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Cell scoring

Source: R/CellScoring.R
CellScoring.Rd

This function performs cell scoring on a Seurat object. It calculates scores for a given set of features and stores them in meta.data and/or a score assay, depending on new_assay and store_metadata.

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",
  backend = c("cpp", "r"),
  cpp_strategy = c("sparse", "topk", "full"),
  classification = TRUE,
  name = "",
  new_assay = FALSE,
  store_metadata = NULL,
  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. When the object also contains ChromatinAssay, the default assay and additional ChromatinAssay will be preprocessed sequentially.

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", "CytoTRACE2". MSigDB subcollections can be requested as "MSigDB_<collection>", such as "MSigDB_H" for human Hallmark and "MSigDB_MH" for mouse Hallmark. Note: "CytoTRACE2" is species-independent and downloads pre-trained model data required by RunCytoTRACE.

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", "UCell", "GSVA", "ssGSEA", "zscore", "PLAGE", or "VISION". Multiple methods can be supplied at once; in that case each method is run separately and stored with a method suffix such as "GO_AUCell" or "GO_GSVA". Default is "Seurat".

backend

Scoring backend. "cpp" is the default for supported methods. "r" uses the original package implementation. "cpp" currently supports method = "Seurat", method = "AUCell", method = "GSVA", method = "ssGSEA", method = "zscore", and method = "PLAGE". method = "UCell" and method = "VISION" fall back to "r" when backend is not explicitly set.

cpp_strategy

C++ AUCell ranking strategy. "sparse" ranks non-zero genes and approximates zero ties, "topk" ranks only genes that can contribute to AUCell AUC, and "full" ranks all genes.

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.

store_metadata

Whether to also store score columns in meta.data. When NULL, manual features = list(...) input is stored in meta.data by default, while database-derived results stay assay-only when new_assay = TRUE.

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)
features_all <- rownames(pancreas_sub)
pancreas_sub <- CellScoring(
  pancreas_sub,
  features = list(
    A = features_all[1:100],
    B = features_all[101:200]
  ),
  method = "AUCell",
  name = "test"
)
CellDimPlot(pancreas_sub, "test_classification")

FeatureDimPlot(
  pancreas_sub,
  features = "test_A"
)

pancreas_sub <- CellScoring(
  pancreas_sub,
  features = list(A = features_all[1:100]),
  method = c("AUCell", "GSVA")
)
FeatureStatPlot(
  pancreas_sub,
  stat.by = c("AUCell_A", "GSVA_A"),
  group.by = "CellType",
  plot.by = "feature",
  plot_type = "violin",
  stack = TRUE
)

FeatureDimPlot(
  pancreas_sub,
  features = c("AUCell_A", "GSVA_A"),
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

GroupHeatmap(
  pancreas_sub,
  features = c("AUCell_A", "GSVA_A", "Sox9", "Anxa2", "Bicc1"),
  group.by = "CellType"
)

data(panc8_sub)
panc8_sub <- integration_scop(
  panc8_sub,
  batch = "tech",
  integration_method = "Harmony"
)

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

panc8_sub <- integration_scop(
  panc8_sub,
  assay = "GO",
  batch = "tech",
  integration_method = "Harmony"
)
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 = "AUCell",
  name = "GO",
  new_assay = TRUE
)
pancreas_sub <- standard_scop(
  pancreas_sub,
  assay = "GO"
)

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