在这篇短文中，您可以学习如何在Seurat v3对象上执行基本的NicheNet分析，以及如何在circos图中可视化输出。值得注意的是，我们作为NicheNet的开发者，通常推荐通过结合几个热图(配体活性、配体-靶标链接、配体-受体链接、配体表达、配体LFC等)来可视化输出，而不是使用circos图可视化。无奈使用圈图可视化的需求太多了，所以有了这篇教程。
作为细胞相互作用的例子，我们将使用Medaglia等人的小鼠NICHE-seq数据来探索淋巴细胞脉络丛脑膜炎病毒(LCMV)感染前和72小时后腹股沟淋巴结T细胞区域的细胞间通讯[见@ medagli_spatial_2017]。我们将利用NicheNet来探索免疫细胞对这种LCMV感染的反应。在本数据集中，观察到稳定状态下的CD8 T细胞和LCMV感染后的CD8 T细胞之间的差异表达。NicheNet可以应用于观察淋巴结中的几种免疫细胞群(如单核细胞、树突状细胞、NK细胞、B细胞、CD4 T细胞)如何调节和诱导这些观察到的基因表达变化。NicheNet将特别优先考虑来自这些免疫细胞的配体及其在LCMV感染后表达改变的靶基因。
装载所需的包，用处理过的相互作用细胞的表达数据和NicheNet的配体-靶先验模型、配体-受体网络和加权综合网络读取Seurat对象。
#devtools::install_github("saeyslab/nichenetr")
library(nichenetr)
library(Seurat)
library(tidyverse)
library(circlize)
ligand_target_matrix = readRDS(url("https://zenodo.org/record/3260758/files/ligand_target_matrix.rds"))
ligand_target_matrix[1:5,1:5]
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"))
> head(lr_network)
# A tibble: 6 x 4
from to source database
1 CXCL1 CXCR2 kegg_cytokines kegg
2 CXCL2 CXCR2 kegg_cytokines kegg
3 CXCL3 CXCR2 kegg_cytokines kegg
4 CXCL5 CXCR2 kegg_cytokines kegg
5 PPBP CXCR2 kegg_cytokines kegg
6 CXCL6 CXCR2 kegg_cytokines kegg
weighted_networks = readRDS(url("https://zenodo.org/record/3260758/files/weighted_networks.rds"))
head(weighted_networks$lr_sig) # interactions and their weights in the ligand-receptor + signaling network
# A tibble: 6 x 3
from to weight
1 A1BG ABCC6 0.422
2 A1BG ACE2 0.101
3 A1BG ADAM10 0.0970
4 A1BG AGO1 0.0525
5 A1BG AKT1 0.0855
6 A1BG ANXA7 0.457
seuratObj = readRDS(url("https://zenodo.org/record/3531889/files/seuratObj.rds"))
[email protected] %>% head()
## nGene nUMI orig.ident aggregate res.0.6 celltype nCount_RNA nFeature_RNA
## W380370 880 1611 LN_SS SS 1 CD8 T 1607 876
## W380372 541 891 LN_SS SS 0 CD4 T 885 536
## W380374 742 1229 LN_SS SS 0 CD4 T 1223 737
## W380378 847 1546 LN_SS SS 1 CD8 T 1537 838
## W380379 839 1606 LN_SS SS 0 CD4 T 1603 836
## W380381 517 844 LN_SS SS 0 CD4 T 840 513
seuratObj
An object of class Seurat
13541 features across 5027 samples within 1 assay
Active assay: RNA (13541 features, 1575 variable features)
4 dimensional reductions calculated: cca, cca.aligned, tsne, pca
观察存在哪些细胞群:CD4 T细胞(包括调节性T细胞)、CD8 T细胞、B细胞、NK细胞、树突状细胞(DCs)和炎症单核细胞
[email protected]$celltype %>% table() # note that the number of cells of some cell types is very low and should preferably be higher for a real application
## .
## B CD4 T CD8 T DC Mono NK Treg
## 382 2562 1645 18 90 131 199
DimPlot(seuratObj, reduction = "tsne",label = T)+ theme_bw()
[email protected]$aggregate %>% table()
