作者，追风少年i
系统总结nichenet多条件通讯差异分析
nichenet的原理大家可以参考文章10X单细胞（10X空间转录组）通讯分析之NicheNet，10X单细胞（10X空间转录组）空间相关性分析和cellphoneDB与NicheNet联合进行细胞通讯分析，单细胞分析之细胞交互-5：NicheNet多组间互作比较。
Differential NicheNet analysis between niches of interest
关于niche，在空间转录组上很常见，就是生态位、微环境。
The goal of Differential NicheNet is to predict ligand-receptors pairs that are both differentially expressed and active between different niches of interest.
Load in packages
library(nichenetr)
library(RColorBrewer)
library(tidyverse)
library(Seurat)
示例数据
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")
Read in the NicheNet ligand-receptor network and ligand-target matrix
ligand_target_matrix = readRDS(url("https://zenodo.org/record/3260758/files/ligand_target_matrix.rds"))
ligand_target_matrix[1:5,1:5] # target genes in rows, ligands in columns
## CXCL1 CXCL2 CXCL3 CXCL5 PPBP
## A1BG 3.534343e-04 4.041324e-04 3.729920e-04 3.080640e-04 2.628388e-04
## A1BG-AS1 1.650894e-04 1.509213e-04 1.583594e-04 1.317253e-04 1.231819e-04
## A1CF 5.787175e-04 4.596295e-04 3.895907e-04 3.293275e-04 3.211944e-04
## A2M 6.027058e-04 5.996617e-04 5.164365e-04 4.517236e-04 4.590521e-04
## A2M-AS1 8.898724e-05 8.243341e-05 7.484018e-05 4.912514e-05 5.120439e-05
lr_network = readRDS(url("https://zenodo.org/record/3260758/files/lr_network.rds"))
lr_network = lr_network %>% mutate(bonafide = ! database %in% c("ppi_prediction","ppi_prediction_go"))
lr_network = lr_network %>% dplyr::rename(ligand = from, receptor = to) %>% distinct(ligand, receptor, bonafide)
head(lr_network)
## # A tibble: 6 x 3
## ligand receptor bonafide
##
## 1 CXCL1 CXCR2 TRUE
## 2 CXCL2 CXCR2 TRUE
## 3 CXCL3 CXCR2 TRUE
## 4 CXCL5 CXCR2 TRUE
## 5 PPBP CXCR2 TRUE
## 6 CXCL6 CXCR2 TRUE
人鼠基因转换
if(organism == "mouse"){
lr_network = lr_network %>% mutate(ligand = convert_human_to_mouse_symbols(ligand), receptor = convert_human_to_mouse_symbols(receptor)) %>% drop_na()
colnames(ligand_target_matrix) = ligand_target_matrix %>% colnames() %>% convert_human_to_mouse_symbols()
rownames(ligand_target_matrix) = ligand_target_matrix %>% rownames() %>% convert_human_to_mouse_symbols()
ligand_target_matrix = ligand_target_matrix %>% .[!is.na(rownames(ligand_target_matrix)), !is.na(colnames(ligand_target_matrix))]
}
1、Define the niches/microenvironments of interest
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 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:(借助空间转录组定义生态位)
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”.
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"))
)
2、Calculate differential expression between the niches
determine DE between the different niches for both senders and receivers to define the DE of L-R pairs.
Calculate DE
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).
assay_oi = "SCT" # other possibilities: RNA,...
