作者，追风少年i
周三了，好好学习，天天搬砖，人生没有倒带，前进才是真理
接上一篇，10X单细胞（10X空间转录组）通讯分析之总结Nichenet生态位通讯比较分析，不同条件下样本之间的差异通讯，其实关于通讯最开始的分析是细胞类型之间，后来慢慢转移到相对于normal，疾病之间通讯的变化。
Differential NicheNet 的目标是预测配体-受体对，它们在不同的感兴趣的生态位之间都有差异表达和活跃。关于生态位，请参考10X单细胞（10X空间转录组）通讯分析之总结Nichenet生态位通讯比较分析。
0. Read in the expression data of interest, and the NicheNet ligand-receptor network and ligand-target matrix
Load in packages
library(nichenetr)
library(RColorBrewer)
library(tidyverse)
library(Seurat)
读取表达数据，大家自己读取分析的rds即可
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
检查细胞类型的分布
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 3
对于Differential NicheNet，需要相互比较至少2 个生态位或条件。 在这种情况下，两个生态位是 pEMT-high-niche 和 pEMT-low-niche。 fenxi 将根据细胞类型的样本分布调整它们的名称。
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 CAF_Low
## 1093 689 549 396 382 105 104
## Myeloid_High myofibroblast_Low Endothelial_Low Myeloid_Low T.cell_Low
## 92 61 53 7 3
celltype_id = "celltype_aggregate" # metadata column name of the cell type of interest
seurat_obj = SetIdent(seurat_obj, value = seurat_obj[[celltype_id]])
Read in the NicheNet ligand-receptor network and ligand-target matrix(读取数据库)
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
## <chr> <chr> <lgl>
## 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
organism = "human" # user adaptation required on own dataset
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
每个生态位应至少有一个“发送者/生态位”细胞群和一个“接收者/目标”细胞群
在本案例研究中，希望发现 pEMT 高和 pEMT 低肿瘤之间细胞间相互作用与恶性细胞的差异。 因此，pEMT-High 生态位中的接收细胞群是“Malignant_High”细胞类型，而在 pEMT-Low 生态位中，这是“Malignant_Low”。 感兴趣的发送细胞群是肌成纤维细胞、内皮细胞、CAF、T 细胞和骨髓细胞。 重要的是，只在 pEMT-High 生态位中包括 T.Cell 和 Myeloid，因为这些群体的细胞太少存在于 pEMT-low 生态位中。 因此，证明了在分析中包括特定条件细胞类型的可能性——这是可能的，因为计算了与其他生态位的所有发送细胞相比的 DE，而不仅仅是同一细胞的 pEMT-low 细胞类型。
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 dataset
2. Calculate differential expression between the niches
在这一步中，将为发送者和接收者确定不同生态位之间的 DE，以定义 L-R 对的 DE。
Calculate DE
计算 differential expression的方法在这里是标准的 Seurat Wilcoxon 检验，但如果需要，可以将其替换（唯一要求：输出表 DE_sender_processed 和 DE_receiver_processed 应采用与此处所示相同的格式）。
将为每个成对的发送者（或接收者）细胞类型在生态位之间的比较计算 DE（因此跨生态位，而不是在生态位内）。 在分析的案例研究中，这意味着 myofibroblast_High 配体的 DE 将通过 myofibroblast_High 与 myofibroblast_Low 的 DE 分析来计算； 肌成纤维细胞高 vs 内皮低； 和肌成纤维细胞高 vs CAF低。 根据细胞类型拆分细胞，而不是合并来自其他生态位的所有细胞，以避免 DE 分析由最丰富的细胞类型驱动（和上一篇差异巨大的地方）。
assay_oi = "SCT" # other possibilities: RNA,...
