gsva_cellIdentify: Cell-type naming with GSVA

In scMappR: Single Cell Mapper

Cell-type naming with GSVA

Description

This function computes the mean expression of every cell-type before predicting the most likely cell-type using the GSVA method.

Usage

gsva_cellIdentify(
  pbmc,
  theSpecies,
  naming_preference = -9,
  rda_path = "",
  toSave = FALSE
)

Arguments

pbmc	Processed Seurat object without named cells.
theSpecies	"human" or "mouse" – it will determine which species cell-type markers will originate from.
naming_preference	Once top cell-type markers are identified, naming_preferences will then extract CT markers within a more appropriate tissue type.
rda_path	Path to pre-computed cell-type .gmt files (rda objects).
toSave	If scMappR is allowed to write files and directories.

Details

This function inputs a Seurat object and uses the average normalized expression of each gene in each cluster to identify cell-types using the GSVA method.

Value

List with the following elements:

cellMarker	Most likely cell-types predicted from CellMarker database.
panglao	Most likely cell-types predicted from Panglao database.
avg_expression	Average expression of each gene in each cell-type.

Examples

data(sm)
toProcess <- list(example = sm)
tst1 <- process_from_count(countmat_list = toProcess,name = "testProcess",
                            theSpecies = "mouse")
cellnames <- gsva_cellIdentify(pbmc = tst1, theSpecies = "mouse",
 naming_preference = "brain", rda_path = "")


 

scMappR documentation built on July 9, 2023, 6:26 p.m.