Annotate single cells using SingleR

Usage

RunSingleR(
  srt_query,
  srt_ref,
  query_group = NULL,
  ref_group = NULL,
  query_assay = "RNA",
  ref_assay = "RNA",
  genes = "de",
  de.method = "wilcox",
  sd.thresh = 1,
  de.n = NULL,
  aggr.ref = FALSE,
  aggr.args = list(),
  quantile = 0.8,
  fine.tune = TRUE,
  tune.thresh = 0.05,
  prune = TRUE,
  BPPARAM = BiocParallel::bpparam()
)

Arguments

srt_query: An object of class Seurat to be annotated with cell types.
srt_ref: An object of class Seurat storing the reference cells.
query_group: A character vector specifying the column name in the `srt_query` metadata that represents the cell grouping.
ref_group: A character vector specifying the column name in the `srt_ref` metadata that represents the cell grouping.
query_assay: A character vector specifying the assay to be used for the query data. Defaults to the default assay of the `srt_query` object.
ref_assay: A character vector specifying the assay to be used for the reference data. Defaults to the default assay of the `srt_ref` object.
genes: "genes" parameter in SingleR function.
de.method: "de.method" parameter in SingleR function.
sd.thresh: Deprecated and ignored.
de.n: An integer scalar specifying the number of DE genes to use when genes="de". If de.method="classic", defaults to 500 * (2/3) ^ log2(N) where N is the number of unique labels. Otherwise, defaults to 10. Ignored if genes is a list of markers/DE genes.
aggr.ref, aggr.args: Arguments controlling the aggregation of the references prior to annotation, see trainSingleR.
quantile: "quantile" parameter in SingleR function.
fine.tune: "fine.tune" parameter in SingleR function.
tune.thresh: "tune.thresh" parameter in SingleR function.
prune: "prune" parameter in SingleR function.
BPPARAM: A BiocParallelParam object specifying how parallelization should be performed in other steps, see ?trainSingleR and ?classifySingleR for more details.

Examples

if (FALSE) { # \dontrun{
data("panc8_sub")
# Simply convert genes from human to mouse and preprocess the data
genenames <- make.unique(
  capitalize(
    rownames(panc8_sub),
    force_tolower = TRUE
  )
)
names(genenames) <- rownames(panc8_sub)
panc8_sub <- RenameFeatures(
  panc8_sub,
  newnames = genenames
)
panc8_sub <- check_srt_merge(
  panc8_sub,
  batch = "tech"
)[["srt_merge"]]

# Annotation
data("pancreas_sub")
pancreas_sub <- standard_scop(pancreas_sub)
pancreas_sub <- RunSingleR( # bug
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  query_group = "Standardclusters",
  ref_group = "celltype",
)
CellDimPlot(
  pancreas_sub,
  group.by = "singler_annotation"
)

pancreas_sub <- RunSingleR( # bug
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  query_group = NULL,
  ref_group = "celltype"
)
CellDimPlot(
  pancreas_sub,
  group.by = "singler_annotation"
)
} # }