Spatial Transcriptomics Data Processing

Author

Ahmed M. Elhossiny

1 Setup & Data Loading

Here we load essential packages and visium_samples_manifest.xlsx from the data directory, that includes information and metadata about visium samples. We use Seurat v5 (Hao et al. 2023) as data container for processing and visualization.

Code

suppressPackageStartupMessages({
  library(Seurat)
  library(tidyverse)
  library(parallel)
  library(scCustomize)
  library(SPATA2)
  library(qs)
  library(CellChat)
})

# loading manifest ------------------------------------------------------------

visium_samples <- readxl::read_xlsx("../data/visium_samples_manifest.xlsx")

# processing data ------------------------------------------------------------

outputDir <- "outputs/Preprocessing/spatial_seurat_objects/"
dir.create(outputDir, showWarnings = F, recursive = T)

2 Seurat Object Preparation

Here we generate spatial Seurat objects for each patient sample, and save them separately. They will be merged later in Clustering Analysis with deconvolution and clustering results

Code

lapply(visium_samples$patient_id, function(x){
  message(x)
  path <- visium_samples %>% filter(patient_id == x) %>% pull(cellranger_3.0.1_output_path)
  seurat_obj <- Load10X_Spatial(path)
  seurat_obj <- NormalizeData(seurat_obj)
  seurat_obj <- ScaleData(seurat_obj)
  saveRDS(seurat_obj, paste0(outputDir, x,".rds"))
})

References

Hao, Yuhan, Tim Stuart, Madeline H Kowalski, Saket Choudhary, Paul Hoffman, Austin Hartman, Avi Srivastava, et al. 2023. “Dictionary Learning for Integrative, Multimodal and Scalable Single-Cell Analysis.” Nature Biotechnology. https://doi.org/10.1038/s41587-023-01767-y.