data$celltype_aggregate %>% table() %>% sort(decreasing = TRUE)
## .
## Malignant_High T.cell_High Malignant_Low CAF_High myofibroblast_High Endothelial_High
## 1093 689 549 396 382 105
## CAF_Low Myeloid_High myofibroblast_Low Endothelial_Low Myeloid_Low T.cell_Low
## 104 92 61 53 7 3celltype_id = "celltype_aggregate" # metadata column name of the cell type of interest
seurat_obj = SetIdent(seurat_obj, value = seurat_obj[[celltype_id, drop=TRUE]])ligand_target_matrix = readRDS(url("https://zenodo.org/record/7074291/files/ligand_target_matrix_nsga2r_final.rds"))
ligand_target_matrix[1:5,1:5] # target genes in rows, ligands in columns
## A2M AANAT ABCA1 ACE ACE2
## A-GAMMA3'E 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.000000000
## A1BG 0.0018503922 0.0011108718 0.0014225077 0.0028594037 0.001139013
## A1BG-AS1 0.0007400797 0.0004677614 0.0005193137 0.0007836698 0.000375007
## A1CF 0.0024799266 0.0013026348 0.0020420890 0.0047921048 0.003273375
## A2M 0.0084693452 0.0040689323 0.0064256379 0.0105191365 0.005719199lr_network = readRDS(url("https://zenodo.org/record/7074291/files/lr_network_human_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 A2M MMP2
## 2 A2M MMP9
## 3 A2M LRP1
## 4 A2M KLK3
## 5 AANAT MTNR1A
## 6 AANAT MTNR1BNote: if your data is of mouse origin: use the mouse networks
organism = "human" # user adaptation required on own dataset
if(organism == "mouse"){
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"))
}Each 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 malignant cells between pEMT high and pEMT low tumors. The receiver cell population in the pEMT-High niche is thus the “Malignant_High” cell type, and in the pEMT-Low niche this is “Malignant_Low”. The sender cell populations of interest are myofibroblasts, Endothelial, CAF, T.cell, and Myeloid. Importantly, we only include T.Cell and Myeloid in the pEMT-High niche, because there are too few cells of these populations present in the pEMT-low niche. Hereby, we demonstrate the possibility to include a condition-specific cell type in the analysis - which is possible because we calculate DE compared to all sender cells of the other niche, and not only to the pEMT-low group of cells of the same cell type.
! Important: your receiver cell type should consist of 1 cluster!
niches = list(
"pEMT_High_niche" = list(
"sender" = c("myofibroblast_High", "Endothelial_High", "CAF_High", "T.cell_High", "Myeloid_High"),
"receiver" = c("Malignant_High")),
"pEMT_Low_niche" = list(
"sender" = c("myofibroblast_Low", "Endothelial_Low", "CAF_Low"),
"receiver" = c("Malignant_Low"))
) # user adaptation required on own datasetIn 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 that DE of myofibroblast_High ligands will be calculated by DE analysis of myofibroblast_High vs myofibroblast_Low; myofibroblast_High vs Endothelial_Low; and myofibroblast_High vs CAF_Low. 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,...
