upload SRTsim

src/methods/SRTsim/config.vsh.yaml

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+__merge__: ../../api/comp_method.yaml
+
+name: splatter
+
+info:
+  label: Splatter
+  summary: A single cell RNA-seq data simulator based on a gamma-Poisson distribution.
+  description: |
+    The Splat model is a gamma-Poisson distribution used to generate a gene by cell matrix of counts. Mean expression levels for each gene are simulated from a gamma distribution and the Biological Coefficient of Variation is used to enforce a mean-variance trend before counts are simulated from a Poisson distribution.
+  reference: 10.1186/s13059-017-1305-0
+  documentation_url: https://bioconductor.org/packages/devel/bioc/vignettes/splatter/inst/doc/splatter.html
+  repository_url: https://github.com/Oshlack/splatter
+
+resources:
+  - type: r_script
+    path: script.R
+
+engines:
+  - type: docker
+    image: ghcr.io/openproblems-bio/base_images/r:1.1.0
+    setup:
+      - type: r
+        bioc: splatter
+
+runners:
+  - type: executable
+  - type: nextflow
+    directives:
+      label: [midtime, midmem, midcpu]

src/methods/SRTsim/script.R

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+suppressMessages(library(SingleCellExperiment, quietly = TRUE))
+suppressMessages(library(splatter, quietly = TRUE))
+
+## VIASH START
+par <- list(
+  input = "resources_test/datasets/MOBNEW/dataset_sp.h5ad",
+  base = "domain"
+)
+meta <- list(
+  name = "splatter"
+)
+## VIASH END
+
+cat("Reading input files\n")
+input <- anndata::read_h5ad(par$input)
+
+sce <- SingleCellExperiment(
+  list(counts = Matrix::t(input$layers[["counts"]])),
+  colData = input$obs
+)
+
+cat("Splatter simulation start\n")
+
+set.seed(1)
+if (par$base != "domain") {
+  stop("ONLY domain base")
+}
+
+ordered_indices <- order(colData(sce)$spatial_cluster)
+sce_ordered <- sce[, ordered_indices]
+
+simulated_result <- NULL
+for (spatial_cluster in (unique(sce_ordered$spatial_cluster))) {
+  print(spatial_cluster)
+  res <- try({
+    sce_spatial_cluster <- sce_ordered[, sce_ordered$spatial_cluster == spatial_cluster]
+    params <- splatter::splatEstimate(as.matrix(counts(sce_spatial_cluster)))
+    sim_spatial_cluster <- splatter::splatSimulate(params)
+    sim_spatial_cluster$spatial_cluster <- spatial_cluster
+    colnames(sim_spatial_cluster) <- paste0(spatial_cluster, colnames(sim_spatial_cluster))
+    names(rowData(sim_spatial_cluster)) <- paste(spatial_cluster, names(rowData(sim_spatial_cluster)))
+
+    # combine the cell types
+    if (is.null(simulated_result)) {
+      simulated_result <- sim_spatial_cluster
+    } else {
+      simulated_result <- SingleCellExperiment::cbind(simulated_result, sim_spatial_cluster)
+    }
+  })
+}
+
+colnames(simulated_result) <- colnames(sce_ordered)
+rownames(simulated_result) <- rownames(sce_ordered)
+
+cat("Generating output\n")
+
+simulated_result_order <- sce_ordered
+counts(simulated_result_order) <- counts(simulated_result)
+
+simulated_result_order <- simulated_result_order[, match(colnames(sce), colnames(simulated_result_order))]
+simulated_result_order <- simulated_result_order[match(rownames(sce), rownames(simulated_result_order)), ]
+
+output <- anndata::AnnData(
+  layers = list(
+    counts = Matrix::t(counts(simulated_result_order))
+  ),
+  obs = as.data.frame(simulated_result_order@colData),
+  var = input$var,
+  uns = c(
+    input$uns,
+    list(
+      method_id = meta$name
+    )
+  )
+)
+
+cat("Write output files\n")
+output$write_h5ad(par$output, compression = "gzip")