Performs automated cell annotation on query datasets using reference data

SingleR iterates through singular cells and iterates through probabilitiy comparisons to identify which cell type the query cell is likely to be. If a probably cell type cannot be discovered then

perform.singleR.annotation(
  object,
  assay = "RAW",
  slot = "counts",
  ref,
  log.transform.query = TRUE,
  tpm.transform.query = FALSE,
  log.transform.ref = TRUE,
  tpm.transform.ref = FALSE,
  ref.labels,
  column.suffix = "1",
  verbose = FALSE,
  seed = 1234,
  ...
)

Arguments

object: IBRAP S4 class object
assay: Character. String containing indicating which assay to use
slot: Character. String indicating which slot within the assay should be sourced
ref: AnyMatrix. A matrix of the reference datasets, if data is end-bias then it should be log normalised, if it is full-length then it requires tpm normalisation. Both can be completed within this function.
ref.labels: Vector. The cluster assignments for the reference data. Default = NULL
column.suffix: Character. A suffix to append the end of the new metadata columns if this functiuons is to be used multiple times. Default = '1'
verbose: Logical Should function messages be printed?
seed: Numeric. What should the seed be set as. Default = 1234
...: arguments to be passed to singleR::SingleR
log.transform: Boolean. Should the reference data be log transformed. Default = TRUE
tpm.transform: Boolean. Should the reference data be tpm normalised. Default = FALSE

Value

Produces a new 'methods' assay containing normalised, scaled and HVGs.

Examples


object <- perform.singleR.annotation(object = object, ref = reference_matrix, ref.labels = metadata_reference$celltype)
#> Error in is(object, "IBRAP"): object 'object' not found