sc-rna-reduce.cwl

cwlVersion: v1.0
class: CommandLineTool


requirements:
- class: InlineJavascriptRequirement
- class: EnvVarRequirement
  envDef:
    R_MAX_VSIZE: $((inputs.vector_memory_limit * 1000000000).toString())


hints:
- class: DockerRequirement
  dockerPull: biowardrobe2/sc-tools:v0.0.13


inputs:

  query_data_rds:
    type: File
    inputBinding:
      prefix: "--query"
    doc: |
      Path to the RDS file to load Seurat object from. This file should include genes
      expression information stored in the RNA assay.

  datasets_metadata:
    type: File?
    inputBinding:
      prefix: "--metadata"
    doc: |
      Path to the TSV/CSV file to optionally extend Seurat object metadata with
      categorical values using samples identities. First column - 'library_id'
      should correspond to all unique values from the 'new.ident' column of the
      loaded Seurat object. If any of the provided in this file columns are already
      present in the Seurat object metadata, they will be overwritten. When combined
      with --barcodes parameter, first the metadata will be extended, then barcode
      filtering will be applied.
      Default: no extra metadata is added

  barcodes_data:
    type: File?
    inputBinding:
      prefix: "--barcodes"
    doc: |
      Path to the TSV/CSV file to optionally prefilter and
      extend Seurat object metadata be selected barcodes.
      First column should be named as 'barcode'. If file
      includes any other columns they will be added to the
      Seurat object metadata ovewriting the existing ones if
      those are present.
      Default: all cells used, no extra metadata is added

  cell_cycle_data:
    type: File?
    inputBinding:
      prefix: "--cellcycle"
    doc: |
      Path to the TSV/CSV file with the information for cell cycle score assignment.
      First column - 'phase', second column 'gene_id'. If loaded Seurat object already
      includes cell cycle scores in 'S.Score' and 'G2M.Score' metatada columns they will
      be removed.
      Default: skip cell cycle score assignment.

  normalization_method:
    type:
    - "null"
    - type: enum
      symbols:
      - "sct"
      - "log"
      - "sctglm"
    inputBinding:
      prefix: "--norm"
    doc: |
      Normalization method applied to genes expression counts. If loaded Seurat object
      includes multiple datasets, normalization will be run independently for each of
      them, unless integration is disabled with 'none' or set to 'harmony'
      Default: sct

  integration_method:
    type:
    - "null"
    - type: enum
      symbols:
      - "seurat"
      - "harmony"
      - "none"
    inputBinding:
      prefix: "--ntgr"
    doc: |
      Integration method used for joint analysis of multiple datasets. Automatically
      set to 'none' if loaded Seurat object includes only one dataset.
      Default: seurat

  integrate_by:
    type:
    - "null"
    - string
    - string[]
    inputBinding:
      prefix: "--ntgrby"
    doc: |
      Column(s) from the Seurat object metadata to define the variable(s) that should
      be integrated out when running multiple datasets integration with harmony. May
      include columns from the extra metadata added with --metadata parameter. Ignored
      if --ntgr is not set to harmony.
      Default: new.ident

  highly_var_genes_count:
    type: int?
    inputBinding:
      prefix: "--highvargenes"
    doc: |
      Number of highly variable genes used in datasets integration, scaling and
      dimensionality reduction.
      Default: 3000

  regress_mito_perc:
    type: boolean?
    inputBinding:
      prefix: "--regressmt"
    doc: |
      Regress the percentage of transcripts mapped to mitochondrial genes as a
      confounding source of variation.
      Default: false

  regress_genes:
    type:
    - "null"
    - string
    - string[]
    inputBinding:
      prefix: "--regressgenes"
    doc: |
      Genes which expression should be regressed as a confounding source of variation.
      Default: None

  regress_cellcycle:
    type: boolean?
    inputBinding:
      prefix: "--regresscellcycle"
    doc: |
      Regress cell cycle scores as a confounding source of variation.
      Ignored if --cellcycle is not provided.
      Default: false