## .
## LCMV SS
## 3886 1141
DimPlot(seuratObj, reduction = "tsne", group.by = "aggregate")
对Seurat对象的NicheNet分析:解释两种条件的差异表达。在这个案例研究中，接收细胞群是' CD8 T '细胞群，而发送细胞群是' CD4 T '， ' Treg '， ' Mono '， ' NK '， ' B '和' DC '。以上描述的功能将考虑一个基因在至少一个集群中预定义的部分细胞(默认为10%)中表达时的表达。我们感兴趣的基因是LCMV感染后CD8 T细胞中差异表达的基因。因此，感兴趣的条件是“LCMV”，而参考/稳态条件是“SS”。条件的信息可以从元数据列“aggregate”中提取出来，计算差异基因的方法是标准的Seurat Wilcoxon检验。
用于预测活性靶基因和构建活性配体-受体网络的配体的数量默认为20个。
# indicated cell types should be cell class identities
# check via:
# seuratObj %>% Idents() %>% table()
sender_celltypes = c("CD4 T","Treg", "Mono", "NK", "B", "DC")
nichenet_output = nichenet_seuratobj_aggregate(
seurat_obj = seuratObj,
receiver = "CD8 T",
condition_colname = "aggregate", condition_oi = "LCMV", condition_reference = "SS",
sender = sender_celltypes,
ligand_target_matrix = ligand_target_matrix, lr_network = lr_network, weighted_networks = weighted_networks, organism = "mouse")
## [1] "Read in and process NicheNet's networks"
## [1] "Define expressed ligands and receptors in receiver and sender cells"
## [1] "Perform DE analysis in receiver cell"
## [1] "Perform NicheNet ligand activity analysis"
## [1] "Infer active target genes of the prioritized ligands"
## [1] "Infer receptors of the prioritized ligands"
# 输出的是一个列表：
nichenet_output %>% names()
[1] "ligand_activities" "top_ligands" "top_targets"
[4] "top_receptors" "ligand_target_matrix" "ligand_target_heatmap"
[7] "ligand_target_df" "ligand_activity_target_heatmap" "ligand_receptor_matrix"
[10] "ligand_receptor_heatmap" "ligand_receptor_df" "ligand_receptor_matrix_bonafide"
[13] "ligand_receptor_heatmap_bonafide" "ligand_receptor_df_bonafide" "geneset_oi"
[16] "background_expressed_genes"
配体活性分析结果。NicheNet做的第一件事，是根据预测的配体活性来确定配体的优先级。使用如下命令查看这些配体的排名:
nichenet_output$ligand_activities
## # A tibble: 44 x 6
## test_ligand auroc aupr pearson rank bona_fide_ligand
##
## 1 Ebi3 0.662 0.238 0.219 1 FALSE
## 2 Il15 0.596 0.160 0.109 2 TRUE
## 3 Crlf2 0.560 0.160 0.0890 3 FALSE
## 4 App 0.499 0.134 0.0750 4 TRUE
## 5 Tgfb1 0.498 0.134 0.0631 5 TRUE
## 6 Ptprc 0.539 0.142 0.0602 6 TRUE
## 7 H2-M3 0.526 0.149 0.0533 7 TRUE
## 8 Icam1 0.544 0.134 0.0496 8 TRUE
## 9 Cxcl10 0.536 0.134 0.0457 9 TRUE
## 10 Adam17 0.517 0.129 0.0378 10 TRUE
## # ... with 34 more rows
这些配体由一个或多个输入发送细胞表达。要看哪个细胞群表达了这些配体，你可以运行以下程序:
DotPlot(seuratObj, features = nichenet_output$top_ligands %>% rev(), cols = "RdYlBu") + RotatedAxis()
如你所见，大多数排名靠前的op配体似乎主要由树突状细胞和单核细胞表达。
观察这些配体在LCMV感染后是否有差异表达也是很有趣的。
DotPlot(seuratObj, features = nichenet_output$top_ligands %>% rev(), split.by = "aggregate") + RotatedAxis()
VlnPlot(seuratObj, features = c("Il15", "Cxcl10","Cxcl16"), split.by = "aggregate", pt.size = 0, combine = T)
## [[1]]
image.png
推断活跃的配体-靶标连接
NicheNet还推断出这些顶级配体的活性靶基因。要查看哪个顶级配体被预测调控了哪些差异表达基因的表达，可以运行以下命令来查看热图:
nichenet_output$ligand_target_heatmap
Circos绘图来可视化配体-靶标和配体-受体的相互作用。这一可视化分组根据最强表达的细胞类型预测活性配体。因此，我们需要确定每种细胞类型，它们表达的配体比其他细胞类型更强。计算发送细胞中平均配体表达量。
# avg_expression_ligands = AverageExpression(seuratObj %>% subset(subset = aggregate == "LCMV"),features = nichenet_output$top_ligands) # if want to look specifically in LCMV-only cells
avg_expression_ligands = AverageExpression(seuratObj, features = nichenet_output$top_ligands)
