Adding new method: scMerge2

src/methods/semisupervised_scmerge2/config.vsh.yaml

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+__merge__: ../../api/comp_method.yaml
+name: semisupervised_scmerge2
+label: Semi-supervised Scmerge2
+summary: "scMerge2 is an algorithm that integrates multiple single-cell RNA-seq datasets by leveraging factor analysis of stably expressed genes and pseudoreplication."
+description: |
+  When cell type information are known (e.g. results from cell type classification using reference),
+  scMerge2 can use this information to construct pseudo-replicates and identify mutual nearest groups with cellTypes input.
+  scMerge works by integrating multiple single-cell RNA-seq datasets while correcting for batch effects and preserving biological signals.
+  It first identifies a set of stably expressed genes (SEGs) that are assumed to remain consistent across datasets.
+  Then, it uses a factor analysis model on these SEGs to estimate and remove unwanted variation.
+  To improve accuracy, scMerge creates pseudo-replicates which serve as anchors for alignment.
+  Finally, it corrects the data using these estimates, producing a harmonized expression matrix suitable for downstream analysis.
+references:
+  doi: 
+    - 10.1073/pnas.1820006116
+links:
+  documentation: https://sydneybiox.github.io/scMerge/articles/scMerge2.html
+  repository: https://github.com/SydneyBioX/scMerge
+info:
+  preferred_normalization: log_cpm
+resources:
+  - type: r_script
+    path: script.R
+engines:
+  - type: docker
+    image: openproblems/base_r:1
+    setup:
+      - type: apt
+        packages: cmake
+      - type: r
+        bioc: 
+        - scmerge
+runners:
+  - type: executable
+  - type: nextflow
+    directives:
+      label: [midtime,midmem,midcpu]

src/methods/semisupervised_scmerge2/script.R

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+library(anndata)
+library(scMerge)
+
+## VIASH START
+par <- list(
+  input = "resources_test/task_batch_integration/cxg_immune_cell_atlas/dataset.h5ad",
+  output = "output.h5ad"
+)
+meta <- list(
+  name = "semisupervised_scmerge2"
+)
+## VIASH END
+
+cat("Reading input files\n")
+adata <- anndata::read_h5ad(par$input)
+adata$obs["batch"] <- sub("\\+", "plus", adata$obs[["batch"]]) # Replace "+"" characters in batch names
+
+anndataToSemiSupervisedScMerge2 <- function(adata, seg_list, layer = "normalized", verbose = FALSE) {
+  exprsMat_all <- t(as.matrix(adata$layers[[layer]]))
+  batch_all <- as.character(adata$obs$batch)
+  celltypes_all <- as.character(adata$obs$cell_type)
+
+  valid_cells <- !is.na(batch_all)
+  exprsMat <- exprsMat_all[, valid_cells, drop = FALSE]
+  batch <- batch_all[valid_cells]
+  cellTypes <- celltypes_all[valid_cells]
+
+  # Check overlap with human/mouse scSEG lists
+  gene_ids <- rownames(exprsMat)
+  species <- NULL
+  best_match <- 0
+
+  for (organism in names(seg_list)) {
+    scseg_name <- paste0(organism, "_scSEG")
+    seg_genes <- seg_list[[organism]][[scseg_name]]
+    overlap <- length(intersect(gene_ids, seg_genes))
+
+    if (overlap > best_match) {
+      best_match <- overlap
+      species <- organism
+    }
+  }
+
+  if (is.null(species) || best_match == 0) {
+    stop("No match found between gene IDs in exprsMat and scSEG lists for human or mouse. ",
+         "Please ensure you're using Ensembl IDs for human or mouse, or provide a custom SEG list.")
+  }
+
+  message("Detected species: ", species, " (matched ", best_match, " genes)")
+
+  ctl <- seg_list[[species]][[paste0(species, "_scSEG")]]
+
+  scMerge2_res <- scMerge2(
+    exprsMat = exprsMat,
+    batch = batch,
+    cellTypes = cellTypes,
+    ctl = ctl,
+    verbose = verbose
+  )
+
+  return(scMerge2_res)
+}
+
+data("segList_ensemblGeneID")
+
+cat("Run semi-supervised scMerge2\n")
+
+scMerge2_res <- anndataToSemiSupervisedScMerge2(
+  adata = adata,
+  seg_list = segList_ensemblGeneID,
+  layer = "normalized",
+  verbose = TRUE
+)
+
+
+cat("Store output\n")
+corrected_mat <- scMerge2_res$newY
+
+embedding <- prcomp(t(corrected_mat))$x[, 1:10]
+
+rownames(embedding) <- colnames(corrected_mat)
+
+output <- anndata::AnnData(
+  X = NULL,
+  obs = adata$obs[, c()],
+  var = NULL,
+  obsm = list(
+    X_emb = embedding[rownames(adata), , drop = FALSE]  # match input cells
+  ),
+  uns = list(
+    dataset_id = adata$uns[["dataset_id"]],
+    normalization_id = adata$uns[["normalization_id"]],
+    method_id = meta$name 
+  ),
+  shape = adata$shape
+)
+
+cat("Write output AnnData to file\n")
+output$write_h5ad(par[["output"]], compression = "gzip")