  dimensions:
    type:
    - "null"
    - int
    - int[]
    inputBinding:
      prefix: "--dimensions"
    doc: |
      Dimensionality to use in UMAP projection (from 1 to 50). If single value N
      is provided, use from 1 to N PCs. If multiple values are provided, subset to
      only selected PCs. In combination with --ntgr set to harmony, selected principle
      components will be used in Harmony integration.
      Default: from 1 to 10

  umap_spread:
    type: float?
    inputBinding:
      prefix: "--uspread"
    doc: |
      The effective scale of embedded points on UMAP. In combination with '--mindist'
      it determines how clustered/clumped the embedded points are.
      Default: 1

  umap_mindist:
    type: float?
    inputBinding:
      prefix: "--umindist"
    doc: |
      Controls how tightly the embedding is allowed compress points together on UMAP.
      Larger values ensure embedded points are moreevenly distributed, while smaller
      values allow the algorithm to optimise more accurately with regard to local structure.
      Sensible values are in the range 0.001 to 0.5.
      Default:  0.3

  umap_neighbors:
    type: int?
    inputBinding:
      prefix: "--uneighbors"
    doc: |
      Determines the number of neighboring points used in UMAP. Larger values will result
      in more global structure being preserved at the loss of detailed local structure.
      In general this parameter should often be in the range 5 to 50.
      Default: 30

  umap_metric:
    type:
    - "null"
    - type: enum
      symbols:
      - "euclidean"
      - "manhattan"
      - "chebyshev"
      - "minkowski"
      - "canberra"
      - "braycurtis"
      - "mahalanobis"
      - "wminkowski"
      - "seuclidean"
      - "cosine"
      - "correlation"
      - "haversine"
      - "hamming"
      - "jaccard"
      - "dice"
      - "russelrao"
      - "kulsinski"
      - "ll_dirichlet"
      - "hellinger"
      - "rogerstanimoto"
      - "sokalmichener"
      - "sokalsneath"
      - "yule"
    inputBinding:
      prefix: "--umetric"
    doc: |
      The metric to use to compute distances in high dimensional space for UMAP.
      Default: cosine

  umap_method:
    type:
    - "null"
    - type: enum
      symbols:
      - "uwot"
      - "uwot-learn"
      - "umap-learn"
    inputBinding:
      prefix: "--umethod"
    doc: |
      UMAP implementation to run. If set to 'umap-learn' use --umetric 'correlation'
      Default: uwot

  export_pdf_plots:
    type: boolean?
    inputBinding:
      prefix: "--pdf"
    doc: |
      Export plots in PDF.
      Default: false

  color_theme:
    type:
    - "null"
    - type: enum
      symbols:
      - "gray"
      - "bw"
      - "linedraw"
      - "light"
      - "dark"
      - "minimal"
      - "classic"
      - "void"
    inputBinding:
      prefix: "--theme"
    doc: |
      Color theme for all generated plots. One of gray, bw, linedraw, light,
      dark, minimal, classic, void.
      Default: classic

  verbose:
    type: boolean?
    inputBinding:
      prefix: "--verbose"
    doc: |
      Print debug information.
      Default: false

  export_h5seurat_data:
    type: boolean?
    inputBinding:
      prefix: "--h5seurat"
    doc: |
      Save Seurat data to h5seurat file.
      Default: false

  export_h5ad_data:
    type: boolean?
    inputBinding:
      prefix: "--h5ad"
    doc: |
      Save Seurat data to h5ad file.
      Default: false

  export_ucsc_cb:
    type: boolean?
    inputBinding:
      prefix: "--cbbuild"
    doc: |
      Export results to UCSC Cell Browser. Default: false

  low_memory:
    type: boolean?
    inputBinding:
      prefix: "--lowmem"
    doc: |
      Attempts to minimize RAM usage when integrating multiple datasets
      with SCTransform algorithm (slows down the computation). Ignored if
      '--ntgr' is not set to 'seurat' or if '--norm' is not set to either
      'sct' or 'sctglm'.
      Default: false

