ModuleScoreCalculator

Calculate the module scores for each cell

The module scores are calculated by biopipen.utils::RunModuleScoring() with the scoring method specified by env.defaults.method (or per module by method in the module dict):

Scores from different methods are not comparable with each other — only the column names are consistent.

Input

  • srtobj: The seurat object loaded by SeuratClustering

Output

  • rdsfile: Default: {{in.srtobj | stem}}.qs.
    The seurat object with module scores added to the metadata.

Environment Variables

  • defaults (ns): The default parameters for modules.
    • method (choice): Default: seurat.
      The scoring method to use, one of seurat, ucell, aucell, ssgsea, jasmine, scse or scps.
      Can be overridden per module.
    • features: The features (genes) to calculate the scores.
      A comma-separated string of genes, e.g.
      "HAVCR2,ENTPD1,LAYN,LAG3", yields one score column named by the module key. A list of gene vectors, e.g.
      ["HAVCR2","ENTPD1"], yields one column per element, named {key}1, {key}2, ... if unnamed, or {key}_{name} if named. You can also specify cc.genes, cc.genes.updated.2019 or cc.genes.mouse (or use kind: "cc", with features defaulting to cc.genes) to calculate cell cycle scores. Three columns will be added to the metadata: {key}_S.Score, {key}_G2M.Score and {key}_Phase. This works for all methods. Use one of the reserved no-prefix keys ("_", "-", "*" or "#") as the module key to keep the plain S.Score, G2M.Score and Phase names. For diffusion map modules (kind: "dm"), this is the number of components to keep (default 2).
    • nbin (type=int): Default: 24.
      Number of bins of aggregate expression levels for all analyzed features. Only for the seurat method.
    • ctrl (type=int): Default: 100.
      Number of control features selected from the same bin per analyzed feature. Only for the seurat method.
    • k (flag): Default: False.
      Use feature clusters returned from DoKMeans.
      Only for the seurat method.
    • assay: The assay to use (for tools that accept it).
    • seed (type=int): Default: 8525.
      Set a random seed. Only for the seurat method.
    • search (flag): Default: False.
      Search for symbol synonyms for features that don't match features in object? Only for the seurat method.
    • <more>: Other parameters, passed to the underlying tool of the method. For seurat, they go to Seurat::AddModuleScore() or Seurat::CellCycleScoring() (see https://satijalab.org/seurat/reference/addmodulescore and https://satijalab.org/seurat/reference/cellcyclescoring).
      For ucell: maxRank, w_neg and slot (default "counts", not layer). For aucell: aucMaxRank, plotStats. For ssgsea: kcdf, verbose, min.sz, max.sz, tau, etc. For scse/scps: layer (default "data"). scps uses the bundled scPS implementation, which runs PCA on the scale.data of the object, so the signature genes must be scaled first (ScaleData(features = ...) or SCTransform); it also requires the GSEABase package. For diffmap modules (kind: "dm"): n_pcs (use PCA embeddings instead of assay data) and other arguments passed to destiny::DiffusionMap().
      The agg and keep parameters from old versions are removed and ignored.
  • ncores (type=int): Default: 1.
    The number of cores to use for reading and writing the seurat object.

  • modules (type=json): Default: {}.
    The modules to calculate the scores.
    Keys are the names of the expression programs and values are the dicts inherited from env.defaults.
    Here are some examples -

    {
    "CellCycleMouse": {"features": "cc.genes.mouse"},
    "CellCycle": {"kind": "cc", "features": "cc.genes.updated.2019"},
    "TcellState": {
        "features": {
            "Exhaustion": ["HAVCR2", "ENTPD1", "LAYN", "LAG3"],
            "Activation": ["IFNG"]
        },
        "method": "ucell",
        "maxRank": 500
    },
    "Proliferation": {"features": "STMN1,TUBB"},
    "DC": {"kind": "dm"}
}

    For CellCycle, the columns CellCycle_S.Score, CellCycle_G2M.Score and CellCycle_Phase will be added to the metadata.

    For TcellState, the columns TcellState_Exhaustion and TcellState_Activation will be added to the metadata, one for each program in the named features list (the list values are gene vectors, not comma-separated strings).

    For DC, a diffusion map will be calculated with destiny (regardless of method), and the first 2 components will be added as the DC reduction as well as the DC_1 and DC_2 columns to the metadata. dm is a shortcut for diffmap/diffusion_map.
    You can later plot the diffusion map by using reduction = "DC" in env.dimplots in SeuratClusterStats.
    This requires SingleCellExperiment and destiny R packages.
    - post_mutaters (type=json): Default: {}.
    The mutaters to mutate the metadata after calculating the module scores.
    The mutaters will be applied in the order specified.
    This is useful when you want to create new scores based on the calculated module scores.

Metadata

The metadata of the Seurat object will be updated with the module scores:

ModuleScoreCalculator-metadata