task_batch_integration/src/methods/seurat_cca/config.vsh.yaml at d66e5cbd3ef2d286fa96d5ce2c815d0cfb2b0908 · openproblems-bio/task_batch_integration · GitHub

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__merge__: /src/api/comp_method.yaml
name: seurat_cca
label: Seurat CCA
summary: Integration using Seurat's anchor-based CCA integration
description: |
  Seurat's Canonical Correlation Analysis (CCA) integration method identifies shared
  correlation structures across datasets to perform batch correction. This method is
  effective for datasets with shared cell types across conditions/batches.

  The method works by:
  1. Finding highly variable features for each dataset
  2. Identifying integration anchors using CCA
  3. Using anchors to harmonize datasets
  4. Generating integrated low-dimensional embedding
references:
  # Stuart, T., Butler, A., Hoffman, P. et al.
  # Comprehensive Integration of Single-Cell Data.
  # Cell 177, 1888-1902.e21 (2019). https://doi.org/10.1016/j.cell.2019.05.031
  doi: 10.1016/j.cell.2019.05.031
links:
  repository: https://github.com/satijalab/seurat
  documentation: https://satijalab.org/seurat/articles/seurat5_integration.html
info:
  method_types: [embedding]
  preferred_normalization: log_cp10k
arguments:
  - name: --dims
    type: integer
    description: Number of dimensions to use for integration.
    default: 30
  - name: --k_anchor
    type: integer
    description: Number of neighbors to use when picking anchors.
    default: 5
  - name: --k_filter
    type: integer
    description: Number of neighbors to use when filtering anchors.
    default: 200
  - name: --k_score
    type: integer
    description: Number of neighbors to use when scoring anchors.
    default: 30
resources:
  - type: r_script
    path: script.R
engines:
  - type: docker
    image: openproblems/base_r:1
    setup:
      - type: r
        cran:
          - Seurat
          - SeuratObject
runners:
  - type: executable
  - type: nextflow
    directives:
      label: [lowcpu, highmem, hightime]