  output_prefix:
    type: string?
    inputBinding:
      prefix: "--output"
    doc: |
      Output prefix.
      Default: ./sc

  parallel_memory_limit:
    type: int?
    inputBinding:
      prefix: "--memory"
    doc: |
      Maximum memory in GB allowed to be shared between the workers
      when using multiple --cpus.
      Default: 32

  vector_memory_limit:
    type: int?
    default: 128
    doc: |
      Maximum vector memory in GB allowed to be used by R.
      Default: 128

  threads:
    type: int?
    inputBinding:
      prefix: "--cpus"
    doc: |
      Number of cores/cpus to use.
      Default: 1


outputs:

  elbow_plot_png:
    type: File?
    outputBinding:
      glob: "*_elbow.png"
    doc: |
      Elbow plot (from cells PCA).
      PNG format

  elbow_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_elbow.pdf"
    doc: |
      Elbow plot (from cells PCA).
      PDF format

  qc_dim_corr_plot_png:
    type: File?
    outputBinding:
      glob: "*_qc_dim_corr.png"
    doc: |
      Correlation plots between QC metrics and cells PCA components.
      PNG format

  qc_dim_corr_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_qc_dim_corr.pdf"
    doc: |
      Correlation plots between QC metrics and cells PCA components.
      PDF format

  umap_qc_mtrcs_plot_png:
    type: File?
    outputBinding:
      glob: "*_umap_qc_mtrcs.png"
    doc: |
      QC metrics on cells UMAP.
      PNG format

  umap_qc_mtrcs_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_umap_qc_mtrcs.pdf"
    doc: |
      QC metrics on cells UMAP.
      PDF format

  umap_plot_png:
    type: File?
    outputBinding:
      glob: "*_umap.png"
    doc: |
      Cells UMAP.
      PNG format

  umap_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_umap.pdf"
    doc: |
      Cells UMAP.
      PDF format

  umap_spl_ph_plot_png:
    type: File?
    outputBinding:
      glob: "*_umap_spl_ph.png"
    doc: |
      Split by cell cycle phase cells UMAP.
      PNG format

  umap_spl_ph_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_umap_spl_ph.pdf"
    doc: |
      Split by cell cycle phase cells UMAP.
      PDF format

  umap_spl_mito_plot_png:
    type: File?
    outputBinding:
      glob: "*_umap_spl_mito.png"
    doc: |
      Split by the percentage of transcripts mapped to mitochondrial genes cells UMAP.
      PNG format

  umap_spl_mito_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_umap_spl_mito.pdf"
    doc: |
      Split by the percentage of transcripts mapped to mitochondrial genes cells UMAP.
      PDF format

  umap_spl_umi_plot_png:
    type: File?
    outputBinding:
      glob: "*_umap_spl_umi.png"
    doc: |
      Split by the UMI per cell counts cells UMAP.
      PNG format

  umap_spl_umi_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_umap_spl_umi.pdf"
    doc: |
      Split by the UMI per cell counts cells UMAP.
      PDF format

  umap_spl_gene_plot_png:
    type: File?
    outputBinding:
      glob: "*_umap_spl_gene.png"
    doc: |
      Split by the genes per cell counts cells UMAP.
      PNG format

  umap_spl_gene_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_umap_spl_gene.pdf"
    doc: |
      Split by the genes per cell counts cells UMAP.
      PDF format

  umap_spl_idnt_plot_png:
    type: File?
    outputBinding:
      glob: "*_umap_spl_idnt.png"
    doc: |
      Split by dataset cells UMAP.
      PNG format

  umap_spl_idnt_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_umap_spl_idnt.pdf"
    doc: |
      Split by dataset cells UMAP.
      PDF format

  umap_spl_cnd_plot_png:
    type: File?
    outputBinding:
      glob: "*_umap_spl_cnd.png"
    doc: |
      Split by grouping condition cells UMAP.
      PNG format

  umap_spl_cnd_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_umap_spl_cnd.pdf"
    doc: |
      Split by grouping condition cells UMAP.
      PDF format