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" "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" "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" "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" "Calculate Sender DE between: CAF_Low and Endothelial_High"
## [3] "Calculate Sender DE between: CAF_Low and CAF_High" "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 x 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)))
Process DE results:
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")
Combine sender-receiver DE based on L-R pairs:
如上所述，来自一种配体细胞类型的配体的 DE 确定为计算该细胞类型与其他生态位的所有配体细胞类型之间的 DE。 为了总结该细胞类型配体的 DE，有几个选择：可以采用平均 LFC，但也可以采用与其他生态位相比的最小 LFC。 建议使用最小 LFC，因为这是对配体表达的最强特异性测量，因为高 min LFC 意味着与所有生态位 2 的细胞类型相比，配体在生态位 1 的细胞类型中的表达更强（与 高平均 LFC，不排除生态位 2 中的一种或多种细胞类型也强烈表达该配体）。
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(针对有空间的数据)
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只添加区域 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。
在这个案例研究中，感兴趣的区域是肿瘤前沿，因为将该区域定义为对 pEMT 过程很重要。 还将 CAF 定义为靠近前缘的成纤维细胞，而其他成纤维细胞（肌成纤维细胞）不优先位于肿瘤前缘。因此，现在可以通过查看前沿成纤维细胞 (=CAF) 和非前沿成纤维细胞 (肌成纤维细胞) 之间的 DE 配体来进一步优先考虑成纤维细胞配体。
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 dataset
spatial_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))
}
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 pipeline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。 在这个案例研究中，与 pEMT 低肿瘤相比，pEMT 高生态位的geneset是 pEMT 高肿瘤中上调的基因，反之亦然。
还可以以不同的方式定义这些感兴趣的基因集！
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" "PRKCDBP" "IL8" "PTRF" "SEPP1" "C1orf186" "CCDC109B" "C10orf54"
## [9] "LEPREL1" "ZNF812" "LOC645638" "LOC401397" "LINC00162" "DFNA5" "PLK1S1" "ZMYM6NB"
## [17] "C19orf10" "CTSL1" "SQRDL" "LOC375295" "WBP5" "LOC100505633" "AIM1" "C1orf63"
## [25] "LOC100507463" "GPR115" "VIMP" "SEP15" "C1orf172" "NAPRT1" "LHFP" "KRT16P1"
## [33] "C7orf10" "PTPLA" "GRAMD3" "CPSF3L" "MESDC2" "C10orf10" "KIAA1609" "CCDC53"
## [41] "TXLNG2P" "NGFRAP1" "ERO1L" "FAM134A" "LSMD1" "TCEB2" "B3GALTL" "HN1L"
## [49] "LOC550643" "KIAA0922" "GLT25D1" "FAM127A" "C1orf151-NBL1" "SEPW1" "GPR126" "LOC100505806"
## [57] "LINC00478" "TCEB1" "GRAMD2" "GNB2L1" "KIRREL"
geneset_niche2 %>% setdiff(rownames(ligand_target_matrix))
