1. #怎么样的分群是合理的
    2. rm(list = ls())
    3. # devtools::install_github('satijalab/seurat-data')
    4. library(SeuratData)
    5. library(ggplot2)
    6. library(patchwork)
    7. library(dplyr)
    8. load(file = 'basic.sce.pbmc.Rdata')
    10. DimPlot(pbmc, 
    11.         reduction = 'umap', 
    12.         label = TRUE, 
    13.         pt.size = 0.5) + NoLegend()
    14. sce=pbmc
    16. #先执行不同resolution 下的分群
    17. library(Seurat)
    18. library(clustree)
    19. sce <- FindClusters(object = sce,
    20.                     resolution = c(seq(.1,1.6,.2)))
    21. clustree(sce@meta.data, 
    22.          prefix = "RNA_snn_res.")
    23. colnames(sce@meta.data)

    image.png

    1. ##
    2. p1=DimPlot(sce, 
    3.            reduction = 'umap', 
    4.            group.by = 'RNA_snn_res.0.5',
    5.            label = TRUE, pt.size = 0.5) + NoLegend()
    6. p2=DimPlot(pbmc, 
    7.            reduction = 'umap',# group.by = 'seurat_clusters',
    8.            label = TRUE, pt.size = 0.5) + NoLegend()
    9. p1+p2
    12. ##
    13. p1=DimPlot(sce, 
    14.            reduction = 'umap',
    15.            group.by = 'RNA_snn_res.0.5',
    16.            label = TRUE, pt.size = 0.5) + NoLegend()
    17. p2=DimPlot(sce, 
    18.            reduction = 'umap',
    19.            group.by = 'RNA_snn_res.1.5',
    20.            label = TRUE, pt.size = 0.5) + NoLegend()
    21. p1+p2

    image.png
    image.png