seurat_obj = PrepSCTFindMarkers(seurat_obj, assay = "SCT", verbose = FALSE)
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"
## [2] "Calculate Sender DE between: LSECs_portal and Fibroblast 2"
## [3] "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"
## [2] "Calculate Sender DE between: Hepatocytes_portal and Fibroblast 2"
## [3] "Calculate Sender DE between: Hepatocytes_portal and Capsule fibroblasts"
## [4] "Calculate Sender DE between: Hepatocytes_portal and Mesothelial cells"
## [1] "Calculate Sender DE between: Stellate cells_portal and Cholangiocytes"
## [2] "Calculate Sender DE between: Stellate cells_portal and Fibroblast 2"
## [3] "Calculate Sender DE between: Stellate cells_portal and Capsule fibroblasts"
## [4] "Calculate Sender DE between: Stellate cells_portal and Mesothelial cells"
## [1] "Calculate Sender DE between: Cholangiocytes and LSECs_portal"
## [2] "Calculate Sender DE between: Cholangiocytes and Hepatocytes_portal"
## [3] "Calculate Sender DE between: Cholangiocytes and Stellate cells_portal"
## [4] "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"
## [2] "Calculate Sender DE between: Fibroblast 2 and Hepatocytes_portal"
## [3] "Calculate Sender DE between: Fibroblast 2 and Stellate cells_portal"
## [4] "Calculate Sender DE between: Fibroblast 2 and Capsule fibroblasts"
## [5] "Calculate Sender DE between: Fibroblast 2 and Mesothelial cells"
## [1] "Calculate Sender DE between: Capsule fibroblasts and LSECs_portal"
## [2] "Calculate Sender DE between: Capsule fibroblasts and Hepatocytes_portal"
## [3] "Calculate Sender DE between: Capsule fibroblasts and Stellate cells_portal"
## [4] "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"
## [2] "Calculate Sender DE between: Mesothelial cells and Hepatocytes_portal"
## [3] "Calculate Sender DE between: Mesothelial cells and Stellate cells_portal"
## [4] "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 x 2
## receiver receiver_other_niche
##
## 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)))
注意这里的差异分析，任意两对的细胞类型都纳入分析。
Process DE results
expression_pct = 0.10 ###这个值最好大一点
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")
Combine sender-receiver DE based on L-R pairs
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)
3、Optional: Calculate differential expression between the different spatial regions
为了改进细胞-细胞相互作用预测，如果可能且适用，可以考虑空间信息。空间信息可以来自显微镜数据，也可以来自空间转录组学数据，例如 Visium。
有几种方法可以将空间信息合并到差分 NicheNet pipeline中。首先，如果细胞类型位于相同的空间位置，则只能将它们视为属于同一生态位。另一种方法是在优先框架中包括一种细胞类型内配体-受体对的空间差异表达。
例如：有一个细胞类型 X，位于区域 A 和 B，想研究区域 A 的细胞间通信。首先在生态位定义中仅添加区域 A 的 celltypeX，然后计算 celltypeX-regionA 之间的 DE和 celltypeX-regionB 为 regionA 特异性配体提供更高的优先权。
在本案例研究中，感兴趣的区域是肝脏的门静脉周围区域，因为小鼠中的 KCs 主要位于门静脉周围区域。因此，与中心周围区域相比，分析将赋予在 KCs 生态位细胞中更高表达的配体的权重。
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))
}
4. Calculate ligand activities and infer active ligand-target links
在这一步中，将预测每个配体在不同生态位中的每种受体细胞类型的配体活性。 这类似于在正常 NicheNet 管道中进行的配体活性分析。
为了计算配体活性，首先需要为每个生态位定义一个感兴趣的geneset。 在本案例研究中，与胶囊和胆管巨噬细胞相比，Kupffer 细胞生态位感兴趣的基因组是 Kupffer 细胞中上调的基因。 与胶囊巨噬细胞和枯否细胞相比，胆管巨噬细胞生态位的基因组是胆管巨噬细胞中上调的基因。 同样对于感兴趣的胶囊巨噬细胞基因组。
也可以认为定义感兴趣的geneset
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] "Fcna" "Wfdc17" "AW112010" "mt-Co1" "mt-Nd2" "C4b" "Adgre4" "mt-Co3"
## [9] "Pira2" "mt-Co2" "mt-Nd4" "mt-Atp6" "mt-Nd1" "mt-Nd3" "Ear2" "2900097C17Rik"
## [17] "Iigp1" "Trim30a" "B430306N03Rik" "mt-Cytb" "Pilrb2" "Anapc15" "Arf2" "Gbp8"
## [25] "AC149090.1" "Cd209f" "Xlr" "Ifitm6"
geneset_MoMac2 %>% setdiff(rownames(ligand_target_matrix))
## [1] "Chil3" "Lyz1" "Ccl9" "Tmsb10" "Ly6c2" "Gm21188" "Gm10076" "Ms4a6c"
## [9] "Calm3" "Atp5e" "Ftl1-ps1" "S100a11" "Clec4a3" "Snrpe" "Cox6c" "Ly6i"
## [17] "1810058I24Rik" "Rpl34" "Aph1c" "Atp5o.1"
geneset_MoMac1 %>% setdiff(rownames(ligand_target_matrix))
## [1] "H2-Ab1" "Malat1" "H2-Aa" "Hspa1b" "Gm26522" "Ly6a" "H2-D1" "Klra2" "Bcl2a1d" "Kcnq1ot1"
length(geneset_KC)
## [1] 443
length(geneset_MoMac2)
## [1] 339
length(geneset_MoMac1)
## [1] 84
It 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)
在这一步中，将计算所有感兴趣的细胞类型的配体、受体和靶基因的平均（缩放）表达和表达分数。 现在，这通过 Seurat 的 DotPlot 函数进行了分析，但当然也可以通过其他方式完成。
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))
6. Expression fraction and receptor
在这一步中，将根据受体的表达强度对配体-受体相互作用进行评分，这样就可以对特定细胞类型中特定配体的最强表达受体给予更高的分数。 这不会影响以后单个配体的等级，但将有助于确定每个配体最重要的受体的优先级.