## [1] "LOC344887" "AGPAT9" "C1orf110" "KIAA1467" "LOC100292680" "EPT1" "CT45A4" "LOC654433"
## [9] "UPK3BL" "LINC00340" "LOC100128338" "FAM60A" "CCDC144C" "LOC401109" "LOC286467" "LEPREL4"
## [17] "LOC731275" "LOC642236" "LINC00516" "LOC101101776" "SC5DL" "PVRL4" "LOC100130093" "LINC00338"
## [25] "LOC100132891" "PPAP2C" "C6orf1" "C2orf47" "WHSC1L1" "LOC100289019" "SETD8" "KDM5B-AS1"
## [33] "SPG20" "CXCR7" "LOC100216479" "LOC100505761" "MGC57346" "LPHN3" "CENPC1" "C11orf93"
## [41] "C14orf169" "LOC100506060" "FLJ31485" "LOC440905" "MLF1IP" "TMEM194A" "RRP7B" "REXO1L1"
## [49] "LOC100129269" "KIAA1715" "CTAGE5" "LOC202781" "LOC100506714" "LOC401164" "UTS2D" "LOC146880"
## [57] "KIAA1804" "C5orf55" "C21orf119" "PRUNE" "LRRC16A" "LOC339240" "FLJ35024" "C5orf28"
## [65] "LOC100505876" "MGC21881" "LOC100133985" "PPAPDC2" "FRG1B" "CECR5" "LOC100129361" "CCBL1"
## [73] "PTPLAD1" "MST4" "LOC550112" "LOC389791" "CCDC90A" "KIAA0195" "LOC100506469" "LOC100133161"
## [81] "LOC646719" "LOC728819" "BRE" "LOC284581" "LOC441081" "LOC728377" "LOC100134229" "C3orf65"
## [89] "SMEK2" "KIAA1737" "C17orf70" "PLEKHM1P" "LOC338758" "PCNXL2" "LOC91948" "C17orf89"
## [97] "LOC100505783" "SMCR7L" "C8orf4" "GPR56" "ATHL1" "LOC339535" "PPAPDC1B" "DAK"
## [105] "LOC100507173" "CRHR1-IT1" "PPAP2B" "ADCK4" "KIAA0146" "GYLTL1B" "LOC100272216" "LOC400027"
## [113] "WHSC1" "LOC100130855" "C7orf55" "C19orf40" "ADCK3" "C9orf142" "SGOL1" "LOC90834"
## [121] "PTPLAD2" "KIAA1967" "LOC100132352" "LOC100630918" "ADRBK2" "LINC00263" "FAM64A" "LOC401074"
## [129] "FAM179B" "RP1-177G6.2" "METTL21D" "ERO1LB" "FLJ45445" "NADKD1" "LOC100506233" "LOC100652772"
## [137] "FAM175A" "LINC00630" "C11orf82" "SETD5-AS1" "SGK196" "FLJ14186" "CCDC104" "FAM63A"
## [145] "NARG2" "MTERFD1" "CCDC74B-AS1" "LOC286186" "WDR67" "C12orf52" "FLJ30403" "KIAA2018"
## [153] "GCN1L1" "FLJ43681" "LOC152217" "FONG" "C18orf8" "ALG1L9P" "GTDC2" "LOC100507217"
## [161] "NBPF24" "WBSCR27" "C14orf1" "LOC284889" "KIAA0317" "FAM65A" "PMS2L2" "LUST"
## [169] "C15orf52" "FAM195A" "LOC399744" "PYCRL" "LOC338799" "LOC100506190" "C9orf91" "FLJ45340"
## [177] "LOC349196" "LOC100128881" "TOMM70A" "ALS2CR8" "LDOC1L" "HDGFRP3" "ZNF767" "LOC728558"
## [185] "LOC283693" "LEPREL2" "QTRTD1" "SELM" "C6orf25" "C1orf86" "HNRPLL" "LOC145820"
## [193] "LOC100289341" "C17orf85" "C3orf72" "C14orf64" "C9orf9" "LOC100506394"
length(geneset_niche1)
## [1] 1668
length(geneset_niche2)
## [1] 2889
在进行配体活性分析之前检查geneset中的基因数量。 建议在感兴趣的基因集中包含 20 到 1000 个基因，以及至少 5000 个基因的背景，以便进行适当的配体活性分析。 如果检索到的 DE 基因过多，建议使用更高的 lfc_cutoff 阈值。 如果有 > 2 个接收细胞/niche来比较并使用 min_lfc 作为特异性得分，建议使用 0.15 的截止值。 如果只有 2 个receiver/niche，建议使用更高的阈值（例如使用 0.25）。 如果有 Smart-seq2 等具有高测序深度的单细胞数据，建议也使用更高的阈值。
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" "PRKCDBP" "IL8" "PTRF" "SEPP1" "C1orf186"
geneset_niche2 %>% setdiff(rownames(ligand_target_matrix))