分配配体给发送细胞
为了给发送端细胞类型分配配体，我们可以查找哪个发送端细胞类型的表达式高于平均值+ SD。
sender_ligand_assignment = avg_expression_ligands$RNA %>% apply(1, function(ligand_expression){
ligand_expression > (ligand_expression %>% mean() + ligand_expression %>% sd())
}) %>% t()
sender_ligand_assignment[1:4,1:4]
CD8 T CD4 T Treg B
Ebi3 FALSE FALSE FALSE FALSE
Il15 FALSE FALSE FALSE FALSE
Crlf2 FALSE FALSE FALSE FALSE
App FALSE FALSE FALSE FALSE
sender_ligand_assignment = sender_ligand_assignment %>% apply(2, function(x){x[x == TRUE]}) %>% purrr::keep(function(x){length(x) > 0})
names(sender_ligand_assignment)
## [1] "B" "NK" "Mono" "DC"
(sender_ligand_assignment)
$B
H2-M3
TRUE
$NK
Ptprc Itgb1
TRUE TRUE
$Mono
Ebi3 Crlf2 App Tgfb1 Cxcl10 Adam17 Cxcl11 Cxcl9 Sema4d C3 Anxa1
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
$DC
Il15 Icam1 H2-T23 Ccl5 Cxcl16 Itgb1
TRUE TRUE TRUE TRUE TRUE TRUE
顶部的配体似乎在B细胞、NK细胞、单核细胞和DCs中表达最强烈。我们也会知道在多种细胞类型中哪些配体是常见的(=特定于> 1细胞类型的配体，或在之前的代码块中未指定给某个细胞类型的配体)。现在确定哪些优先配体是由CAFs或内皮细胞表达的。
all_assigned_ligands = sender_ligand_assignment %>% lapply(function(x){names(x)}) %>% unlist()
unique_ligands = all_assigned_ligands %>% table() %>% .[. == 1] %>% names()
general_ligands = nichenet_output$top_ligands %>% setdiff(unique_ligands)
B_specific_ligands = sender_ligand_assignment$B %>% names() %>% setdiff(general_ligands)
NK_specific_ligands = sender_ligand_assignment$NK %>% names() %>% setdiff(general_ligands)
Mono_specific_ligands = sender_ligand_assignment$Mono %>% names() %>% setdiff(general_ligands)
DC_specific_ligands = sender_ligand_assignment$DC %>% names() %>% setdiff(general_ligands)
ligand_type_indication_df = tibble(
ligand_type = c(rep("B-specific", times = B_specific_ligands %>% length()),
rep("NK-specific", times = NK_specific_ligands %>% length()),
rep("Mono-specific", times = Mono_specific_ligands %>% length()),
rep("DC-specific", times = DC_specific_ligands %>% length()),
rep("General", times = general_ligands %>% length())),
ligand = c(B_specific_ligands, NK_specific_ligands, Mono_specific_ligands, DC_specific_ligands, general_ligands))
ligand_type_indication_df %>% head
# A tibble: 6 x 2
ligand_type ligand
1 B-specific H2-M3
2 NK-specific Ptprc
3 Mono-specific Ebi3
4 Mono-specific Crlf2
5 Mono-specific App
6 Mono-specific Tgfb1
定义感兴趣的配体-目标链接
为了避免circos图中有太多配体目标链接，我们将只显示权重高于预定义截止值的链接:属于最低分数的40%的链接被删除。这并不是说用于这种可视化的边界和其他边界可以根据用户的需要进行更改。
active_ligand_target_links_df = nichenet_output$ligand_target_df %>% mutate(target_type = "LCMV-DE") %>% inner_join(ligand_type_indication_df) # if you want ot make circos plots for multiple gene sets, combine the different data frames and differentiate which target belongs to which gene set via the target type
cutoff_include_all_ligands = active_ligand_target_links_df$weight %>% quantile(0.40)
active_ligand_target_links_df_circos = active_ligand_target_links_df %>% filter(weight > cutoff_include_all_ligands)
ligands_to_remove = setdiff(active_ligand_target_links_df$ligand %>% unique(), active_ligand_target_links_df_circos$ligand %>% unique())
targets_to_remove = setdiff(active_ligand_target_links_df$target %>% unique(), active_ligand_target_links_df_circos$target %>% unique())