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, bonafide) %>% distinct() %>% ungroup()
7. Prioritization of ligand-receptor and ligand-target links
在这一步中，将结合上述所有计算信息来优先考虑配体-受体-目标link。 在 0 和 1 之间缩放每个感兴趣的属性，最终的优先级分数是所有感兴趣属性的缩放分数的加权和。
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_weights argument: "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_weights argument: "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_weights argument: "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_weights argument: "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_weights argument: "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_weights argument: "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_weights argument: "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_weights argument: "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_weights argument: "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_weights argument: "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_weights argument: "scaled_activity_normalized". Recommended weight: at least 1.
Prior knowledge quality of the L-R interaction: the NicheNet LR network consists of two types of interactions: L-R pairs documented in curated databases, and L-R pairs predicted based on gene annotation and PPIs. The former are categorized as ‘bona fide’ interactions. To rank bona fide interactions higher, but not exlude potentially interesting non-bona-fide ones, we give bona fide interactions a score of 1, and non-bona-fide interactions a score fof 0.5. prioritizing_weights argument: "bona_fide" Recommend 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,
"bona_fide" = 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]]$receiver) %>% head(10)
## # A tibble: 10 x 37
## niche receiver sender ligand_receptor ligand receptor bonafide ligand_score ligand_signific~ ligand_present ligand_expressi~
