代码语言：javascript复制
#Installation
devtools::install_github("satijalab/seurat", ref = "spatial")加载需要的R包
代码语言：javascript复制library(Seurat)
library(SeuratData)
library(ggplot2)
library(cowplot)
library(dplyr)安装测试的数据集
代码语言：javascript复制InstallData("stxBrain")
brain <- LoadData("stxBrain", type = "anterior1")计算一下所有细胞的counts分布情况，发现counts差距比较大，因此需要normalize
代码语言：javascript复制plot1 <- VlnPlot(brain, features = "nCount_Spatial", pt.size = 0.1) NoLegend()
plot2 <- SpatialFeaturePlot(brain, features = "nCount_Spatial") theme(legend.position = "right")
plot_grid(plot1, plot2)image.png
常用的LogNormalize会损失不同的位置本身存在的counts差距的信息，因此选用SCTransform
代码语言：javascript复制brain <- SCTransform(brain, assay = "Spatial", verbose = FALSE)SpatialFeaturePlot查看基因的位置表达信息
代码语言：javascript复制SpatialFeaturePlot(brain, features = c("Hpca", "Ttr"))image.png
调整点的大小和透明度
代码语言：javascript复制p1 <- SpatialFeaturePlot(brain, features = "Ttr", pt.size.factor = 1)
p2 <- SpatialFeaturePlot(brain, features = "Ttr", alpha = c(0.1, 1))
plot_grid(p1, p2)image.png
下面就是跟单细胞一样的流程：降维聚类
代码语言：javascript复制brain <- RunPCA(brain, assay = "SCT", verbose = FALSE)
brain <- FindNeighbors(brain, reduction = "pca", dims = 1:30)
brain <- FindClusters(brain, verbose = FALSE)
brain <- RunUMAP(brain, reduction = "pca", dims = 1:30)可视化
代码语言：javascript复制p1 <- DimPlot(brain, reduction = "umap", label = TRUE)
p2 <- SpatialDimPlot(brain, label = TRUE, label.size = 3)
plot_grid(p1, p2)image.png
交互式展示
代码语言：javascript复制# move your mouse
SpatialDimPlot(brain, do.hover = TRUE)image.png
用cells.highlight展示感兴趣的细胞的位置
代码语言：javascript复制SpatialDimPlot(brain, cells.highlight = CellsByIdentities(object = brain, idents = c(1, 2, 5, 3, 4, 8)), facet.highlight = TRUE, ncol = 3)image.png
（未完待续） 原文选自：https://satijalab.org/seurat/v3.1/spatial_vignette.html
