PredictCellTypeProbability: Applies trained models to get celltype probabilities

In bimberlabinternal/RIRA_classification: A package for the Rhesus Immune Reference Atlas (RIRA): A multi-tissue single-cell landscape of immune cells

Applies trained models to get celltype probabilities

Description

Applies trained models to get celltype probabilities.

Usage

PredictCellTypeProbability(
  seuratObj,
  models,
  fieldName = "RIRA_Consensus",
  batchSize = 20000,
  assayName = "RNA",
  minimum_probability = 0.5,
  minimum_delta = 0.25
)

Arguments

seuratObj	The Seurat Object to be updated
models	A named vector of models, where the values are the modelName (for built-in model), or filePath to an RDS file. The names of the vector should be the cell-type label for cells scored positive by the classifier.
fieldName	The name of the metadata column to store the result
batchSize	To conserve memory, data will be chunked into batches of at most this many cells
assayName	The assay holding gene expression data
minimum_probability	The minimum probability for a confident cell type assignment
minimum_delta	The minimum difference in probabilities necessary to call one celltype over another.

bimberlabinternal/RIRA_classification documentation built on April 14, 2025, 5:59 p.m.