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, ...
"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 adaptationrequiredon own dataset...
Define a set of potential ligands: these are ligands that are expressed by the “sender/niche” cell population and bind a (putative) receptor expressed by the “receiver/target” population Because we combined the expressed genes of each sender cell type, in this example, we will perform one...
#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...
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 ...
sender_receiver_tbl = sender_receiver_tbl,grouping_tbl = grouping_tbl,scenario = "no_frac_LR_expr", #fraction_cutoff = fraction_cutoff,abundance_data_receiver = abundance_expression_info$abundance_data_receiver,abundance_data_sender = abundance_expression_info$abundance_data_sender,ligand_activity_...
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...
"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...
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" ...