  umap_gr_cnd_spl_ph_plot_png:
    type: File?
    outputBinding:
      glob: "*_umap_gr_cnd_spl_ph.png"
    doc: |
      Grouped by condition split by cell cycle cells UMAP.
      PNG format

  umap_gr_cnd_spl_ph_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_umap_gr_cnd_spl_ph.pdf"
    doc: |
      Grouped by condition split by cell cycle cells UMAP.
      PDF format

  umap_gr_cnd_spl_mito_plot_png:
    type: File?
    outputBinding:
      glob: "*_umap_gr_cnd_spl_mito.png"
    doc: |
      Grouped by condition split by the percentage of transcripts mapped to mitochondrial genes cells UMAP.
      PNG format

  umap_gr_cnd_spl_mito_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_umap_gr_cnd_spl_mito.pdf"
    doc: |
      Grouped by condition split by the percentage of transcripts mapped to mitochondrial genes cells UMAP.
      PDF format

  umap_gr_cnd_spl_umi_plot_png:
    type: File?
    outputBinding:
      glob: "*_umap_gr_cnd_spl_umi.png"
    doc: |
      Grouped by condition split by the UMI per cell counts cells UMAP.
      PNG format

  umap_gr_cnd_spl_umi_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_umap_gr_cnd_spl_umi.pdf"
    doc: |
      Grouped by condition split by the UMI per cell counts cells UMAP.
      PDF format

  umap_gr_cnd_spl_gene_plot_png:
    type: File?
    outputBinding:
      glob: "*_umap_gr_cnd_spl_gene.png"
    doc: |
      Grouped by condition split by the genes per cell counts cells UMAP.
      PNG format

  umap_gr_cnd_spl_gene_plot_pdf:
    type: File?
    outputBinding:
      glob: "*_umap_gr_cnd_spl_gene.pdf"
    doc: |
      Grouped by condition split by the genes per cell counts cells UMAP.
      PDF format

  ucsc_cb_config_data:
    type: Directory?
    outputBinding:
      glob: "*_cellbrowser"
    doc: |
      Directory with UCSC Cellbrowser configuration data.

  ucsc_cb_html_data:
    type: Directory?
    outputBinding:
      glob: "*_cellbrowser/html_data"
    doc: |
      Directory with UCSC Cellbrowser html data.

  ucsc_cb_html_file:
    type: File?
    outputBinding:
      glob: "*_cellbrowser/html_data/index.html"
    doc: |
      HTML index file from the directory with UCSC Cellbrowser html data.

  seurat_data_rds:
    type: File
    outputBinding:
      glob: "*_data.rds"
    doc: |
      Reduced Seurat data in RDS format

  seurat_data_h5seurat:
    type: File?
    outputBinding:
      glob: "*_data.h5seurat"
    doc: |
      Reduced Seurat data in h5seurat format

  seurat_data_h5ad:
    type: File?
    outputBinding:
      glob: "*_data.h5ad"
    doc: |
      Reduced Seurat data in h5ad format

  stdout_log:
    type: stdout

  stderr_log:
    type: stderr


baseCommand: ["sc_rna_reduce.R"]

stdout: sc_rna_reduce_stdout.log
stderr: sc_rna_reduce_stderr.log


$namespaces:
  s: http://schema.org/

$schemas:
- https://github.com/schemaorg/schemaorg/raw/main/data/releases/11.01/schemaorg-current-http.rdf


label: "Single-cell RNA-Seq Dimensionality Reduction Analysis"
s:name: "Single-cell RNA-Seq Dimensionality Reduction Analysis"
s:alternateName: "Integrates multiple single-cell RNA-Seq datasets, reduces dimensionality using PCA"

s:downloadUrl: https://raw.githubusercontent.com/Barski-lab/workflows/master/tools/sc-rna-reduce.cwl
s:codeRepository: https://github.com/Barski-lab/workflows
s:license: http://www.apache.org/licenses/LICENSE-2.0

s:isPartOf:
  class: s:CreativeWork
  s:name: Common Workflow Language
  s:url: http://commonwl.org/