seurat_obj = alias_to_symbol_seurat(seurat_obj, organism = "human")
DE_sender = calculate_niche_de(seurat_obj = seurat_obj %>% subset(features = lr_network$ligand %>% unique()), niches = niches, type = "sender", assay_oi = assay_oi) # only ligands important for sender cell types
## [1] "Calculate Sender DE between: myofibroblast_High and myofibroblast_Low"
## [2] "Calculate Sender DE between: myofibroblast_High and Endothelial_Low"
## [3] "Calculate Sender DE between: myofibroblast_High and CAF_Low"
## [1] "Calculate Sender DE between: Endothelial_High and myofibroblast_Low"
## [2] "Calculate Sender DE between: Endothelial_High and Endothelial_Low"
## [3] "Calculate Sender DE between: Endothelial_High and CAF_Low"
## [1] "Calculate Sender DE between: CAF_High and myofibroblast_Low"
## [2] "Calculate Sender DE between: CAF_High and Endothelial_Low"
## [3] "Calculate Sender DE between: CAF_High and CAF_Low"
## [1] "Calculate Sender DE between: T.cell_High and myofibroblast_Low"
## [2] "Calculate Sender DE between: T.cell_High and Endothelial_Low"
## [3] "Calculate Sender DE between: T.cell_High and CAF_Low"
## [1] "Calculate Sender DE between: Myeloid_High and myofibroblast_Low"
## [2] "Calculate Sender DE between: Myeloid_High and Endothelial_Low"
## [3] "Calculate Sender DE between: Myeloid_High and CAF_Low"
## [1] "Calculate Sender DE between: myofibroblast_Low and myofibroblast_High"
## [2] "Calculate Sender DE between: myofibroblast_Low and Endothelial_High"
## [3] "Calculate Sender DE between: myofibroblast_Low and CAF_High"
## [4] "Calculate Sender DE between: myofibroblast_Low and T.cell_High"
## [5] "Calculate Sender DE between: myofibroblast_Low and Myeloid_High"
## [1] "Calculate Sender DE between: Endothelial_Low and myofibroblast_High"
## [2] "Calculate Sender DE between: Endothelial_Low and Endothelial_High"
## [3] "Calculate Sender DE between: Endothelial_Low and CAF_High"
## [4] "Calculate Sender DE between: Endothelial_Low and T.cell_High"
## [5] "Calculate Sender DE between: Endothelial_Low and Myeloid_High"
## [1] "Calculate Sender DE between: CAF_Low and myofibroblast_High"
## [2] "Calculate Sender DE between: CAF_Low and Endothelial_High"
## [3] "Calculate Sender DE between: CAF_Low and CAF_High"
## [4] "Calculate Sender DE between: CAF_Low and T.cell_High"
## [5] "Calculate Sender DE between: CAF_Low and Myeloid_High"
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: 1 × 2
## receiver receiver_other_niche
## <chr> <chr>
## 1 Malignant_High Malignant_Low
## [1] "Calculate receiver DE between: Malignant_High and Malignant_Low"
## [1] "Calculate receiver DE between: Malignant_Low and Malignant_High"
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 tumor leading edge, since Puram et al defined this region as important regarding the pEMT process. Puram et al also defined CAFs as the fibroblasts that are close to leading edge, whereas the other fibroblasts (myofibroblasts) were not preferentially located in the tumor leading edge. We can thus now prioritize fibroblast ligands further by looking at ligands that are DE between leading-edge fibroblasts (=CAFs) and non-leading-edge fibroblasts (myofibroblasts).
We do this as follows, by first defining a ‘spatial info’ dataframe. If 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 # user adaptation required on own dataset
include_spatial_info_receiver = FALSE # if spatial info to include: put this to true # user adaptation required on own datasetspatial_info = tibble(celltype_region_oi = "CAF_High", celltype_other_region = "myofibroblast_High", niche = "pEMT_High_niche", celltype_type = "sender") # user adaptation required on own dataset
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$ligand %>% unique()), spatial_info = spatial_info %>% filter(celltype_type == "sender"))
sender_spatial_DE_processed = process_spatial_de(DE_table = sender_spatial_DE, type = "sender", lr_network = lr_network, expression_pct = expression_pct, specificity_score = specificity_score_spatial)
# add a neutral spatial score for sender celltypes in which the spatial is not known / not of importance
sender_spatial_DE_others = get_non_spatial_de(niches = niches, spatial_info = spatial_info, type = "sender", lr_network = lr_network)
sender_spatial_DE_processed = sender_spatial_DE_processed %>% bind_rows(sender_spatial_DE_others)
sender_spatial_DE_processed = sender_spatial_DE_processed %>% mutate(scaled_ligand_score_spatial = scale_quantile_adapted(ligand_score_spatial))
} else {
# # add a neutral spatial score for all sender celltypes (for none of them, spatial is relevant in this case)
sender_spatial_DE_processed = get_non_spatial_de(niches = niches, spatial_info = spatial_info, type = "sender", lr_network = lr_network)
sender_spatial_DE_processed = sender_spatial_DE_processed %>% mutate(scaled_ligand_score_spatial = scale_quantile_adapted(ligand_score_spatial))
}
## [1] "Calculate Spatial DE between: CAF_High and myofibroblast_High"if(include_spatial_info_receiver == TRUE){
receiver_spatial_DE = calculate_spatial_DE(seurat_obj = seurat_obj %>% subset(features = lr_network$receptor %>% unique()), spatial_info = spatial_info %>% filter(celltype_type == "receiver"))
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 pEMT-high niche are the genes upregulated in pEMT-high tumors compared to pEMT-low tumors, and vice versa.
