Some questions in spatial transcriptome analysis

Hello everyone, I have encountered a problem when analyzing spatial transcriptome data with the protocol in (https://satijalab.org/seurat/articles/spatial_vignette.html). When I use SpatialPlot() functions, it produces this plot.

A part should have been in the left part of the picture, but it is in the right side. However, there seems no problem in the coordinate information since the shape of the plot draw by the plot() function is intact.

Also, I use the LinkedDimPlot() function to see the coordinates, and when I point to this part the horizontal coordinate also shows it should have been in the left part of the picture.

My code is as follows, along with my expression matrix and coordinate information. Thanks a lot for helping me solve the problem.

library(Seurat)
library(ggplot2)
library(patchwork)
library(dplyr)
library(SeuratData)
setwd("~/dbitseq/stpipline_res/10d_50um_rna_rep1/")

Sys.setenv(LANGUAGE = "en")
options(stringsAsFactors = FALSE)
rm(list=ls())

# creat Seurat object
expr = data.table::fread(
  input = "10d_50um_rna_rep1.tsv",
  sep = "\t",
  data.table = F
)
rownames(expr) = expr[, 1]
expr = t(expr[, -1])
expr = na.omit(expr)
stdata = CreateSeuratObject(
  counts = expr,
  project = "dbitseq",
  assay = "spatial"
)

# load coordinate information
barcode = colnames(expr)
coord = strsplit(barcode, 'x', fixed = T)
xcoord = sapply(coord, function(x){ x[2]})
ycoord = sapply(coord, function(x){ x[1]})
total_counts = colSums(expr, na.rm = T)
positions = data.frame(xcoord, ycoord, barcode, total_counts)
rownames(positions) = positions$barcode
positions = positions[, 1:2]
stdata[["image"]] = new(
  Class = 'SlideSeq',
  assay = 'spatial',
  coordinates = positions
)
plot(positions$ycoord, positions$xcoord)
SpatialPlot(stdata, pt.size.factor = 5)

# UMI count
plot1 <- VlnPlot(stdata, features = "nCount_spatial", pt.size = 1, log = TRUE) + NoLegend()
stdata$log_nCount_Spatial <- log(stdata$nCount_spatial)
plot2 <- SpatialFeaturePlot(stdata, features = "nCount_spatial", pt.size.factor = 5) + theme(legend.position = "right")
wrap_plots(plot1, plot2)

# Normalization, dimension reduction and clustering
stdata <- SCTransform(stdata, assay = "spatial", ncells = 1754, verbose = FALSE)
stdata <- RunPCA(stdata)
stdata <- RunUMAP(stdata, dims = 1:30)
stdata <- FindNeighbors(stdata, dims = 1:30)
stdata <- FindClusters(stdata, resolution = 0.3, verbose = FALSE)

# Visualization
plot1 <- DimPlot(stdata, reduction = "umap", label = TRUE)
plot2 <- SpatialDimPlot(stdata, stroke = 0, pt.size.factor = 5)
plot1 + plot2

satijalab / seurat

Some questions in spatial transcriptome analysis #6691