circos_links = active_ligand_target_links_df %>% filter(!target %in% targets_to_remove &!ligand %in% ligands_to_remove)
circos_links
# A tibble: 124 x 5
ligand target weight target_type ligand_type
1 Ebi3 Cd274 0.00325 LCMV-DE Mono-specific
2 Ebi3 Cd53 0.00321 LCMV-DE Mono-specific
3 Ebi3 Ddit4 0.00335 LCMV-DE Mono-specific
4 Ebi3 Id3 0.00373 LCMV-DE Mono-specific
5 Ebi3 Ifit3 0.00320 LCMV-DE Mono-specific
6 Ebi3 Irf1 0.00692 LCMV-DE Mono-specific
7 Ebi3 Irf7 0.00312 LCMV-DE Mono-specific
8 Ebi3 Irf9 0.00543 LCMV-DE Mono-specific
9 Ebi3 Parp14 0.00336 LCMV-DE Mono-specific
10 Ebi3 Pdcd4 0.00335 LCMV-DE Mono-specific
# ... with 114 more rows
准备circos可视化:给每个片段配体和目标特定的颜色和顺序
grid_col_ligand =c("General" = "lawngreen",
"NK-specific" = "royalblue",
"B-specific" = "darkgreen",
"Mono-specific" = "violet",
"DC-specific" = "steelblue2")
grid_col_target =c(
"LCMV-DE" = "tomato")
grid_col_tbl_ligand = tibble(ligand_type = grid_col_ligand %>% names(), color_ligand_type = grid_col_ligand)
grid_col_tbl_target = tibble(target_type = grid_col_target %>% names(), color_target_type = grid_col_target)
circos_links = circos_links %>% mutate(ligand = paste(ligand," ")) # extra space: make a difference between a gene as ligand and a gene as target!
circos_links = circos_links %>% inner_join(grid_col_tbl_ligand) %>% inner_join(grid_col_tbl_target)
links_circle = circos_links %>% select(ligand,target, weight)
ligand_color = circos_links %>% distinct(ligand,color_ligand_type)
grid_ligand_color = ligand_color$color_ligand_type %>% set_names(ligand_color$ligand)
target_color = circos_links %>% distinct(target,color_target_type)
grid_target_color = target_color$color_target_type %>% set_names(target_color$target)
grid_col =c(grid_ligand_color,grid_target_color)
# give the option that links in the circos plot will be transparant ~ ligand-target potential score
transparency = circos_links %>% mutate(weight =(weight-min(weight))/(max(weight)-min(weight))) %>% mutate(transparency = 1-weight) %>% .$transparency
准备可视化的circos:排序配体和目标
target_order = circos_links$target %>% unique()
ligand_order = c(Mono_specific_ligands, DC_specific_ligands, NK_specific_ligands,B_specific_ligands, general_ligands) %>% c(paste(.," ")) %>% intersect(circos_links$ligand)
order = c(ligand_order,target_order)
准备circos可视化:定义不同片段之间的间隙
width_same_cell_same_ligand_type = 0.5
width_different_cell = 6
width_ligand_target = 15
width_same_cell_same_target_type = 0.5
gaps = c(
# width_ligand_target,
rep(width_same_cell_same_ligand_type, times = (circos_links %>% filter(ligand_type == "Mono-specific") %>% distinct(ligand) %>% nrow() -1)),
width_different_cell,
rep(width_same_cell_same_ligand_type, times = (circos_links %>% filter(ligand_type == "DC-specific") %>% distinct(ligand) %>% nrow() -1)),
width_different_cell,
rep(width_same_cell_same_ligand_type, times = (circos_links %>% filter(ligand_type == "NK-specific") %>% distinct(ligand) %>% nrow() -1)),
width_different_cell,
rep(width_same_cell_same_ligand_type, times = (circos_links %>% filter(ligand_type == "B-specific") %>% distinct(ligand) %>% nrow() -1)),
width_different_cell,
rep(width_same_cell_same_ligand_type, times = (circos_links %>% filter(ligand_type == "General") %>% distinct(ligand) %>% nrow() -1)),
width_ligand_target,
