空间转录组|数据读入，标准数据形式外，还有哪些"天残地缺"可以读取

/path/spaceranger count --id=6332STDY10289523 \
--transcriptome=/path/refdata-gex-GRCh38-2020-A/ \
--fastqs=/path/ST_data/ \
--sample=6332STDY10289523 \
--image=/path/V10S24-031_D1.jpg \
--slide=V10S24-031 \
--area=D1

library(Seurat)
library(hdf5r)
spe = Load10X_Spatial(data.dir = "./outs/",
                            filename = "filtered_feature_bc_matrix.h5",
                            assay = "Spatial", 
                            slice = "Lung")
head(spe@meta.data,2)
#                      orig.ident nCount_Spatial nFeature_Spatial
#AAACAAGTATCTCCCA-1 SeuratProject           7232             2774
#AAACACCAATAACTGC-1 SeuratProject          20915             6318
#空转的话，就是SpatialFeaturePlot ，相较于单细胞转录组的FeaturePlot
SpatialFeaturePlot(spe, features = "nFeature_Spatial")

for (i in colnames((spe@images$Lung@coordinates))) {
  spe@images$Lung@coordinates[[i]] <- as.integer(spe@images$Lung@coordinates[[i]])
}

SpatialFeaturePlot(spe, features = "nFeature_Spatial")
p0 <- SpatialDimPlot(spe,alpha = 0)
p1 <- SpatialDimPlot(spe,alpha = 1)
p0 + p1

spe2 = Read10X("./outs/filtered_feature_bc_matrix/")
image2 <- Read10X_Image(image.dir = file.path("./outs/", 
                                              "spatial"), filter.matrix = TRUE)
spe2 <- CreateSeuratObject(counts = spe2, assay = "Spatial")
image2 <- image2[Cells(x = spe2)]
DefaultAssay(spe2 = image2) <- "Spatial"
spe2[["slice1"]] <- image2
#没有报错，无需转化
for (i in colnames((spe2@images$slice1@coordinates))) {
  spe2@images$slice1@coordinates[[i]] <- as.integer(spe2@images$slice1@coordinates[[i]])
}
SpatialFeaturePlot(spe2, features = "nFeature_Spatial")

spe3 <- readRDS("./STData/P1N_Spatial.rds")
SpatialFeaturePlot(spe3 , features = "MS4A1")

spe3<- UpdateSeuratObject(spe3)
SpatialFeaturePlot(spe3, features = "MS4A1")

img = Read10X_Image("./outs/spatial/", 
                    image.name = "tissue_hires_image.png")
spe4 = Load10X_Spatial("./outs/", image = img)
spe4@images$slice1@scale.factors$lowres = spe4@images$slice1@scale.factors$hires
#没有报错，无需转化
for (i in colnames((spe4@images$slice1@coordinates))) {
  spe4@images$slice1@coordinates[[i]] <- as.integer(spe4@images$slice1@coordinates[[i]])
}
#对比一下两张图的差异
p1 <- SpatialFeaturePlot(spe2, features = "nFeature_Spatial")
p2 <- SpatialFeaturePlot(spe4, features = "nFeature_Spatial")
p1 + p2

spe5 <- spe2
lowres = spe5@images$slice1
hires = Read10X_Image("./outs/spatial/", 
                      image.name = "tissue_hires_image.png")
hires@scale.factors$lowres = hires@scale.factors$hires
#Set the assay and key to the same as the lowres (I'm not sure if this is necessary or not).
hires@assay = lowres@assay
hires@key = lowres@key
#Finally, add the image into the object and everything should be good!
spe5@images$slice1 = hires
#没有报错，无需转化
for (i in colnames((spe5@images$slice1@coordinates))) {
  spe5@images$slice1@coordinates[[i]] <- as.integer(spe5@images$slice1@coordinates[[i]])
}
p1 <- SpatialFeaturePlot(spe2, features = "nFeature_Spatial")
p2 <- SpatialFeaturePlot(spe5, features = "nFeature_Spatial")
p1 + p2