##
## 1 KC_niche KCs Hepatocytes~ Apoa1--Lrp1 Apoa1 Lrp1 FALSE 3.18 1 1 14.7
## 2 KC_niche KCs Hepatocytes~ Apoa1--Msr1 Apoa1 Msr1 FALSE 3.18 1 1 14.7
## 3 KC_niche KCs Hepatocytes~ Apoa1--Abca1 Apoa1 Abca1 FALSE 3.18 1 1 14.7
## 4 KC_niche KCs Hepatocytes~ Apoa1--Scarb1 Apoa1 Scarb1 FALSE 3.18 1 1 14.7
## 5 KC_niche KCs Hepatocytes~ Apoa1--Derl1 Apoa1 Derl1 FALSE 3.18 1 1 14.7
## 6 KC_niche KCs Hepatocytes~ Serpina1a--Lrp1 Serpina~ Lrp1 TRUE 2.64 1 1 6.97
## 7 KC_niche KCs Hepatocytes~ Apoa1--Atp5b Apoa1 Atp5b FALSE 3.18 1 1 14.7
## 8 KC_niche KCs Hepatocytes~ Trf--Tfrc Trf Tfrc TRUE 1.61 1 1 6.19
## 9 KC_niche KCs Hepatocytes~ Apoa1--Cd36 Apoa1 Cd36 FALSE 3.18 1 1 14.7
## 10 KC_niche KCs LSECs_portal Cxcl10--Fpr1 Cxcl10 Fpr1 FALSE 1.66 1 1 2.35
## # ... with 26 more variables: ligand_expression_scaled , ligand_fraction , ligand_score_spatial , receptor_score ,
## # receptor_significant , receptor_present , receptor_expression , receptor_expression_scaled ,
## # receptor_fraction , receptor_score_spatial , ligand_scaled_receptor_expression_fraction ,
## # avg_score_ligand_receptor , activity , activity_normalized , scaled_ligand_score ,
## # scaled_ligand_expression_scaled , scaled_receptor_score , scaled_receptor_expression_scaled ,
## # scaled_avg_score_ligand_receptor , scaled_ligand_score_spatial , scaled_receptor_score_spatial ,
## # scaled_ligand_fraction_adapted , scaled_receptor_fraction_adapted , scaled_activity , ...
prioritization_tables$prioritization_tbl_ligand_target %>% filter(receiver == niches[[1]]$receiver) %>% head(10)
## # A tibble: 10 x 20
## niche receiver sender ligand_receptor ligand receptor bonafide target target_score target_signific~ target_present target_expressi~
##
## 1 KC_niche KCs Hepato~ Apoa1--Lrp1 Apoa1 Lrp1 FALSE Abca1 0.197 1 1 0.979
## 2 KC_niche KCs Hepato~ Apoa1--Lrp1 Apoa1 Lrp1 FALSE Actb 0.279 1 1 21.6
## 3 KC_niche KCs Hepato~ Apoa1--Lrp1 Apoa1 Lrp1 FALSE Ehd1 0.272 1 1 0.402
## 4 KC_niche KCs Hepato~ Apoa1--Lrp1 Apoa1 Lrp1 FALSE Hmox1 1.16 1 1 5.23
## 5 KC_niche KCs Hepato~ Apoa1--Lrp1 Apoa1 Lrp1 FALSE Sgk1 0.265 1 1 0.629
## 6 KC_niche KCs Hepato~ Apoa1--Lrp1 Apoa1 Lrp1 FALSE Tcf7l2 0.811 1 1 1.32
## 7 KC_niche KCs Hepato~ Apoa1--Lrp1 Apoa1 Lrp1 FALSE Tsc22~ 0.263 1 1 0.635
## 8 KC_niche KCs Hepato~ Apoa1--Msr1 Apoa1 Msr1 FALSE Abca1 0.197 1 1 0.979
## 9 KC_niche KCs Hepato~ Apoa1--Msr1 Apoa1 Msr1 FALSE Actb 0.279 1 1 21.6
## 10 KC_niche KCs Hepato~ Apoa1--Msr1 Apoa1 Msr1 FALSE Ehd1 0.272 1 1 0.402
## # ... with 8 more variables: target_expression_scaled , target_fraction , ligand_target_weight , activity ,
## # activity_normalized , scaled_activity , scaled_activity_normalized , prioritization_score
prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(receiver == niches[[2]]$receiver) %>% head(10)
## # A tibble: 10 x 37
## niche receiver sender ligand_receptor ligand receptor bonafide ligand_score ligand_significa~ ligand_present ligand_expressi~