s:creator:
- class: s:Organization
  s:legalName: "Cincinnati Children's Hospital Medical Center"
  s:location:
  - class: s:PostalAddress
    s:addressCountry: "USA"
    s:addressLocality: "Cincinnati"
    s:addressRegion: "OH"
    s:postalCode: "45229"
    s:streetAddress: "3333 Burnet Ave"
    s:telephone: "+1(513)636-4200"
  s:logo: "https://www.cincinnatichildrens.org/-/media/cincinnati%20childrens/global%20shared/childrens-logo-new.png"
  s:department:
  - class: s:Organization
    s:legalName: "Allergy and Immunology"
    s:department:
    - class: s:Organization
      s:legalName: "Barski Research Lab"
      s:member:
      - class: s:Person
        s:name: Michael Kotliar
        s:email: mailto:misha.kotliar@gmail.com
        s:sameAs:
        - id: http://orcid.org/0000-0002-6486-3898


doc: |
  Single-cell RNA-Seq Dimensionality Reduction Analysis

  Integrates multiple single-cell RNA-Seq datasets, reduces dimensionality using PCA.


s:about: |
  usage: sc_rna_reduce.R [-h] --query QUERY [--metadata METADATA]
                                        [--barcodes BARCODES]
                                        [--cellcycle CELLCYCLE]
                                        [--norm {sct,log,sctglm}]
                                        [--ntgr {seurat,harmony,none}]
                                        [--ntgrby [NTGRBY [NTGRBY ...]]]
                                        [--highvargenes HIGHVARGENES]
                                        [--regressmt]
                                        [--regressgenes [REGRESSGENES [REGRESSGENES ...]]]
                                        [--regresscellcycle]
                                        [--dimensions [DIMENSIONS [DIMENSIONS ...]]]
                                        [--uspread USPREAD]
                                        [--umindist UMINDIST]
                                        [--uneighbors UNEIGHBORS]
                                        [--umetric {euclidean,manhattan,chebyshev,minkowski,canberra,braycurtis,mahalanobis,wminkowski,seuclidean,cosine,correlation,haversine,hamming,jaccard,dice,russelrao,kulsinski,ll_dirichlet,hellinger,rogerstanimoto,sokalmichener,sokalsneath,yule}]
                                        [--umethod {uwot,uwot-learn,umap-learn}]
                                        [--pdf] [--verbose] [--h5seurat]
                                        [--h5ad] [--cbbuild] [--lowmem]
                                        [--output OUTPUT]
                                        [--theme {gray,bw,linedraw,light,dark,minimal,classic,void}]
                                        [--cpus CPUS] [--memory MEMORY]

