SpaTalk

src/methods/spatalk/config.vsh.yaml

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+functionality:
+  name: spatalk
+  arguments:
+    - name: --input
+      type: file
+      description: Path to the input file
+      example: resources/datasets/Zhuang_2023_merfish_brain/dataset.h5ad
+    - name: --output
+      type: file
+      description: Path to the output directory
+      direction: output
+      example: resources/results/spatalk/prediction.h5ad
+  resources:
+    - type: r_script
+      path: script.R
+platforms:
+  - type: docker
+    image: satijalab/seurat:4.1.0
+    setup:
+      - type: r
+        packages:
+          - anndata
+          - tidyverse
+        github:
+          - linxihui/NNLM
+          - ZJUFanLab/SpaTalk

src/methods/spatalk/script.R

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+## VIASH START
+par <- list(
+  input = "resources/datasets/Zhuang_2023_merfish_brain/dataset.h5ad",
+  output = "resources/results/spatalk/prediction.h5ad"
+)
+## VIASH END
+library(tidyverse)
+library(SpaTalk)
+library(anndata)
+
+adata <- read_h5ad(par$input)
+count_matrix <- adata$layers[["counts"]] %>% t %>%
+  `rownames<-`(adata$var$gene_symbol)
+coords <- adata$obsm %>% data.frame %>% 
+  mutate(cell = colnames(count_matrix), .before=1) %>% 
+  rename(x = spatial.1, y = spatial.2)
+
+pyad <- adata$.__enclos_env__$private$.anndata
+pyad$obs <- reticulate::py_get_item(pyad$obs, c("celltype", "fov"))
+
+# create SpaTalk data
+obj <- createSpaTalk(st_data = count_matrix,
+                     st_meta = coords,
+                     species = "Mouse",
+                     if_st_is_sc = T,
+                     spot_max_cell = 1,
+                     celltype = as.character(adata$obs[["celltype"]]))
+
+obj <- find_lr_path(object = obj, lrpairs = lrpairs, pathways = pathways)
+obj <- dec_cci_all(object = obj)
+
+# generate prediction
+ccc_pred <- data.frame(
+  obj@lrpair 
+) %>% arrange(desc(score))
+
+# create output
+output <- anndata::AnnData(
+  shape = c(0L, 0L),
+  uns = list(
+    ccc_pred = ccc_pred
+  )
+)
+output$write_h5ad(par$output, compression = "gzip")