## [1] "LOC344887" "AGPAT9" "C1orf110" "KIAA1467" "LOC100292680" "EPT1" "CT45A4"
length(geneset_niche1)
## [1] 169
length(geneset_niche2)
## [1] 136
top_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)
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 函数进行了演示，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排名，但将有助于优先考虑每个配体最重要的受体。
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
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_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)
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]]$receiver) %>% head(10)
## # A tibble: 10 x 37
## niche receiver sender ligand_receptor ligand receptor bonafide ligand_score ligand_signific~ ligand_present ligand_expressi~
## <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <dbl> <dbl> <dbl> <dbl>
## 1 pEMT_High_niche Malignan~ T.cel~ PTPRC--MET PTPRC MET FALSE 3.22 1 1 9.32
## 2 pEMT_High_niche Malignan~ T.cel~ PTPRC--EGFR PTPRC EGFR FALSE 3.22 1 1 9.32
## 3 pEMT_High_niche Malignan~ T.cel~ PTPRC--CD44 PTPRC CD44 FALSE 3.22 1 1 9.32
## 4 pEMT_High_niche Malignan~ T.cel~ PTPRC--ERBB2 PTPRC ERBB2 FALSE 3.22 1 1 9.32
## 5 pEMT_High_niche Malignan~ T.cel~ PTPRC--IFNAR1 PTPRC IFNAR1 FALSE 3.22 1 1 9.32
## 6 pEMT_High_niche Malignan~ T.cel~ TNF--TNFRSF21 TNF TNFRSF21 TRUE 1.74 1 1 2.34
## 7 pEMT_High_niche Malignan~ Myelo~ SERPINA1--LRP1 SERPI~ LRP1 TRUE 2.52 1 1 4.83
## 8 pEMT_High_niche Malignan~ Myelo~ IL1B--IL1RAP IL1B IL1RAP TRUE 1.50 1 1 1.93
## 9 pEMT_High_niche Malignan~ Myelo~ IL1RN--IL1R2 IL1RN IL1R2 TRUE 1.62 1 1 2.07
## 10 pEMT_High_niche Malignan~ T.cel~ PTPRC--INSR PTPRC INSR FALSE 3.22 1 1 9.32
## # ... with 26 more variables: ligand_expression_scaled <dbl>, ligand_fraction <dbl>, ligand_score_spatial <dbl>, receptor_score <dbl>,
## # receptor_significant <dbl>, receptor_present <dbl>, receptor_expression <dbl>, receptor_expression_scaled <dbl>,
## # receptor_fraction <dbl>, receptor_score_spatial <dbl>, ligand_scaled_receptor_expression_fraction <dbl>,
## # avg_score_ligand_receptor <dbl>, activity <dbl>, activity_normalized <dbl>, scaled_ligand_score <dbl>,
## # scaled_ligand_expression_scaled <dbl>, scaled_receptor_score <dbl>, scaled_receptor_expression_scaled <dbl>,
## # scaled_avg_score_ligand_receptor <dbl>, scaled_ligand_score_spatial <dbl>, scaled_receptor_score_spatial <dbl>,
## # scaled_ligand_fraction_adapted <dbl>, scaled_receptor_fraction_adapted <dbl>, scaled_activity <dbl>, ...
prioritization_tables$prioritization_tbl_ligand_target %>% filter(receiver == niches[[1]]$receiver) %>% head(10)
