Because we combined the expressed genes of each sender cell type, in this example, we will perform one NicheNet analysis by poolingallligands from all cell types together. Later on during the interpretation of the output,we will check which sender cell type expresses which ligand. ligands=lr_ne...
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、...
Moreover, we provide instructions on how to make intuitive visualizations of the main predictions (e.g., via circos plots). 2. Perform NicheNet analysis starting from a Seurat object 本文的演示数据集和代码来自NicheNet官方分析单细胞数据的教程:https://github.com/saeyslab/nichenetr/blob/master/vig...
1.Browaeys R, Saelens W, Saeys Y. NicheNet: modeling intercellular communication by linking ligands to target genes[J]. Nature methods, 2020, 17(2): 159-162. 2.Zhang K, Erkan E P, Jamalzadeh S, et al. Longitudinal single-cel...
#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...
2.Zhang K, Erkan E P, Jamalzadeh S, et al. Longitudinal single-cell RNA-seq analysis reveals stress-promoted chemoresistance in metastatic ovarian cancer[J]. Science advances, 2022, 8(8): eabm1831. 3.https://github.com/saeyslab/nichenetr/blob/master/vignettes/faq.md...
Single-cell NicheNet’s ligand activity analysis:vignette("ligand_activity_single_cell", package="nichenetr") If you want to make a circos plot visualization of the NicheNet output to show active ligand-target links between interacting cells, you can check following vignettes: ...
Perform NicheNet analysis starting from a Seurat object:vignette("seurat_wrapper", package="nichenetr") Perform NicheNet analysis starting from a Seurat object: step-by-step analysis:vignette("seurat_steps", package="nichenetr") People interested in building own models or benchmark own models again...
#step4:ifferential expression (DE) analysis---#DE: FindMarkers approach.DE_info = get_DE_info_sampleAgnostic(sce = sce,group_id = group_id,celltype_id = celltype_id,contrasts_oi = contrasts_oi,expressed_df = frq_list$expressed_df,min_cells = min_cells,contrast_tbl = contrast_tbl) ...
An open-source R implementation of NicheNet is available at GitHub (https://github.com/saeyslab/nichenetr). The release includes tutorials and example vignettes for the following analyses: ligand activity analysis on a gene set of interest, single-cell ligand activity analysis; ligand-to-target ...