Cell scoring

This function performs cell scoring on a Seurat object. It calculates scores for a given set of features and adds the scores as metadata to the Seurat object.

Usage

CellScoring(
  srt,
  features = NULL,
  layer = "data",
  assay = NULL,
  split.by = NULL,
  IDtype = "symbol",
  species = "Homo_sapiens",
  db = "GO_BP",
  termnames = NULL,
  db_update = FALSE,
  db_version = "latest",
  convert_species = TRUE,
  Ensembl_version = NULL,
  mirror = NULL,
  minGSSize = 10,
  maxGSSize = 500,
  method = "Seurat",
  classification = TRUE,
  name = "",
  new_assay = FALSE,
  seed = 11,
  cores = 1,
  verbose = TRUE,
  ...
)

Arguments

srt: A Seurat object.
features: A named list of feature lists for scoring. If NULL, db will be used to create features sets.
layer: Which layer to use. Default is data.
assay: Which assay to use. If NULL, the default assay of the Seurat object will be used.
split.by: Name of a column in meta.data column to split plot by. Default is NULL.
IDtype: A character vector specifying the type of gene IDs in the srt object or geneID argument. This argument is used to convert the gene IDs to a different type if IDtype is different from result_IDtype.
species: A character vector specifying the species for which the gene annotation databases should be prepared. Can be "Homo_sapiens" or "Mus_musculus".
db: A character vector specifying the annotation sources to be included in the gene annotation databases. Can be one or more of "GO", "GO_BP", "GO_CC", "GO_MF", "KEGG", "WikiPathway", "Reactome", "CORUM", "MP", "DO", "HPO", "PFAM", "CSPA", "Surfaceome", "SPRomeDB", "VerSeDa", "TFLink", "hTFtarget", "TRRUST", "JASPAR", "ENCODE", "MSigDB", "CellTalk", "CellChat", "Chromosome", "GeneType", "Enzyme", "TF".
termnames: A vector of term names to be used from the database. Default is NULL, in which case all features from the database are used.
db_update: Whether the gene annotation databases should be forcefully updated. If set to FALSE, the function will attempt to load the cached databases instead. Default is FALSE.
db_version: A character vector specifying the version of the gene annotation databases to be retrieved. Default is "latest".
convert_species: Whether to use a species-converted database when the annotation is missing for the specified species. Default is TRUE.
Ensembl_version: An integer specifying the Ensembl version. Default is NULL. If NULL, the latest version will be used.
mirror: Specify an Ensembl mirror to connect to. The valid options here are "www", "uswest", "useast", "asia".
minGSSize: The minimum size of a gene set to be considered in the enrichment analysis.
maxGSSize: The maximum size of a gene set to be considered in the enrichment analysis.
method: The method to use for scoring. Can be "Seurat", "AUCell", or "UCell". Default is "Seurat".
classification: Whether to perform classification based on the scores. Default is TRUE.
name: The name of the assay to store the scores in. Only used if new_assay is TRUE. Default is "".
new_assay: Whether to create a new assay for storing the scores. Default is FALSE.
seed: Random seed for reproducibility. Default is 11.
cores: The number of cores to use for parallelization with foreach::foreach. Default is 1.
verbose: Whether to print the message. Default is TRUE.
...: Additional arguments to be passed to the scoring methods.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-01-27 07:24:21] Start standard scop workflow...
#> ℹ [2026-01-27 07:24:21] Checking a list of <Seurat>...
#> ! [2026-01-27 07:24:21] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 07:24:21] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 07:24:23] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 07:24:23] Use the separate HVF from srt_list
#> ℹ [2026-01-27 07:24:23] Number of available HVF: 2000
#> ℹ [2026-01-27 07:24:24] Finished check
#> ℹ [2026-01-27 07:24:24] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 07:24:24] Perform pca linear dimension reduction
#> ℹ [2026-01-27 07:24:25] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 07:24:25] Reorder clusters...
#> ℹ [2026-01-27 07:24:25] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 07:24:25] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 07:24:28] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 07:24:31] Run scop standard workflow completed
features_all <- rownames(pancreas_sub)
pancreas_sub <- CellScoring(
  pancreas_sub,
  features = list(
    A = features_all[1:100],
    B = features_all[101:200]
  ),
  method = "Seurat",
  name = "test"
)
#> ℹ [2026-01-27 07:24:31] Start cell scoring
#> ℹ [2026-01-27 07:24:32] Data type is log-normalized
#> ℹ [2026-01-27 07:24:32] Number of feature lists to be scored: 2
#> ℹ [2026-01-27 07:24:33] Using 1 core
#> ⠙ [2026-01-27 07:24:33] Running for 1 [1/2] ■■■■■■■■■■■■■■■■                  5…
#> ✔ [2026-01-27 07:24:33] Completed 2 tasks in 135ms
#> 
#> ℹ [2026-01-27 07:24:33] Building results
#> ✔ [2026-01-27 07:24:33] Cell scoring completed
CellDimPlot(pancreas_sub, "test_classification")
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


FeatureDimPlot(pancreas_sub, "test_A")


if (FALSE) { # \dontrun{
data(panc8_sub)
panc8_sub <- integration_scop(
  panc8_sub,
  batch = "tech",
  integration_method = "Seurat"
)
CellDimPlot(
  panc8_sub,
  group.by = c("tech", "celltype")
)

panc8_sub <- CellScoring(
  panc8_sub,
  layer = "data",
  assay = "RNA",
  db = "GO_BP",
  species = "Homo_sapiens",
  minGSSize = 10,
  maxGSSize = 100,
  method = "Seurat",
  name = "GO",
  new_assay = TRUE
)

panc8_sub <- integration_scop(
  panc8_sub,
  assay = "GO",
  batch = "tech",
  integration_method = "Seurat"
)
CellDimPlot(
  panc8_sub,
  group.by = c("tech", "celltype")
)

pancreas_sub <- CellScoring(
  pancreas_sub,
  layer = "data",
  assay = "RNA",
  db = "GO_BP",
  species = "Mus_musculus",
  termnames = panc8_sub[["GO"]]@meta.features[, "termnames"],
  method = "Seurat",
  name = "GO",
  new_assay = TRUE
)
pancreas_sub <- standard_scop(
  pancreas_sub,
  assay = "GO"
)
CellDimPlot(pancreas_sub, "SubCellType")

pancreas_sub[["tech"]] <- "Mouse"
panc_merge <- integration_scop(
  srt_list = list(panc8_sub, pancreas_sub),
  assay = "GO",
  batch = "tech", integration_method = "Seurat"
)
CellDimPlot(
  srt = panc_merge,
  group.by = c("tech", "celltype", "SubCellType", "Phase")
)

genenames <- make.unique(
  thisutils::capitalize(
    rownames(panc8_sub[["RNA"]])
  ),
  force_tolower = TRUE
)
names(genenames) <- rownames(panc8_sub)
panc8_sub <- RenameFeatures(
  panc8_sub,
  newnames = genenames,
  assay = "RNA"
)
head(rownames(panc8_sub))
panc_merge <- integration_scop(
  srt_list = list(panc8_sub, pancreas_sub),
  assay = "RNA",
  batch = "tech", integration_method = "Seurat"
)
CellDimPlot(
  srt = panc_merge,
  group.by = c("tech", "celltype", "SubCellType", "Phase")
)
} # }

Usage

Arguments

See also

Examples