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: Malignant_High and Malignant_Low"
## [1] "Calculate receiver DE between: Malignant_Low and Malignant_High"
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_niche1 = DE_receiver_processed_targets %>% filter(receiver == niches[[1]]$receiver & target_score >= lfc_cutoff & target_significant == 1 & target_present == 1) %>% pull(target) %>% unique()
geneset_niche2 = DE_receiver_processed_targets %>% filter(receiver == niches[[2]]$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_niche1 %>% setdiff(rownames(ligand_target_matrix))
## [1] "ANXA8L2" "LOC645638" "LOC401397" "LOC375295" "LOC100505633" "LOC100507463" "KRT16P1"
## [8] "LSMD1" "HN1L" "LOC550643" "LOC100505806" "SARS" "LOC100130476"
geneset_niche2 %>% setdiff(rownames(ligand_target_matrix))
## [1] "LOC344887" "AGPAT9" "LOC100292680" "CT45A4" "LOC654433" "LOC100128338" "LOC401109"
## [8] "LOC286467" "LOC100133331" "LOC440173" "LOC731275" "LOC642236" "LINC00516" "LOC101101776"
## [15] "LOC100130093" "LOC100132891" "C2orf47" "LOC100289019" "LOC100216479" "LOC100505761" "MGC57346"
## [22] "LOC100506060" "LOC728752" "FLJ31485" "LOC440905" "LOC100129269" "LOC285074" "LOC202781"
## [29] "LOC100506714" "LOC401164" "LOC146880" "KIAA1804" "LOC339240" "FLJ35024" "LOC100505876"
## [36] "MGC21881" "LOC100133985" "LOC100129361" "MST4" "LOC283922" "LOC550112" "LOC389791"
## [43] "LOC100506469" "LOC100133161" "LOC646719" "LOC728819" "LOC728377" "LOC100134229" "MUM1"
## [50] "LOC338758" "LOC91948" "LOC100505783" "LOC339535" "LOC150776" "LOC100507173" "LOC645513"
## [57] "LOC100272216" "LOC400027" "LOC100130855" "LOC100288748" "C7orf55" "LOC90834" "LOC100132352"
## [64] "LOC100630918" "MGC27345" "LOC401074" "RP1-177G6.2" "FLJ45445" "LOC100506233" "LOC100652772"
## [71] "FLJ14186" "MARS" "LOC100287042" "LOC286186" "FLJ30403" "FLJ43681" "LOC152217"
## [78] "LOC100507217" "NBPF24" "LOC284889" "PMS2L2" "LOC100130451" "LOC399744" "LOC338799"
## [85] "LOC100506190" "LOC400927" "FLJ45340" "LOC349196" "LOC100128881" "LOC728558" "LOC283693"
## [92] "LOC145820" "LOC100289341" "LOC100506394"
length(geneset_niche1)
## [1] 1668
length(geneset_niche2)
## [1] 2889It 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.
As we see here, we have Smart-seq2 data and only 2 niches to compare, so we will use a stronger LFC threshold to keep less DE genes, but more trustworthy ones.
