learnUMAP

Projects new samples on a UMAP calculated previously for a reference data set with the same parameters as the new sample. Before executing this function, runUMAP needs to be run with ret_model = TRUE for the reference data set.

Usage

learnUMAP(
  fcd,
  input_type,
  data_slot,
  fcd_model,
  nEpochs = 100,
  prefix = NULL,
  nThreads = 32,
  seed = 91
)

Arguments

fcd

Flow cytometry dataset for which the UMAP coordinates should be predicted.

input_type

Data to use for the calculation of the UMAP, e.g. expr or pca. This should be the same which has been used for calculating the UMAP of the reference data set.

data_slot

Name of the input_type data slot to use e.g. orig, if no prefix was added. This should be the same which has been used for calculating the UMAP of the reference data set.

fcd_model

Flow cytometry reference data set containing data associated with an existing embedding in fcd_model$extras.

nEpochs

Number of epochs to use during the optimization of the embedded coordinates. A value between 30 - 100 is a reasonable trade off between speed and thoroughness. By default, this value is set to one third the number of epochs used to build the model.

prefix

Prefix for the name of the dimensionality reduction.

nThreads

Number of threads to use, (except during stochastic gradient descent). By default nThreads = 32.

seed

A seed is set for reproducibility.

Value

learnUMAP() returns a fcd with the predicted UMAP coordinates saved in fcd$umap$expr_orig, if no prefix was set.

Details

learnUMAP

learnUMAP() uses umap_transform to project new samples contained in fcd on the embedding previously calculated in a reference data set, fcd_model, using coderunUMAP.