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# train_transfer_model

Source: `R/data_projection.R`
`train_transfer_model.Rd`

Train a machine learning model to transfer cell labels (this function implements the `caret` workflow)

## Usage

    train_transfer_model(
      fcd,
      input_type,
      data_slot,
      cluster_slot,
      cluster_var,
      method = "knn",
      tuneLength = 5,
      trControl = caret::trainControl(method = "cv"),
      seed = 91
    )

## Arguments

fcd

  flow cytometry dataset.

input_type

  Data to use for the calculation of the UMAP, e.g. `expr` or `pca`.

data_slot

  Name of the `input_type` data slot to use e.g. `orig`, if no prefix was added.

cluster_slot

  string specifying which clustering slot to use to find variable specified in `cluster_var`.

cluster_var

  string specifying variable name in `cluster_slot` that identifies cell population labels to be used (e.g. clusters or metaclusters).

method

  A string specifying which classification or regression model to use, by default `method = "knn"`. See `train` for possible values.

tuneLength

  An integer denoting the amount of granularity in the tuning parameter grid, default `tuneLength = 5`.

trControl

  A list of values that define how this function acts, default `trControl = caret::trainControl(method = "cv")`. See `trainControl` and <http://topepo.github.io/caret/using-your-own-model-in-train.html>.

seed

  A seed is set for reproducibility.

## Value

`train_transfer_model` returns a `fcd` with the model and associated visualizations saved in `fcd$extras$lt_model`.

## Details

train_transfer_model

The `train_transfer_model` uses `train`.