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, ...
#Perform the NicheNet analysis## 1. Define set of potential ligandsreceiver="Tcell"expressed_genes_receiver<- get_expressed_genes(receiver, sce, pct = 0.1) sender_celltypes<- c("Endo","Fib","Musc","Ker","APCs","Tcell","Mast","LY","Mela")list_expressed_genes_sender<- sender_cellty...
Starting from the input single-cell expression matrix, we describe a "sender-agnostic" approach which considers ligands from the entire microenvironment, and a "sender-focused" approach which only considers ligands from cell populations of interest. As output, users will obtain a list of prioritized...
sender cells to their corresponding receptors expressed by receiver cells. However, functional understanding of a CCC process also requires knowing how these inferred ligand-receptor interactions result in changes in the expression of downstream target genes within the receiver cells. Therefore, we ...
Current approaches study intercellular communication from (single-cell) expression data by linking ligands expressed by sender cells to their corresponding receptors expressed by receiver cells. However, functional understanding of a cellular communication process also requires knowing how these inferred ligand...
1. Define a “sender/niche” cell population and a “receiver/target” cell population present in your expression data and determine which genes are expressed in both populations ## receiver Idents(seuratObj) <- 'celltype' receiver = "CD4+ pro.T" ...
一般的预测细胞交互的软件往往只考虑sender细胞的配体和receiver细胞的受体表达,但细胞交互过程除了配体-受体相互作用以外,还包含了receiver细胞接受信号后相关通路的激活。 NicheNet输入基因表达数据,并将其与通过整合信号通路而构建的模型相结合。不止是预测配体与受体的相互作用,还整合了细胞内信号传导。因此,NicheNet可以...
"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。
#Perform the NicheNet analysis## 1. Define set of potential ligandsreceiver="Tcell"expressed_genes_receiver<- get_expressed_genes(receiver, sce, pct = 0.1) sender_celltypes<- c("Endo","Fib","Musc","Ker","APCs","Tcell","Mast","LY","Mela")list_expressed_genes_sender<- sender_cellty...
@@ -1580,6 +1591,9 @@ nichenet_seuratobj_cluster_de = function(seurat_obj, receiver_affected, receiver #' #' @return A list with the following elements: $ligand_activities: data frame with output ligand activity analysis; $top_ligands: top_n ligands based on ligand activity; $top_target...