src/methods/unsupervised_scmerge2/config.vsh.yaml

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+__merge__: ../../api/comp_method.yaml
+name: unsupervised_scmerge2
+label: unsupervised Scmerge2
+summary: "scMerge2 is an algorithm that integrates multiple single-cell RNA-seq datasets by leveraging factor analysis of stably expressed genes and pseudoreplication."
+description: |
+  scMerge works by integrating multiple single-cell RNA-seq datasets while correcting for batch effects and preserving biological signals.
+  It first identifies a set of stably expressed genes (SEGs) that are assumed to remain consistent across datasets.
+  Then, it uses a factor analysis model on these SEGs to estimate and remove unwanted variation.
+  To improve accuracy, scMerge creates pseudo-replicates which serve as anchors for alignment.
+  Finally, it corrects the data using these estimates, producing a harmonized expression matrix suitable for downstream analysis.
+references:
+  doi: 
+    - 10.1073/pnas.1820006116
+links:
+  documentation: https://sydneybiox.github.io/scMerge/articles/scMerge2.html
+  repository: https://github.com/SydneyBioX/scMerge
+info:
+  preferred_normalization: log_cpm
+resources:
+  - type: r_script
+    path: script.R
+engines:
+  - type: docker
+    image: openproblems/base_r:1
+    setup:
+      - type: apt
+        packages: cmake
+      - type: r
+        bioc: 
+        - scmerge
+runners:
+  - type: executable
+  - type: nextflow
+    directives:
+      label: [midtime,midmem,midcpu]

src/methods/unsupervised_scmerge2/script.R

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+cat(">> Load dependencies\n")
+requireNamespace("anndata", quietly = TRUE)
+library(scMerge)
+
+## VIASH START
+par <- list(
+  input = "resources_test/task_batch_integration/cxg_immune_cell_atlas/dataset.h5ad",
+  output = "output.h5ad"
+)
+meta <- list(
+  name = "unsupervised_scmerge2"
+)
+## VIASH END
+
+cat("Reading input files\n")
+adata <- anndata::read_h5ad(par$input)
+adata$obs["batch"] <- sub("\\+", "plus", adata$obs[["batch"]]) # Replace "+"" characters in batch names
+
+anndataToScMerge2 <- function(adata, seg_list, layer = "normalized", verbose = FALSE) {
+  exprsMat_all <- t(as.matrix(adata$layers[[layer]]))
+  batch_all <- as.character(adata$obs$batch)
+
+  valid_cells <- !is.na(batch_all)
+  exprsMat <- exprsMat_all[, valid_cells, drop = FALSE]
+  batch <- batch_all[valid_cells]
+
+  # Check overlap with human/mouse scSEG lists
+  gene_ids <- rownames(exprsMat)
+  species <- NULL
+  best_match <- 0
+
+  for (organism in names(seg_list)) {
+    scseg_name <- paste0(organism, "_scSEG")
+    seg_genes <- seg_list[[organism]][[scseg_name]]
+    overlap <- length(intersect(gene_ids, seg_genes))
+
+    if (overlap > best_match) {
+      best_match <- overlap
+      species <- organism
+    }
+  }
+
+  if (is.null(species) || best_match == 0) {
+    stop("No match found between gene IDs in exprsMat and scSEG lists for human or mouse. ",
+         "Please ensure you're using Ensembl IDs for human or mouse, or provide a custom SEG list.")
+  }
+
+  message("Detected species: ", species, " (matched ", best_match, " genes)")
+
+  ctl <- seg_list[[species]][[paste0(species, "_scSEG")]]
+
+  scMerge2_res <- scMerge2(
+    exprsMat = exprsMat,
+    batch = batch,
+    ctl = ctl,
+    verbose = verbose
+  )
+
+  return(scMerge2_res)
+}
+
+data("segList_ensemblGeneID")
+
+cat("Run scMerge2\n")
+
+scMerge2_res <- anndataToScMerge2(
+  adata = adata,
+  seg_list = segList_ensemblGeneID,
+  layer = "normalized",
+  verbose = TRUE
+)
+
+
+cat("Store output\n")
+corrected_mat <- scMerge2_res$newY
+
+embedding <- prcomp(t(corrected_mat))$x[, 1:10]
+
+rownames(embedding) <- colnames(corrected_mat)
+
+output <- anndata::AnnData(
+  X = NULL,
+  obs = adata$obs[, c()],
+  var = NULL,
+  obsm = list(
+    X_emb = embedding[rownames(adata), , drop = FALSE]  # match input cells
+  ),
+  uns = list(
+    dataset_id = adata$uns[["dataset_id"]],
+    normalization_id = adata$uns[["normalization_id"]],
+    method_id = meta$name 
+  ),
+  shape = adata$shape
+)
+cat("Write output AnnData to file\n")
+output$write_h5ad(par[["output"]], compression = "gzip")