10X空间转录组绘图分析之体现两种细胞类型的空间位置

过年了，有钱没钱，回家过年，但今天居然是工作日，太没有人道了，今天我们就来简单画画图吧，我们来绘制一下空间转录组两种细胞类型的空间位置分布图，下面的图应该都很常见了。

图片.png

要么是全部细胞类型的比例图，要么就是单个细胞类型或者单个基因的空间分布图，那如何展示两种细胞类型的空间关系分布图呢？注意看，我们来实现一下

加载并读取矩阵，这里都已经处理好了，单细胞空间联合分析的结果也都在对象里面了，前面的处理过程大家自己做就可以了。

suppressMessages({
library(Seurat)
library(argparse)
library(dplyr)
library(ggplot2)
})
cortex_sp = readRDS(spatial_rds)

提取信息,单细胞空间联合分析的信息并对齐,添加到对象的meta中

decon_mtrx = t(cortex_sp@assays$predictions@data)

cell_types_all <- colnames(decon_mtrx)[which(colnames(decon_mtrx) != "max")]

decon_df <- decon_mtrx %>%
  data.frame(check.names = F) %>%
  tibble::rownames_to_column("barcodes")

cortex_sp@meta.data <- cortex_sp@meta.data %>%
  tibble::rownames_to_column("barcodes") %>%
  dplyr::left_join(decon_df, by = "barcodes") %>%
  tibble::column_to_rownames("barcodes")

开始绘图

slice <- names(cortex_sp@images)[1]
metadata_ds <- data.frame(cortex_sp@meta.data)
colnames(metadata_ds) <- colnames(cortex_sp@meta.data)
cell_types_interest <- c('Cajal-Retzius-cell','Smooth-muscle-cell')

metadata_ds <- metadata_ds %>% tibble::rownames_to_column("barcodeID") %>%
            dplyr::mutate(rsum = base::rowSums(.[, cell_types_interest,
                drop = FALSE])) %>% dplyr::filter(rsum != 0) %>%
            dplyr::select("barcodeID") %>% dplyr::left_join(metadata_ds %>%
            tibble::rownames_to_column("barcodeID"), by = "barcodeID") %>%
            tibble::column_to_rownames("barcodeID")

空间坐标,spatial_coord包含了空间坐标和单细胞空间联合的信息

spatial_coord <- data.frame(cortex_sp@images[[slice]]@coordinates) %>%
        tibble::rownames_to_column("barcodeID") %>% dplyr::mutate(imagerow_scaled = imagerow *
        cortex_sp@images[[slice]]@scale.factors$lowres, imagecol_scaled = imagecol *
        cortex_sp@images[[slice]]@scale.factors$lowres) %>% dplyr::inner_join(metadata_ds %>%
        tibble::rownames_to_column("barcodeID"), by = "barcodeID")

读取图片并绘图，我们先绘制单个细胞类型的

img <- png::readPNG(img)
img_grob <- grid::rasterGrob(img, interpolate = FALSE, width = grid::unit(1, 
        "npc"), height = grid::unit(1, "npc"))
for (cell in cell_types_all){
Max = max(spatial_coord[,cell])
spatial_coord[,cell] = spatial_coord[,cell]/Max
scatterpie_plt <- suppressMessages(ggplot2::ggplot() + ggplot2::annotation_custom(grob = img_grob, 
        xmin = 0, xmax = ncol(img), ymin = 0, ymax = -nrow(img)) + 
        ggplot2::geom_point(data = spatial_coord, ggplot2::aes(x = imagecol_scaled, 
            y = imagerow_scaled,size = get(cell), color = '#FF4500') +
        ggplot2::scale_y_reverse() + ggplot2::ylim(nrow(img), 
        0) + ggplot2::xlim(0, ncol(img)) + cowplot::theme_half_open(11, 
        rel_small = 1) + ggplot2::theme_void() + ggplot2::coord_fixed(ratio = 1, 
        xlim = NULL, ylim = NULL, expand = TRUE, clip = "on") +ggplot2::scale_size_continuous(range=c(0,2))+labs(size = cell))
pdf(paste(outdir,paste(cell,'dot.pdf',sep = '.'),sep = '/'),width = 8,height = 7)
print(scatterpie_plt)
dev.off()
}

图片.png 图片.png

好，我们把两种细胞类型画在一起

cell = 'Cajal-Retzius-cell'
cell1 = 'Smooth-muscle-cell'
Max = max(spatial_coord[,cell])
spatial_coord[,cell] = spatial_coord[,cell]/Max
Max1 = max(spatial_coord[,cell1])
spatial_coord[,cell1] = spatial_coord[,cell1]/Max1

scatterpie_plt <- suppressMessages(ggplot2::ggplot() + ggplot2::annotation_custom(grob = img_grob,xmin = 0, xmax = ncol(img), ymin = 0, ymax = -nrow(img)) +
ggplot2::geom_point(data = spatial_coord, ggplot2::aes(x = imagecol_scaled,
y = imagerow_scaled,size = get(cell),alpha = get(cell)), color = 'yellow') + ggplot2::geom_point(data = spatial_coord, ggplot2::aes(x = imagecol_scaled,
y = imagerow_scaled,size = get(cell1),alpha = get(cell1)), color = 'blue') +
ggplot2::scale_y_reverse() + ggplot2::ylim(nrow(img),
0) + ggplot2::xlim(0, ncol(img)) + cowplot::theme_half_open(11,
rel_small = 1) + ggplot2::theme_void() + ggplot2::coord_fixed(ratio = 1,
xlim = NULL, ylim = NULL, expand = TRUE, clip = "on") +ggplot2::scale_size_continuous(range=c(0,2))+ggplot2::scale_alpha_continuous(range=c(0,1))+labs(size = cell) + guides(alpha = "none"))

图片.png

看来两种细胞类型之间是有一定的距离的,这个方法用处很广，主要看两种甚至多种细胞类型之间的空间位置，以此来做下游的更重要的分析，比如空间细胞网络、细胞单元，前后细胞类型的空间位置变化等等等。

生活很好，有你更好，祝大家新年快乐