##
## 1 MoMac2_niche MoMac2 Cholangi~ Spp1--Itga4 Spp1 Itga4 TRUE 6.09 1 1 72.4
## 2 MoMac2_niche MoMac2 Cholangi~ Spp1--Cd44 Spp1 Cd44 TRUE 6.09 1 1 72.4
## 3 MoMac2_niche MoMac2 Cholangi~ Spp1--Itgb5 Spp1 Itgb5 TRUE 6.09 1 1 72.4
## 4 MoMac2_niche MoMac2 Cholangi~ Spp1--Itgav Spp1 Itgav TRUE 6.09 1 1 72.4
## 5 MoMac2_niche MoMac2 Cholangi~ Spp1--Itgb1 Spp1 Itgb1 TRUE 6.09 1 1 72.4
## 6 MoMac2_niche MoMac2 Cholangi~ Spp1--Itga9 Spp1 Itga9 TRUE 6.09 1 1 72.4
## 7 MoMac2_niche MoMac2 Cholangi~ Spp1--Ncstn Spp1 Ncstn FALSE 6.09 1 1 72.4
## 8 MoMac2_niche MoMac2 Cholangi~ Spp1--Itga5 Spp1 Itga5 FALSE 6.09 1 1 72.4
## 9 MoMac2_niche MoMac2 Fibrobla~ Lama2--Rpsa Lama2 Rpsa TRUE 1.51 1 1 3.19
## 10 MoMac2_niche MoMac2 Cholangi~ Cyr61--Itgb2 Cyr61 Itgb2 TRUE 0.812 1 1 3.11
## # ... with 26 more variables: ligand_expression_scaled , ligand_fraction , ligand_score_spatial , receptor_score ,
## # receptor_significant , receptor_present , receptor_expression , receptor_expression_scaled ,
## # receptor_fraction , receptor_score_spatial , ligand_scaled_receptor_expression_fraction ,
## # avg_score_ligand_receptor , activity , activity_normalized , scaled_ligand_score ,
## # scaled_ligand_expression_scaled , scaled_receptor_score , scaled_receptor_expression_scaled ,
## # scaled_avg_score_ligand_receptor , scaled_ligand_score_spatial , scaled_receptor_score_spatial ,
## # scaled_ligand_fraction_adapted , scaled_receptor_fraction_adapted , scaled_activity , ...
prioritization_tables$prioritization_tbl_ligand_target %>% filter(receiver == niches[[2]]$receiver) %>% head(10)
## # A tibble: 10 x 20
## niche receiver sender ligand_receptor ligand receptor bonafide target target_score target_signific~ target_present target_expressi~
##
## 1 MoMac2~ MoMac2 Cholang~ Spp1--Itga4 Spp1 Itga4 TRUE Ahnak 1.05 1 1 1.36
## 2 MoMac2~ MoMac2 Cholang~ Spp1--Itga4 Spp1 Itga4 TRUE Cdkn1a 0.609 1 1 0.801
## 3 MoMac2~ MoMac2 Cholang~ Spp1--Itga4 Spp1 Itga4 TRUE Cxcr4 0.374 1 1 0.717
## 4 MoMac2~ MoMac2 Cholang~ Spp1--Itga4 Spp1 Itga4 TRUE Dhrs3 0.371 1 1 0.743
## 5 MoMac2~ MoMac2 Cholang~ Spp1--Itga4 Spp1 Itga4 TRUE Fn1 0.360 1 1 0.456
## 6 MoMac2~ MoMac2 Cholang~ Spp1--Itga4 Spp1 Itga4 TRUE Gadd4~ 0.180 1 1 0.474
## 7 MoMac2~ MoMac2 Cholang~ Spp1--Itga4 Spp1 Itga4 TRUE Gapdh 0.656 1 1 3.27
## 8 MoMac2~ MoMac2 Cholang~ Spp1--Itga4 Spp1 Itga4 TRUE Gdf15 0.479 1 1 0.521
## 9 MoMac2~ MoMac2 Cholang~ Spp1--Itga4 Spp1 Itga4 TRUE Gsn 0.221 1 1 0.647
## 10 MoMac2~ MoMac2 Cholang~ Spp1--Itga4 Spp1 Itga4 TRUE Plec 0.154 1 1 0.164
## # ... with 8 more variables: target_expression_scaled , target_fraction , ligand_target_weight , activity ,
## # activity_normalized , scaled_activity , scaled_activity_normalized , prioritization_score
prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(receiver == niches[[3]]$receiver) %>% head(10)
## # A tibble: 10 x 37
## niche receiver sender ligand_receptor ligand receptor bonafide ligand_score ligand_signific~ ligand_present ligand_expressi~
