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Seurat Objects
- lig_seurat.Rda
- ligands-seurat-B_cell.RDS
- ligands-seurat-cDC1.RDS
- ligands-seurat-cDC2.RDS
- ligands-seurat-Langerhans.RDS
- ligands-seurat-Macrophage.RDS
- ligands-seurat-MigDC.RDS
- ligands-seurat-Monocyte.RDS
- ligands-seurat-Neutrophil.RDS
- ligands-seurat-NK_cell.RDS
- ligands-seurat-pDC.RDS
- ligands-seurat-T_cell_CD4.RDS
- ligands-seurat-T_cell_CD8.RDS
- ligands-seurat-T_cell_gd.RDS
- ligands-seurat-Treg.RDS
Tables of Cytokine Signatures
- Cytokine_Signatures.xlsx (Supplementary Table 31)
- Cytokine_Signatures_Human.xlsx (Supplementary Table 31, with an appended column of human gene symbols based on mouse-human homology translation)
- Polarization_Signatures.xlsx (Supplementary Table 71)
- Polarization_Signatures_Human.xlsx (Supplementary Table 71, with an appended column of human gene symbols based on mouse-human homology translation)
1 Cui, A. et al., Dictionary of immune responses to cytokines at single-cell resolution. Nature. https://doi.org/10.1038/s41586-023-06816-9 (2024)
Note: How to Load RDS Files in R
To load files in R, you can use the following code:
lig_seurat <- readRDS(paste0("path_to_your_file/"),celltype,".rds",sep = "")
Replace path_to_your_file.rds with the actual path to your RDS file.
Note: How to Load Multiple RDS Files in R in one Seurat Object
lig_seurat <- vector(mode="list", length=15)'
lig_seurat[[celltype]] <- readRDS(paste0("path_to_your_file/",celltype, ".rds", sep = ""))
Replace path_to_your_file.rds with the actual path to your RDS file. Replace celltype with the celltype of interest.
lig_seurat.Rda File
The file lig_seurat.Rda contains all cell types combined. This can be useful for comprehensive analysis across multiple cell types.
Additional code to see 'celltype', 'sample', 'rep', 'variable', 'value' columns"
raw_data = lig_seurat[[input$cellType]]
plot_df = cbind(t(as.matrix(raw_data@assays[["RNA"]]@data[input$InputGene,])),
raw_data@meta.data)
plot_df_melt = melt(plot_df, id.vars = names(raw_data@meta.data))
Replace input$cellType with the celltype you are interested in. Replace input$InputGene with the gene of interest.