rep(width_same_cell_same_target_type, times = (circos_links %>% filter(target_type == "LCMV-DE") %>% distinct(target) %>% nrow() -1)),
width_ligand_target
)
渲染circos的情节(所有链接相同的透明度)。只有表明每个靶基因的阻滞的宽度与配体-靶的调控电位成正比(~支持调控相互作用的先验知识)。
circos.par(gap.degree = gaps)
chordDiagram(links_circle, directional = 1,order=order,link.sort = TRUE, link.decreasing = FALSE, grid.col = grid_col,transparency = 0, diffHeight = 0.005, direction.type = c("diffHeight", "arrows"),link.arr.type = "big.arrow", link.visible = links_circle$weight >= cutoff_include_all_ligands,annotationTrack = "grid",
preAllocateTracks = list(track.height = 0.075))
# we go back to the first track and customize sector labels
circos.track(track.index = 1, panel.fun = function(x, y) {
circos.text(CELL_META$xcenter, CELL_META$ylim[1], CELL_META$sector.index,
facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.55), cex = 1)
}, bg.border = NA) #
circos.clear()
绘制circos图(透明度由配体-靶标相互作用的调控潜力决定)
circos.par(gap.degree = gaps)
chordDiagram(links_circle, directional = 1,order=order,link.sort = TRUE, link.decreasing = FALSE, grid.col = grid_col,transparency = transparency, diffHeight = 0.005, direction.type = c("diffHeight", "arrows"),link.arr.type = "big.arrow", link.visible = links_circle$weight >= cutoff_include_all_ligands,annotationTrack = "grid",
preAllocateTracks = list(track.height = 0.075))
# we go back to the first track and customize sector labels
circos.track(track.index = 1, panel.fun = function(x, y) {
circos.text(CELL_META$xcenter, CELL_META$ylim[1], CELL_META$sector.index,
facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.55), cex = 1)
}, bg.border = NA) #
circos.clear()
svg("ligand_target_circos.svg", width = 10, height = 10)
circos.par(gap.degree = gaps)
chordDiagram(links_circle, directional = 1,order=order,link.sort = TRUE, link.decreasing = FALSE, grid.col = grid_col,transparency = transparency, diffHeight = 0.005, direction.type = c("diffHeight", "arrows"),link.arr.type = "big.arrow", link.visible = links_circle$weight >= cutoff_include_all_ligands,annotationTrack = "grid",
preAllocateTracks = list(track.height = 0.075))
# we go back to the first track and customize sector labels
circos.track(track.index = 1, panel.fun = function(x, y) {
circos.text(CELL_META$xcenter, CELL_META$ylim[1], CELL_META$sector.index,
facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.55), cex = 1)
}, bg.border = NA) #
circos.clear()
dev.off()
在circos图中可视化优先配体与受体的相互作用
lr_network_top_df = nichenet_output$ligand_receptor_df %>% mutate(receptor_type = "LCMV_CD8T_receptor") %>% inner_join(ligand_type_indication_df)
grid_col_ligand =c("General" = "lawngreen",
"NK-specific" = "royalblue",
"B-specific" = "darkgreen",
"Mono-specific" = "violet",
"DC-specific" = "steelblue2")
grid_col_receptor =c(
"LCMV_CD8T_receptor" = "darkred")
grid_col_tbl_ligand = tibble(ligand_type = grid_col_ligand %>% names(), color_ligand_type = grid_col_ligand)
grid_col_tbl_receptor = tibble(receptor_type = grid_col_receptor %>% names(), color_receptor_type = grid_col_receptor)
circos_links = lr_network_top_df %>% mutate(ligand = paste(ligand," ")) # extra space: make a difference between a gene as ligand and a gene as receptor!
