Module/scdblfinder #261
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| Original file line number | Diff line number | Diff line change |
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| name: scdblfinder | ||
| channels: | ||
| - conda-forge | ||
| - bioconda | ||
| dependencies: | ||
| - bioconda::bioconductor-scdblfinder=1.24.0 | ||
| - bioconda::bioconductor-singlecellexperiment=1.32.0 | ||
| - bioconda::bioconductor-biocparallel=1.44.0 | ||
| - bioconda::bioconductor-anndatar=1.0.2 | ||
| - bioconda::bioconductor-rhdf5=2.54.1 | ||
| - conda-forge::r-tidyverse=2.0.0 |
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| Original file line number | Diff line number | Diff line change |
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| process SCDBLFINDER { | ||
| tag "$meta.id" | ||
| label 'process_medium' | ||
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| conda "${moduleDir}/environment.yml" | ||
| container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ? | ||
| 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/99/993a012a69d920412b090701eb733ccf35c8655c3d012756ca6b0af1cfcd4780/data' : | ||
| 'community.wave.seqera.io/library/bioconductor-anndatar_bioconductor-biocparallel_bioconductor-rhdf5_bioconductor-scdblfinder_pruned:0f9db6b0855861de' }" | ||
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| input: | ||
| tuple val(meta), path(h5ad), val(dbr) | ||
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| output: | ||
| tuple val(meta), path("${prefix}.h5ad"), emit: h5ad | ||
| tuple val(meta), path("${prefix}.csv"), emit: predictions | ||
| path "versions.yml", emit: versions | ||
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| when: | ||
| task.ext.when == null || task.ext.when | ||
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| script: | ||
| prefix = task.ext.prefix ?: "${meta.id}" | ||
| template('scdblfinder.R') | ||
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| stub: | ||
| prefix = task.ext.prefix ?: "${meta.id}" | ||
| """ | ||
| touch ${prefix}.h5ad | ||
| touch ${prefix}.csv | ||
| touch versions.yml | ||
| """ | ||
| } |
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| name: "scdblfinder" | ||
| description: Detect doublets in single-cell RNA-seq data using scDblFinder | ||
| keywords: | ||
| - doublet-detection | ||
| - single-cell | ||
| - scrnaseq | ||
| - quality-control | ||
| tools: | ||
| - "scdblfinder": | ||
| description: "scDblFinder: Computational identification of doublets in single-cell transcriptomics data" | ||
| homepage: "https://bioconductor.org/packages/scDblFinder" | ||
| documentation: "https://bioconductor.org/packages/release/bioc/vignettes/scDblFinder/inst/doc/scDblFinder.html" | ||
| tool_dev_url: "https://github.com/plger/scDblFinder" | ||
| doi: "10.12688/f1000research.73600.2" | ||
| licence: ["GPL-3.0"] | ||
| identifier: biotools:scdblfinder | ||
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| input: | ||
| - - meta: | ||
| type: map | ||
| description: | | ||
| Groovy Map containing sample information | ||
| e.g. `[ id:'sample1' ]` | ||
| - h5ad: | ||
| type: file | ||
| description: AnnData object in h5ad format | ||
| pattern: "*.{h5ad}" | ||
| ontologies: | ||
| - edam: "http://edamontology.org/format_3590" # HDF5 format | ||
| - dbr: | ||
| type: number | ||
| description: | | ||
| Optional expected doublet rate (0-1). If null, scDblFinder estimates | ||
| the doublet rate internally. | ||
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| output: | ||
| h5ad: | ||
| - - meta: | ||
| type: map | ||
| description: | | ||
| Groovy Map containing sample information | ||
| e.g. `[ id:'sample1' ]` | ||
| - "*.h5ad": | ||
| type: file | ||
| description: AnnData object with doublet annotations | ||
| pattern: "*.h5ad" | ||
| ontologies: | ||
| - edam: "http://edamontology.org/format_3590" # HDF5 format | ||
| predictions: | ||
| - - meta: | ||
| type: map | ||
| description: | | ||
| Groovy Map containing sample information | ||
| e.g. `[ id:'sample1' ]` | ||
| - "*.csv": | ||
| type: file | ||
| description: CSV file containing doublet predictions (boolean) | ||
| pattern: "*.csv" | ||
| ontologies: | ||
| - edam: "http://edamontology.org/format_3752" # CSV | ||
| versions: | ||
| - versions.yml: | ||
| type: file | ||
| description: File containing software versions | ||
| pattern: "versions.yml" | ||
| ontologies: | ||
| - edam: http://edamontology.org/format_3750 # YAML | ||
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| authors: | ||
| - "@KurayiChawatama" | ||
| maintainers: | ||
| - "@KurayiChawatama" |
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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env Rscript | ||
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| library(scDblFinder) | ||
| library(tidyverse) | ||
| library(SingleCellExperiment) | ||
| library(BiocParallel) | ||
| library(anndataR) | ||
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| adata <- read_h5ad("${h5ad}") | ||
| sce <- adata\$as_SingleCellExperiment() | ||
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| num_threads <- max(1L, as.integer("${task.cpus}")) | ||