lfc_cutoff = 0.75
specificity_score_targets = "min_lfc"
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_niche1 = DE_receiver_processed_targets %>% filter(receiver == niches[[1]]$receiver & target_score >= lfc_cutoff & target_significant == 1 & target_present == 1) %>% pull(target) %>% unique()
geneset_niche2 = DE_receiver_processed_targets %>% filter(receiver == niches[[2]]$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_niche1 %>% setdiff(rownames(ligand_target_matrix))
## [1] "ANXA8L2"
geneset_niche2 %>% setdiff(rownames(ligand_target_matrix))
## [1] "LOC344887" "AGPAT9" "LOC100292680" "CT45A4"
length(geneset_niche1)
## [1] 169
length(geneset_niche2)
## [1] 136top_n_target = 250
niche_geneset_list = list(
"pEMT_High_niche" = list(
"receiver" = niches[[1]]$receiver,
"geneset" = geneset_niche1,
"background" = background),
"pEMT_Low_niche" = list(
"receiver" = niches[[2]]$receiver,
"geneset" = geneset_niche2 ,
"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: Malignant_High"
## [1] "Calculate Ligand activities for: Malignant_Low"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)Note: these settings will give substantially more weight to DE ligand-receptor pairs compared to activity. Users can change this if wanted, just like other settings can be changed if that would be better to tackle the specific biological question you want to address.
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]]$receiver) %>% head(10)
## # A tibble: 10 × 36
## niche receiver sender ligand_receptor ligand receptor ligand_score ligand_signific… ligand_present
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 pEMT_High_niche Malignant_Hi… T.cel… PTPRC--DPP4 PTPRC DPP4 3.22 1 1
## 2 pEMT_High_niche Malignant_Hi… T.cel… CD96--NECTIN1 CD96 NECTIN1 2.39 1 1
## 3 pEMT_High_niche Malignant_Hi… Myelo… C1QA--CSPG4 C1QA CSPG4 2.52 1 1
## 4 pEMT_High_niche Malignant_Hi… Myelo… SERPINA1--F12 SERPI… F12 2.52 1 1
## 5 pEMT_High_niche Malignant_Hi… Myelo… C1QB--LRP1 C1QB LRP1 2.69 1 1
## 6 pEMT_High_niche Malignant_Hi… Myelo… ITGB2--CD82 ITGB2 CD82 2.48 1 1
## 7 pEMT_High_niche Malignant_Hi… Myelo… TYROBP--KLRD1 TYROBP KLRD1 2.77 1 1
## 8 pEMT_High_niche Malignant_Hi… T.cel… TNF--TNFRSF21 TNF TNFRSF21 1.74 1 1
## 9 pEMT_High_niche Malignant_Hi… Myelo… TYROBP--TREM1 TYROBP TREM1 2.77 1 1
## 10 pEMT_High_niche Malignant_Hi… T.cel… SIRPG--CD47 SIRPG CD47 2.06 1 1
## # … with 27 more variables: ligand_expression <dbl>, ligand_expression_scaled <dbl>, ligand_fraction <dbl>,
## # ligand_score_spatial <dbl>, receptor_score <dbl>, receptor_significant <dbl>, receptor_present <dbl>,
## # receptor_expression <dbl>, receptor_expression_scaled <dbl>, receptor_fraction <dbl>,
## # receptor_score_spatial <dbl>, ligand_scaled_receptor_expression_fraction <dbl>,
## # avg_score_ligand_receptor <dbl>, activity <dbl>, activity_normalized <dbl>, scaled_ligand_score <dbl>,
## # scaled_ligand_expression_scaled <dbl>, scaled_receptor_score <dbl>, scaled_receptor_expression_scaled <dbl>,
## # scaled_avg_score_ligand_receptor <dbl>, scaled_ligand_score_spatial <dbl>, …
prioritization_tables$prioritization_tbl_ligand_target %>% filter(receiver == niches[[1]]$receiver) %>% head(10)
## # A tibble: 10 × 19
## niche receiver sender ligand_receptor ligand receptor target target_score target_signific… target_present
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 pEMT_High_ni… Maligna… T.cel… PTPRC--DPP4 PTPRC DPP4 AIM2 0.815 1 1
