1. #多个单细胞亚群合并
    2. rm(list = ls())
    3. library(Seurat)
    4. # devtools::install_github('satijalab/seurat-data')
    5. library(SeuratData)
    6. library(ggplot2)
    7. library(patchwork)
    8. library(dplyr)
    9. load(file = 'basic.sce.pbmc.Rdata')
    10. levels(Idents(pbmc))
    12. DimPlot(pbmc, 
    13.         reduction = 'umap', 
    14.         label = TRUE,
    15.         pt.size = 0.5) + NoLegend()
    17. sce=pbmc
    19. #为了合并亚群，将NK、CD8T、CD4T合并为一个亚群，将DC、Mono合并为Mono亚群
    20. genes_to_check = c('PTPRC', 'CD3D', 'CD3E',  'PRF1' , 'NKG7',
    21.                    'CD19', 'CD79A', 'MS4A1' ,
    22.                    'CD68', 'CD163', 'CD14',
    23.                    "FCER1A", "PPBP")
    24. DotPlot(sce, 
    25.         group.by = 'seurat_clusters',
    26.         features = unique(genes_to_check)) + RotatedAxis()
    28. Idents(sce)
    29. levels(sce)
    30. head(sce@meta.data)
    32. #方法1.(将NK、CD8T、CD4T合并为一个亚群，将DC、Mono合并为Mono亚群)
    33. new.cluster.ids <- c("T", "Mono", "T", 
    34.                      "B", "T", "Mono",
    35.                      "T", "DC", "Platelet")
    36. names(new.cluster.ids) <- levels(sce)
    38. #合并后的sce
    39. sce <- RenameIdents(sce, 
    40.                     new.cluster.ids)
    41. #合并后作图
    42. DimPlot(sce,
    43.         reduction = 'umap', 
    44.         label = TRUE, pt.size = 0.5) + NoLegend()
    46. #方法2.(将NK、CD8T、CD4T合并为一个亚群，将DC、Mono合并为Mono亚群)   
    47. cluster2celltype <- c("0"="T",
    48.                   "1"="Mono", 
    49.                   "2"="T", 
    50.                   "3"= "B", 
    51.                   "4"= "T", 
    52.                   "5"= "Mono",
    53.                   "6"= "T", 
    54.                   "7"= "DC", 
    55.                   "8"= "Platelet")
    56. sce[['cell_type']] = unname(cluster2celltype[sce@meta.data$seurat_clusters])
    58. #合并后作图
    59. DimPlot(sce, 
    60.         reduction = 'umap', 
    61.         group.by = 'cell_type',
    62.         label = TRUE, pt.size = 0.5) + NoLegend()
    64. #方法3.(将NK、CD8T、CD4T合并为一个亚群，将DC、Mono合并为Mono亚群) 
    65. # 一个数据框 
    66. (n=length(unique(sce@meta.data$seurat_clusters)))
    67. celltype=data.frame(ClusterID=0:(n-1),
    68.                     celltype='unkown')
    69. celltype[celltype$ClusterID %in% c(0,2,4,6),2]='T'  #第0，2，4，6亚群为T细胞
    70. celltype[celltype$ClusterID %in% c(3),2]='B'        #第3亚群为B细胞
    71. celltype[celltype$ClusterID %in% c(1,5),2]='Mono'   #第1，5亚群为Mono
    72. celltype[celltype$ClusterID %in% c(7),2]='DC'       #第7亚群为DC
    73. celltype[celltype$ClusterID %in% c(8),2]='Platelet' #第8亚群为Platelet
    74. sce@meta.data$celltype = "NA"
    75. for(i in 1:nrow(celltype)){
    76.   sce@meta.data[which(sce@meta.data$seurat_clusters == celltype$ClusterID[i]),'celltype'] <- celltype$celltype[i]}
    77. table(sce@meta.data$celltype)
    78. phe=sce@meta.data
    80. #合并后作图
    81. DimPlot(sce, 
    82.         reduction = 'umap', 
    83.         group.by = 'celltype',
    84.         label = TRUE, pt.size = 0.5) + NoLegend()
    86. head(sce@meta.data)
    87. table(sce@meta.data$cell_type,
    88.       sce@meta.data$celltype)
    90. p1=DimPlot(sce, 
    91.            reduction = 'umap', 
    92.            group.by = 'celltype',
    93.            label = TRUE, pt.size = 0.5) + NoLegend()
    94. p2=DotPlot(sce, 
    95.            group.by = 'celltype',
    96.            features = unique(genes_to_check)) + RotatedAxis()
    97. p1+p2

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