RunSingleR: Annotate single cells using SingleR

In zh542370159/SCP: Single Cell Pipeline

Annotate single cells using SingleR

Description

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.
aggr.ref, aggr.args	Arguments controlling the aggregation of the references prior to annotation, see trainSingleR.
quantile, fine.tune, tune.thresh, prune	Further arguments to pass to classifySingleR.
BPPARAM	A BiocParallelParam object specifying how parallelization should be performed in other steps, see ?trainSingleR and ?classifySingleR for more details.

Examples

data("panc8_sub")
# Simply convert genes from human to mouse and preprocess the data
genenames <- make.unique(capitalize(rownames(panc8_sub), force_tolower = TRUE))
panc8_sub <- RenameFeatures(panc8_sub, newnames = genenames)
panc8_sub <- check_srtMerge(panc8_sub, batch = "tech")[["srtMerge"]]

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

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

zh542370159/SCP documentation built on Nov. 22, 2023, 2:34 a.m.