## 2 pEMT_High_ni… Maligna… T.cel… PTPRC--DPP4 PTPRC DPP4 APP 0.770 1 1
## 3 pEMT_High_ni… Maligna… T.cel… PTPRC--DPP4 PTPRC DPP4 CXCL2 1.07 1 1
## 4 pEMT_High_ni… Maligna… T.cel… PTPRC--DPP4 PTPRC DPP4 CXCL8 0.897 1 1
## 5 pEMT_High_ni… Maligna… T.cel… PTPRC--DPP4 PTPRC DPP4 GADD4… 0.836 1 1
## 6 pEMT_High_ni… Maligna… T.cel… PTPRC--DPP4 PTPRC DPP4 IGFBP3 1.06 1 1
## 7 pEMT_High_ni… Maligna… T.cel… PTPRC--DPP4 PTPRC DPP4 ITGA5 1.05 1 1
## 8 pEMT_High_ni… Maligna… T.cel… PTPRC--DPP4 PTPRC DPP4 MMP1 0.918 1 1
## 9 pEMT_High_ni… Maligna… T.cel… PTPRC--DPP4 PTPRC DPP4 MMP13 0.855 1 1
## 10 pEMT_High_ni… Maligna… T.cel… PTPRC--DPP4 PTPRC DPP4 NDRG1 0.906 1 1
## # … with 9 more variables: target_expression <dbl>, target_expression_scaled <dbl>, target_fraction <dbl>,
## # ligand_target_weight <dbl>, activity <dbl>, activity_normalized <dbl>, scaled_activity <dbl>,
## # scaled_activity_normalized <dbl>, prioritization_score <dbl>
prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(receiver == niches[[2]]$receiver) %>% head(10)
## # A tibble: 10 × 36
## niche receiver sender ligand_receptor ligand receptor ligand_score ligand_signific… ligand_present
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 pEMT_Low_niche Malignant_Low Endoth… IL33--IL1RAP IL33 IL1RAP 1.34 1 1
## 2 pEMT_Low_niche Malignant_Low Endoth… F8--LDLR F8 LDLR 0.952 1 1
## 3 pEMT_Low_niche Malignant_Low Endoth… POSTN--PTK7 POSTN PTK7 0.891 1 1
## 4 pEMT_Low_niche Malignant_Low CAF_Low IGSF10--MILR1 IGSF10 MILR1 0.859 1 1
## 5 pEMT_Low_niche Malignant_Low Endoth… PLAT--LRP1 PLAT LRP1 0.913 1 1
## 6 pEMT_Low_niche Malignant_Low myofib… IGFBPL1--DCC IGFBP… DCC 0.789 0.4 1
## 7 pEMT_Low_niche Malignant_Low Endoth… EPHA4--FGFR2 EPHA4 FGFR2 0.464 0.8 1
## 8 pEMT_Low_niche Malignant_Low CAF_Low SLIT2--SDC1 SLIT2 SDC1 0.494 1 1
## 9 pEMT_Low_niche Malignant_Low Endoth… F8--LRP4 F8 LRP4 0.952 1 1
## 10 pEMT_Low_niche Malignant_Low CAF_Low FGF14--SCN9A FGF14 SCN9A 0.200 0.4 1
## # … with 27 more variables: ligand_expression <dbl>, ligand_expression_scaled <dbl>, ligand_fraction <dbl>,
## # ligand_score_spatial <dbl>, receptor_score <dbl>, receptor_significant <dbl>, receptor_present <dbl>,
## # receptor_expression <dbl>, receptor_expression_scaled <dbl>, receptor_fraction <dbl>,
## # receptor_score_spatial <dbl>, ligand_scaled_receptor_expression_fraction <dbl>,
## # avg_score_ligand_receptor <dbl>, activity <dbl>, activity_normalized <dbl>, scaled_ligand_score <dbl>,
## # scaled_ligand_expression_scaled <dbl>, scaled_receptor_score <dbl>, scaled_receptor_expression_scaled <dbl>,
## # scaled_avg_score_ligand_receptor <dbl>, scaled_ligand_score_spatial <dbl>, …
prioritization_tables$prioritization_tbl_ligand_target %>% filter(receiver == niches[[2]]$receiver) %>% head(10)
## # A tibble: 10 × 19
## niche receiver sender ligand_receptor ligand receptor target target_score target_signific… target_present
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 pEMT_Low_nic… Maligna… Endot… IL33--IL1RAP IL33 IL1RAP F2RL2 0.923 1 1