  Single-cell RNA-Seq Dimensionality Reduction Analysis

  optional arguments:
    -h, --help            show this help message and exit
    --query QUERY         Path to the RDS file to load Seurat object from. This
                          file should include genes expression information
                          stored in the RNA assay.
    --metadata METADATA   Path to the TSV/CSV file to optionally extend Seurat
                          object metadata with categorical values using samples
                          identities. First column - 'library_id' should
                          correspond to all unique values from the 'new.ident'
                          column of the loaded Seurat object. If any of the
                          provided in this file columns are already present in
                          the Seurat object metadata, they will be overwritten.
                          When combined with --barcodes parameter, first the
                          metadata will be extended, then barcode filtering will
                          be applied. Default: no extra metadata is added
    --barcodes BARCODES   Path to the TSV/CSV file to optionally prefilter and
                          extend Seurat object metadata be selected barcodes.
                          First column should be named as 'barcode'. If file
                          includes any other columns they will be added to the
                          Seurat object metadata ovewriting the existing ones if
                          those are present. Default: all cells used, no extra
                          metadata is added
    --cellcycle CELLCYCLE
                          Path to the TSV/CSV file with the information for cell
                          cycle score assignment. First column - 'phase', second
                          column 'gene_id'. If loaded Seurat object already
                          includes cell cycle scores in 'S.Score' and
                          'G2M.Score' metatada columns they will be removed.
                          Default: skip cell cycle score assignment.
    --norm {sct,log,sctglm}
                          Normalization method applied to genes expression
                          counts. If loaded Seurat object includes multiple
                          datasets, normalization will be run independently for
                          each of them, unless integration is disabled with
                          'none' or set to 'harmony' Default: sct
    --ntgr {seurat,harmony,none}
                          Integration method used for joint analysis of multiple
                          datasets. Automatically set to 'none' if loaded Seurat
                          object includes only one dataset. Default: seurat
    --ntgrby [NTGRBY [NTGRBY ...]]
                          Column(s) from the Seurat object metadata to define
                          the variable(s) that should be integrated out when
                          running multiple datasets integration with harmony.
                          May include columns from the extra metadata added with
                          --metadata parameter. Ignored if --ntgr is not set to
                          harmony. Default: new.ident
    --highvargenes HIGHVARGENES
                          Number of highly variable genes used in datasets
                          integration, scaling and dimensionality reduction.
                          Default: 3000
    --regressmt           Regress the percentage of transcripts mapped to
                          mitochondrial genes as a confounding source of
                          variation. Default: false
    --regressgenes [REGRESSGENES [REGRESSGENES ...]]
                          Genes which expression should be regressed as a
                          confounding source of variation. Default: None
    --regresscellcycle    Regress cell cycle scores as a confounding source of
                          variation. Ignored if --cellcycle is not provided.
                          Default: false
    --dimensions [DIMENSIONS [DIMENSIONS ...]]
                          Dimensionality to use in UMAP projection (from 1 to
                          50). If single value N is provided, use from 1 to N
                          PCs. If multiple values are provided, subset to only
                          selected PCs. In combination with --ntgr set to
                          harmony, selected principle components will be used in
                          Harmony integration. Default: from 1 to 10
    --uspread USPREAD     The effective scale of embedded points on UMAP. In
                          combination with '--mindist' it determines how
                          clustered/clumped the embedded points are. Default: 1
    --umindist UMINDIST   Controls how tightly the embedding is allowed compress
                          points together on UMAP. Larger values ensure embedded
                          points are moreevenly distributed, while smaller
                          values allow the algorithm to optimise more accurately
                          with regard to local structure. Sensible values are in
                          the range 0.001 to 0.5. Default: 0.3
    --uneighbors UNEIGHBORS
                          Determines the number of neighboring points used in
                          UMAP. Larger values will result in more global
                          structure being preserved at the loss of detailed
                          local structure. In general this parameter should
                          often be in the range 5 to 50. Default: 30
    --umetric {euclidean,manhattan,chebyshev,minkowski,canberra,braycurtis,mahalanobis,wminkowski,seuclidean,cosine,correlation,haversine,hamming,jaccard,dice,russelrao,kulsinski,ll_dirichlet,hellinger,rogerstanimoto,sokalmichener,sokalsneath,yule}
                          The metric to use to compute distances in high
                          dimensional space for UMAP. Default: cosine
    --umethod {uwot,uwot-learn,umap-learn}
                          UMAP implementation to run. If set to 'umap-learn' use
                          --umetric 'correlation' Default: uwot
    --pdf                 Export plots in PDF. Default: false
    --verbose             Print debug information. Default: false
    --h5seurat            Save Seurat data to h5seurat file. Default: false
    --h5ad                Save Seurat data to h5ad file. Default: false
    --cbbuild             Export results to UCSC Cell Browser. Default: false
    --lowmem              Attempts to minimize RAM usage when integrating
                          multiple datasets with SCTransform algorithm (slows
                          down the computation). Ignored if '--ntgr' is not set
                          to 'seurat' or if '--norm' is not set to either 'sct'
                          or 'sctglm'. Default: false
    --output OUTPUT       Output prefix. Default: ./sc
    --theme {gray,bw,linedraw,light,dark,minimal,classic,void}
                          Color theme for all generated plots. Default: classic
    --cpus CPUS           Number of cores/cpus to use. Default: 1
    --memory MEMORY       Maximum memory in GB allowed to be shared between the
                          workers when using multiple --cpus. Default: 32