## # A tibble: 10 x 20
## niche receiver sender ligand_receptor ligand receptor bonafide target target_score target_signific~ target_present target_expressi~
## <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 pEMT_H~ Malignan~ T.cell~ PTPRC--MET PTPRC MET FALSE EHF 1.04 1 1 1.88
## 2 pEMT_H~ Malignan~ T.cell~ PTPRC--MET PTPRC MET FALSE GADD4~ 0.836 1 1 2.42
## 3 pEMT_H~ Malignan~ T.cell~ PTPRC--MET PTPRC MET FALSE SERPI~ 0.889 1 1 1.79
## 4 pEMT_H~ Malignan~ T.cell~ PTPRC--EGFR PTPRC EGFR FALSE EHF 1.04 1 1 1.88
## 5 pEMT_H~ Malignan~ T.cell~ PTPRC--EGFR PTPRC EGFR FALSE GADD4~ 0.836 1 1 2.42
## 6 pEMT_H~ Malignan~ T.cell~ PTPRC--EGFR PTPRC EGFR FALSE SERPI~ 0.889 1 1 1.79
## 7 pEMT_H~ Malignan~ T.cell~ PTPRC--CD44 PTPRC CD44 FALSE EHF 1.04 1 1 1.88
## 8 pEMT_H~ Malignan~ T.cell~ PTPRC--CD44 PTPRC CD44 FALSE GADD4~ 0.836 1 1 2.42
## 9 pEMT_H~ Malignan~ T.cell~ PTPRC--CD44 PTPRC CD44 FALSE SERPI~ 0.889 1 1 1.79
## 10 pEMT_H~ Malignan~ T.cell~ PTPRC--ERBB2 PTPRC ERBB2 FALSE EHF 1.04 1 1 1.88
## # ... with 8 more variables: target_expression_scaled <dbl>, target_fraction <dbl>, ligand_target_weight <dbl>, activity <dbl>,
## # activity_normalized <dbl>, scaled_activity <dbl>, scaled_activity_normalized <dbl>, prioritization_score <dbl>
prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(receiver == niches[[2]]$receiver) %>% head(10)
## # A tibble: 10 x 37
## niche receiver sender ligand_receptor ligand receptor bonafide ligand_score ligand_signific~ ligand_present ligand_expressi~
## <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <dbl> <dbl> <dbl> <dbl>
## 1 pEMT_Low_niche Malignan~ Endoth~ F8--LRP1 F8 LRP1 TRUE 0.952 1 1 2.17
## 2 pEMT_Low_niche Malignan~ Endoth~ PLAT--LRP1 PLAT LRP1 TRUE 0.913 1 1 2.70
## 3 pEMT_Low_niche Malignan~ CAF_Low FGF10--FGFR2 FGF10 FGFR2 TRUE 0.385 0.8 1 1.07
## 4 pEMT_Low_niche Malignan~ CAF_Low NLGN2--NRXN3 NLGN2 NRXN3 TRUE 0.140 0.2 1 0.269
## 5 pEMT_Low_niche Malignan~ CAF_Low RSPO3--LGR6 RSPO3 LGR6 TRUE 0.557 0.8 1 1.27
## 6 pEMT_Low_niche Malignan~ CAF_Low COMP--SDC1 COMP SDC1 TRUE 0.290 0.8 1 1.27
## 7 pEMT_Low_niche Malignan~ CAF_Low SEMA3C--NRP2 SEMA3C NRP2 TRUE 0.652 1 1 1.73
## 8 pEMT_Low_niche Malignan~ CAF_Low SLIT2--SDC1 SLIT2 SDC1 TRUE 0.494 1 1 0.846
## 9 pEMT_Low_niche Malignan~ Endoth~ IL33--IL1RAP IL33 IL1RAP FALSE 1.34 1 1 2.75
## 10 pEMT_Low_niche Malignan~ CAF_Low C3--LRP1 C3 LRP1 TRUE 0.480 1 1 4.79
## # ... with 26 more variables: ligand_expression_scaled <dbl>, ligand_fraction <dbl>, ligand_score_spatial <dbl>, receptor_score <dbl>,