| bp <- MulticoreParam(workers = num_threads, RNGseed = 123) | ||
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| # Save original cell names and count before overwriting sce | ||
| original_cell_names <- colnames(sce) | ||
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| # Parse per-sample doublet rate from Nextflow input. If unavailable, let | ||
| # scDblFinder estimate dbr internally (recommended default for 10X data). | ||
| dbr_raw <- trimws("${dbr}") | ||
| dbr <- suppressWarnings(as.numeric(dbr_raw)) | ||
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| # Run scDblFinder on the counts matrix (first assay) | ||
| # scDblFinder creates artificial doublets internally and returns a new SCE | ||
| set.seed(123) | ||
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| if (is.na(dbr)) { | ||
| message("No valid doublet_rate provided; using scDblFinder internal dbr estimation") | ||
| dbr <- NULL | ||
| } else { | ||
| message(paste0("Using provided doublet_rate (dbr): ", dbr)) | ||
| } | ||
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| sce <- scDblFinder( | ||
| assays(sce)[[1]], | ||
| BPPARAM = bp, | ||
| dbr = dbr | ||
| ) | ||
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Comment on lines
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Collaborator
There was a problem hiding this comment. I found that scDblFinder has an optional |
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| # Generate a summary table | ||
| message("scDblFinder results summary:") | ||
| print(table(sce\$scDblFinder.class)) | ||
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| # Rename scDblFinder.* columns for consistency with other doublet methods. | ||
| # Replace prefix first, then replace any remaining dots with underscores. | ||
| idx <- grep("^scDblFinder\\\\.", colnames(colData(sce))) | ||
| colnames(colData(sce))[idx] <- gsub( | ||
| "\\\\.", | ||
| "_", | ||
| sub("^scDblFinder\\\\.", "scdblfinder_", colnames(colData(sce))[idx]) | ||
| ) | ||
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| # The doublet calls must stay keyed by the original cell barcodes. If they are not | ||
| # present here, something went wrong during conversion or scDblFinder processing and | ||
| # we should fail instead of inventing replacement identifiers. | ||
| if (is.null(colnames(sce)) || length(colnames(sce)) != ncol(sce)) { | ||
| stop("scDblFinder output is missing valid cell barcodes; cannot write aligned h5ad and prediction outputs.") | ||
| } | ||
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| # Write the updated SingleCellExperiment directly as h5ad, explicitly mapping the | ||
| # primary assay to AnnData X so downstream readers see a valid matrix field. | ||
| primary_assay <- assayNames(sce)[1] | ||
| if (is.na(primary_assay) || primary_assay == "") { | ||
| stop("scDblFinder output is missing a primary assay; cannot write h5ad output.") | ||
| } | ||
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Collaborator
There was a problem hiding this comment. I think this is also unnecessary because the sce is created from the anndata in a way that is perfectly prepared for reversing the process |
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| write_h5ad(sce, "${prefix}.h5ad", x_mapping = primary_assay) | ||
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| # Extract predictions for doublet removal step | ||
| # Create a binary doublet call based on class | ||
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| # Create predictions vector | ||
| doublet_calls <- colData(sce)\$scdblfinder_class == "doublet" | ||
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| # Create data frame without row.names first, then add them | ||
| predictions <- data.frame(doublet = doublet_calls) | ||
| row.names(predictions) <- colnames(sce) | ||
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| colnames(predictions) <- "${prefix}" | ||
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| # Save predictions to CSV | ||
| write.csv(predictions, "${prefix}.csv") | ||
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| ################################################ | ||
| ################################################ | ||
| ## VERSIONS FILE ## | ||
| ################################################ | ||
| ################################################ | ||
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| r.version <- strsplit(version[['version.string']], ' ')[[1]][3] | ||
| scDblFinder.version <- as.character(packageVersion('scDblFinder')) | ||
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| writeLines( | ||
| c( | ||
| '"${task.process}":', | ||
| paste(' R:', r.version), | ||
| paste(' scDblFinder:', scDblFinder.version) | ||
| ), | ||
| 'versions.yml') | ||
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| ################################################ | ||
| ################################################ | ||
| ################################################ | ||
| ################################################ | ||
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