circos_links = circos_links %>% inner_join(grid_col_tbl_ligand) %>% inner_join(grid_col_tbl_receptor)
links_circle = circos_links %>% select(ligand,receptor, weight)
ligand_color = circos_links %>% distinct(ligand,color_ligand_type)
grid_ligand_color = ligand_color$color_ligand_type %>% set_names(ligand_color$ligand)
receptor_color = circos_links %>% distinct(receptor,color_receptor_type)
grid_receptor_color = receptor_color$color_receptor_type %>% set_names(receptor_color$receptor)
grid_col =c(grid_ligand_color,grid_receptor_color)
# give the option that links in the circos plot will be transparant ~ ligand-receptor potential score
transparency = circos_links %>% mutate(weight =(weight-min(weight))/(max(weight)-min(weight))) %>% mutate(transparency = 1-weight) %>% .$transparency
制备可视化的circos:有序配体和受体
receptor_order = circos_links$receptor %>% unique()
ligand_order = c(Mono_specific_ligands, DC_specific_ligands, NK_specific_ligands,B_specific_ligands, general_ligands) %>% c(paste(.," ")) %>% intersect(circos_links$ligand)
order = c(ligand_order,receptor_order)
准备马戏团可视化:定义不同片段之间的间隙
width_same_cell_same_ligand_type = 0.5
width_different_cell = 6
width_ligand_receptor = 15
width_same_cell_same_receptor_type = 0.5
gaps = c(
# width_ligand_target,
rep(width_same_cell_same_ligand_type, times = (circos_links %>% filter(ligand_type == "Mono-specific") %>% distinct(ligand) %>% nrow() -1)),
width_different_cell,
rep(width_same_cell_same_ligand_type, times = (circos_links %>% filter(ligand_type == "DC-specific") %>% distinct(ligand) %>% nrow() -1)),
width_different_cell,
rep(width_same_cell_same_ligand_type, times = (circos_links %>% filter(ligand_type == "NK-specific") %>% distinct(ligand) %>% nrow() -1)),
width_different_cell,
rep(width_same_cell_same_ligand_type, times = (circos_links %>% filter(ligand_type == "B-specific") %>% distinct(ligand) %>% nrow() -1)),
width_different_cell,
rep(width_same_cell_same_ligand_type, times = (circos_links %>% filter(ligand_type == "General") %>% distinct(ligand) %>% nrow() -1)),
width_ligand_receptor,
rep(width_same_cell_same_receptor_type, times = (circos_links %>% filter(receptor_type == "LCMV_CD8T_receptor") %>% distinct(receptor) %>% nrow() -1)),
width_ligand_receptor
)
渲染马戏团的情节(所有链接相同的透明度)。只有表明每个受体的阻滞的宽度与配体-受体相互作用的重量成比例(~支持相互作用的先验知识)。
circos.par(gap.degree = gaps)
chordDiagram(links_circle, directional = 1,order=order,link.sort = TRUE, link.decreasing = FALSE, grid.col = grid_col,transparency = 0, diffHeight = 0.005, direction.type = c("diffHeight", "arrows"),link.arr.type = "big.arrow", link.visible = links_circle$weight >= cutoff_include_all_ligands,annotationTrack = "grid",
preAllocateTracks = list(track.height = 0.075))
# we go back to the first track and customize sector labels
circos.track(track.index = 1, panel.fun = function(x, y) {
circos.text(CELL_META$xcenter, CELL_META$ylim[1], CELL_META$sector.index,
facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.55), cex = 0.8)
}, bg.border = NA) #
circos.clear()
渲染circos图(透明程度由配体-受体相互作用的先验相互作用权重决定——正如指示每个受体的块的宽度)
circos.par(gap.degree = gaps)
chordDiagram(links_circle, directional = 1,order=order,link.sort = TRUE, link.decreasing = FALSE, grid.col = grid_col,transparency = transparency, diffHeight = 0.005, direction.type = c("diffHeight", "arrows"),link.arr.type = "big.arrow", link.visible = links_circle$weight >= cutoff_include_all_ligands,annotationTrack = "grid",
preAllocateTracks = list(track.height = 0.075))
# we go back to the first track and customize sector labels
circos.track(track.index = 1, panel.fun = function(x, y) {
circos.text(CELL_META$xcenter, CELL_META$ylim[1], CELL_META$sector.index,
facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.55), cex = 0.8)
}, bg.border = NA) #
circos.clear()
Perform NicheNet analysis starting from a Seurat object:vignette("seurat_wrapper", package="nichenetr")
Circos plot visualization to show active ligand-target links between interacting cells:
https://github.com/saeyslab/nichenetr/blob/master/vignettes/seurat_wrapper_circos.md
https://github.com/saeyslab/nichenetr/issues/5