## # receptor_significant <dbl>, receptor_present <dbl>, receptor_expression <dbl>, receptor_expression_scaled <dbl>,
## # receptor_fraction <dbl>, receptor_score_spatial <dbl>, ligand_scaled_receptor_expression_fraction <dbl>,
## # avg_score_ligand_receptor <dbl>, activity <dbl>, activity_normalized <dbl>, scaled_ligand_score <dbl>,
## # scaled_ligand_expression_scaled <dbl>, scaled_receptor_score <dbl>, scaled_receptor_expression_scaled <dbl>,
## # scaled_avg_score_ligand_receptor <dbl>, scaled_ligand_score_spatial <dbl>, scaled_receptor_score_spatial <dbl>,
## # scaled_ligand_fraction_adapted <dbl>, scaled_receptor_fraction_adapted <dbl>, scaled_activity <dbl>, ...
prioritization_tables$prioritization_tbl_ligand_target %>% filter(receiver == niches[[2]]$receiver) %>% head(10)
## # A tibble: 10 x 20
## niche receiver sender ligand_receptor ligand receptor bonafide target target_score target_signific~ target_present target_expressi~
## <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 pEMT_L~ Malignan~ Endoth~ F8--LRP1 F8 LRP1 TRUE ETV4 0.771 1 1 1.00
## 2 pEMT_L~ Malignan~ Endoth~ PLAT--LRP1 PLAT LRP1 TRUE CLDN7 0.835 1 1 2.30
## 3 pEMT_L~ Malignan~ Endoth~ PLAT--LRP1 PLAT LRP1 TRUE ETV4 0.771 1 1 1.00
## 4 pEMT_L~ Malignan~ CAF_Low FGF10--FGFR2 FGF10 FGFR2 TRUE ETV4 0.771 1 1 1.00
## 5 pEMT_L~ Malignan~ CAF_Low FGF10--FGFR2 FGF10 FGFR2 TRUE WNT5A 1.40 1 1 2.01
## 6 pEMT_L~ Malignan~ CAF_Low NLGN2--NRXN3 NLGN2 NRXN3 TRUE CLDN5 0.979 1 1 0.991
## 7 pEMT_L~ Malignan~ CAF_Low NLGN2--NRXN3 NLGN2 NRXN3 TRUE ETV4 0.771 1 1 1.00
## 8 pEMT_L~ Malignan~ CAF_Low RSPO3--LGR6 RSPO3 LGR6 TRUE DDC 0.832 1 1 0.785
## 9 pEMT_L~ Malignan~ CAF_Low RSPO3--LGR6 RSPO3 LGR6 TRUE EGFL7 0.763 1 1 1.09
## 10 pEMT_L~ Malignan~ CAF_Low COMP--SDC1 COMP SDC1 TRUE CLDN7 0.835 1 1 2.30
## # ... with 8 more variables: target_expression_scaled <dbl>, target_fraction <dbl>, ligand_target_weight <dbl>, activity <dbl>,
## # activity_normalized <dbl>, scaled_activity <dbl>, scaled_activity_normalized <dbl>, prioritization_score <dbl>
8. Visualization of the Differential NicheNet output
Differential expression of ligand and expression
在可视化之前，需要定义每个生态位中最重要的配体-受体对。 将通过首先确定每个配体/配体-受体对在哪个生态位上找到最高分来做到这一点。 然后获得每个niche的前 50 个配体。
top_ligand_niche_df = prioritization_tables$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
Now 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$prioritization_tbl_ligand_receptor %>% filter(ligand %in% filtered_ligands) %>% select(niche, sender, receiver, ligand, receptor, ligand_receptor, prioritization_score) %>% distinct() %>% inner_join(top_ligand_receptor_niche_df) %>% group_by(ligand) %>% filter(receiver == receiver_oi) %>% top_n(2, prioritization_score) %>% ungroup()
lfc_plot = make_ligand_receptor_lfc_plot(receiver_oi, prioritized_tbl_oi, prioritization_tables$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$prioritization_tbl_ligand_receptor, ligand_spatial = include_spatial_info_sender, receptor_spatial = include_spatial_info_receiver, plot_legend = FALSE, heights = NULL, widths = NULL)
lfc_plot_spatial
Ligand expression, activity and target genes
Active target gene inference - cf Default NicheNet
Now: visualization of ligand activity and ligand-target links
exprs_activity_target_plot = make_ligand_activity_target_exprs_plot(receiver_oi, prioritized_tbl_oi, prioritization_tables$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
基于此图，可以推断出许多假设，例如：“有趣的是，IL1 家族配体似乎具有诱导高 pEMT 和低 pEMT 恶性细胞之间的 DE 基因的活性； 它们主要由骨髓细胞表达，这是一种 pEMT 高肿瘤特有的细胞类型”。
重要：具有高差异表达（或条件特异性）和配体活性（=靶基因富集）的配体-受体对是非常有趣的预测，作为您感兴趣的细胞间通讯过程的关键调节因子！
如果该图包含太多信息，因为查看了许多匹配项（前 50 个配体），您当然也可以为较少的配体制作此图，例如前 20 个配体。
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
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)
Interpretation of these results
大多数排名靠前的差异 L-R 对似乎来自仅存在于 pEMT 高肿瘤中的细胞类型。这可能部分是由于生物学（一种情况下的独特细胞类型可能非常重要），但也可能是由于优先排序的方式以及这些独特的细胞类型在其中没有“对应物”的事实其他niche。
因为骨髓细胞和T细胞与肿瘤微环境中的其他细胞有很大不同，它们的配体会表现出强烈的差异表达。与来自相同细胞类型但不同生态位/条件的细胞之间的差异表达（pEMT-high 中的 CAF 与 pEMT 中的 CAF pEMT-低）。所以结论：Differential NicheNet 的一个优势在于它可以处理特定条件的细胞类型，但用户应该知道最终的一般分数可能会偏向于特定条件的发送者细胞类型。因此，如果有一个案例研究，其中某些发送细胞类型是特定于条件的，建议还查看每个发送细胞类型的top LR 对。
Visualization for the other condition: pEMT-low
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
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