##
## 1 MoMac1_niche MoMac1 Mesotheli~ C3--C3ar1 C3 C3ar1 TRUE 3.52 1 1 22.6
## 2 MoMac1_niche MoMac1 Capsule f~ C3--C3ar1 C3 C3ar1 TRUE 3.42 1 1 20.9
## 3 MoMac1_niche MoMac1 Mesotheli~ C3--Itgb2 C3 Itgb2 TRUE 3.52 1 1 22.6
## 4 MoMac1_niche MoMac1 Mesotheli~ C3--Itgax C3 Itgax TRUE 3.52 1 1 22.6
## 5 MoMac1_niche MoMac1 Mesotheli~ C3--Lrp1 C3 Lrp1 TRUE 3.52 1 1 22.6
## 6 MoMac1_niche MoMac1 Capsule f~ C3--Itgb2 C3 Itgb2 TRUE 3.42 1 1 20.9
## 7 MoMac1_niche MoMac1 Capsule f~ C3--Itgax C3 Itgax TRUE 3.42 1 1 20.9
## 8 MoMac1_niche MoMac1 Capsule f~ C3--Lrp1 C3 Lrp1 TRUE 3.42 1 1 20.9
## 9 MoMac1_niche MoMac1 Capsule f~ Rarres2--Cmklr1 Rarre~ Cmklr1 TRUE 2.50 1 1 15.8
## 10 MoMac1_niche MoMac1 Mesotheli~ C3--Ccr5 C3 Ccr5 FALSE 3.52 1 1 22.6
## # ... with 26 more variables: ligand_expression_scaled , ligand_fraction , ligand_score_spatial , receptor_score ,
## # receptor_significant , receptor_present , receptor_expression , receptor_expression_scaled ,
## # receptor_fraction , receptor_score_spatial , ligand_scaled_receptor_expression_fraction ,
## # avg_score_ligand_receptor , activity , activity_normalized , scaled_ligand_score ,
## # scaled_ligand_expression_scaled , scaled_receptor_score , scaled_receptor_expression_scaled ,
## # scaled_avg_score_ligand_receptor , scaled_ligand_score_spatial , scaled_receptor_score_spatial ,
## # scaled_ligand_fraction_adapted , scaled_receptor_fraction_adapted , scaled_activity , ...
prioritization_tables$prioritization_tbl_ligand_target %>% filter(receiver == niches[[3]]$receiver) %>% head(10)
## # A tibble: 10 x 20
## niche receiver sender ligand_receptor ligand receptor bonafide target target_score target_signific~ target_present target_expressi~
##
## 1 MoMac1~ MoMac1 Mesothe~ C3--C3ar1 C3 C3ar1 TRUE Btg2 0.615 1 1 1.51
## 2 MoMac1~ MoMac1 Mesothe~ C3--C3ar1 C3 C3ar1 TRUE Ccnd2 0.505 1 1 0.490
## 3 MoMac1~ MoMac1 Mesothe~ C3--C3ar1 C3 C3ar1 TRUE Cdk6 0.221 1 1 0.320
## 4 MoMac1~ MoMac1 Mesothe~ C3--C3ar1 C3 C3ar1 TRUE Ier5 0.396 1 1 1.16
## 5 MoMac1~ MoMac1 Mesothe~ C3--C3ar1 C3 C3ar1 TRUE Il1b 0.956 1 1 3.74
## 6 MoMac1~ MoMac1 Mesothe~ C3--C3ar1 C3 C3ar1 TRUE Jun 0.765 1 1 1.93
## 7 MoMac1~ MoMac1 Mesothe~ C3--C3ar1 C3 C3ar1 TRUE Pdgfb 0.243 1 1 0.510
## 8 MoMac1~ MoMac1 Mesothe~ C3--C3ar1 C3 C3ar1 TRUE Ubc 0.306 1 1 2.16
## 9 MoMac1~ MoMac1 Capsule~ C3--C3ar1 C3 C3ar1 TRUE Btg2 0.615 1 1 1.51
## 10 MoMac1~ MoMac1 Capsule~ C3--C3ar1 C3 C3ar1 TRUE Ccnd2 0.505 1 1 0.490
## # ... with 8 more variables: target_expression_scaled , target_fraction , ligand_target_weight , activity ,
## # activity_normalized , scaled_activity , scaled_activity_normalized , prioritization_score
prioritization_tables$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$prioritization_tbl_ligand_target %>% mutate(receiver = factor(receiver, levels = c("KCs","MoMac1","MoMac2")), niche = factor(niche, levels = c("KC_niche","MoMac1_niche","MoMac2_niche")))
8、可视化
Differential expression of ligand and expression
Before visualization, we need to define the most important ligand-receptor pairs per niche. We will do this by first determining for which niche the highest score is found for each ligand/ligand-receptor pair. And then getting the top 50 ligands per niche.
top_ligand_niche_df = prioritization_tables$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 niche
receiver_oi = "KCs"
filtered_ligands = ligand_prioritized_tbl_oi %>% filter(receiver == receiver_oi) %>% pull(ligand) %>% unique()
prioritized_tbl_oi = prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(ligand %in% filtered_ligands) %>% select(niche, sender, receiver, ligand, receptor, ligand_receptor, prioritization_score) %>% distinct() %>% inner_join(top_ligand_receptor_niche_df) %>% group_by(ligand) %>% filter(receiver == receiver_oi) %>% top_n(2, prioritization_score) %>% ungroup()
lfc_plot = make_ligand_receptor_lfc_plot(receiver_oi, prioritized_tbl_oi, prioritization_tables$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$prioritization_tbl_ligand_receptor, ligand_spatial = include_spatial_info_sender, receptor_spatial = include_spatial_info_receiver, plot_legend = FALSE, heights = NULL, widths = NULL)
lfc_plot_spatial
从这个图中，您可以看到一些 KC niche配体，如 Dll4（由 LSEC）和 Il34（由星状细胞）在门静脉周围 LSEC/星状细胞中的表达高于中央周围的。 这可能是有趣的信息，因为知道 KC 主要位于门静脉。 然而，其他配体，如 Gdf2（星状细胞）不优先由门静脉周围星状细胞表达，但这并不意味着它们不有趣。 如下图所示，该配体具有最高的配体活性之一，这意味着其靶基因在 KC 特异性基因中存在很强的富集。
Ligand expression, activity and target genes
exprs_activity_target_plot = make_ligand_activity_target_exprs_plot(receiver_oi, prioritized_tbl_oi, prioritization_tables$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
On this plot, we can see that 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. An example of this is Gdf2 and Bmp10, who bind the receptor Acvrl1 (ALK1). The role of Gdf2/Bmp10-Acvrl1 in KC development was experimentally validated in the Guilliams et al paper.
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 !
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
Circos plot of prioritized ligand-receptor pairs
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)
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