## 2 pEMT_Low_nic… Maligna… Endot… IL33--IL1RAP IL33 IL1RAP MSC 1.04 1 1
## 3 pEMT_Low_nic… Maligna… Endot… IL33--IL1RAP IL33 IL1RAP SHISA2 0.805 1 1
## 4 pEMT_Low_nic… Maligna… Endot… IL33--IL1RAP IL33 IL1RAP SLC7A… 1.05 1 1
## 5 pEMT_Low_nic… Maligna… Endot… F8--LDLR F8 LDLR <NA> NA NA NA
## 6 pEMT_Low_nic… Maligna… Endot… F8--LDLR F8 LDLR <NA> NA NA NA
## 7 pEMT_Low_nic… Maligna… Endot… F8--LDLR F8 LDLR <NA> NA NA NA
## 8 pEMT_Low_nic… Maligna… Endot… F8--LDLR F8 LDLR <NA> NA NA NA
## 9 pEMT_Low_nic… Maligna… Endot… F8--LDLR F8 LDLR <NA> NA NA NA
## 10 pEMT_Low_nic… Maligna… Endot… F8--LDLR F8 LDLR <NA> NA NA NA
## # … with 9 more variables: target_expression <dbl>, target_expression_scaled <dbl>, target_fraction <dbl>,
## # ligand_target_weight <dbl>, activity <dbl>, activity_normalized <dbl>, scaled_activity <dbl>,
## # scaled_activity_normalized <dbl>, prioritization_score <dbl>Before visualization, we need to define the most important ligand-receptor pairs per niche. We will do this by first determining for which niche the highest score is found for each ligand/ligand-receptor pair. And then getting the top 50 ligands per niche.
top_ligand_niche_df = prioritization_tables$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 = "Malignant_High"
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$prioritization_tbl_ligand_receptor, ligand_spatial = include_spatial_info_sender, receptor_spatial = include_spatial_info_receiver, plot_legend = FALSE, heights = NULL, widths = NULL)
lfc_plot_spatial
Active target gene inference - cf Default NicheNet
Now: visualization of ligand activity and ligand-target links
exprs_activity_target_plot = make_ligand_activity_target_exprs_plot(receiver_oi, prioritized_tbl_oi, prioritization_tables$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
Based on this plot, we can infer many hypotheses such as the following:
“Interestingly, IL1 family ligands seem to have activity in inducing the
DE genes between high pEMT and low pEMT malignant cells; and they are
mainly expressed by myeloid cells, a cell type unique for pEMT-high
tumors”.
important: ligand-receptor pairs with both high differential expression (or condition-specificity) 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$exprs_tbl_target, lfc_cutoff, ligand_target_matrix, plot_legend = FALSE, heights = NULL, widths = NULL)
exprs_activity_target_plot$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)
Most top-ranked differential L-R pairs seem to come from the cell types that are only present in the pEMT-high tumors. This might be partially due to biology (unique cell types in one condition, are likely to be very important), but might also be due to the way of prioritizing and the fact that those unique cell types don’t have a ‘counterpart’ in the other niche(s).
Because myeloid cells and T cells are very different from the other cells in the tumor microenvironment, their ligands will show strong differential expression. This differential expression (myeloid/tcell vs myofibroblasts/CAFs/Endothelial cells in low-pEMT) is likely to be more pronounced compared to differential expression between cells from the same cell type but different niche/condition (CAF in pEMT-high vs CAF in pEMT-low). So conclusion: it is an advantage of Differential NicheNet that it can cope with condition-specifc cell types, but the user should be aware that the final general score might be biased towards condition-specific sender cell types. Therefore we suggest to also have a look at the top LR pairs per sender cell type (as we did here for the first figures) if you have a case study in which some sender cell types are condition-specific.
receiver_oi = "Malignant_Low"
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
Puram, Sidharth V., Itay Tirosh, Anuraag S. Parikh, Anoop P. Patel, Keren Yizhak, Shawn Gillespie, Christopher Rodman, et al. 2017. “Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer.” Cell 171 (7): 1611–1624.e24. https://doi.org/10.1016/j.cell.2017.10.044.