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# runUMAP

Source: `R/dim_reduction.R`
`runUMAP.Rd`

Perform a UMAP dimensionality reduction on the expression values or pca results.

## Usage

    runUMAP(
      fcd,
      input_type,
      data_slot,
      nNeighbors = 15,
      nComponents = 2,
      min_dist = 0.2,
      metric = "euclidean",
      seed = 91,
      prefix = NULL,
      nThreads = 32,
      nPC = ncol(fcd[[input_type]][[data_slot]]),
      ret_model = FALSE,
      markers = colnames(fcd$expr[[data_slot]]),
      discard = FALSE
    )

## Arguments

fcd

  flow cytometry dataset.

input_type

  data to use for the calculation of the UMAP, e.g. "expr" or "pca".

data_slot

  data slot to use for the calculation of the UMAP, e.g. "orig" or "norm".

nNeighbors

  Number of items that define the neighborhood around each point. Default = 15.

nComponents

  Number of components for UMAP calculation. Default = 2.

min_dist

  Min_dist for UMAP calculation. Default = 0.2.

metric

  Metric for UMAP calculation. Default = "euclidean".

seed

  A seed is set for reproducibility.

prefix

  Optional prefix for the slot name of the output.

nThreads

  Number of threads to be used in the UMAP calculation. Default = 32.

nPC

  Number of PCs used in the UMAP calculation. Default = All.

ret_model

  LOGICAL if the UMAP model should be saved for future projection of the data using `learnUMAP`.

markers

  Vector of marker names to include or exclude from UMAP calculation according to the discard parameter. See functions `used_markers` and `measured_markers` for the extraction of markers directly from the condor object.

discard

  LOGICAL to decide if the markers specified should be included, "F", or excluded, "T", from the UMAP calculation. Default = F.

## Value

The function returns a fcd including a data frame with the UMAP coordinates saved in `fcd$umap`. The name of the output consists of the prefix (if given) and the data slot. If a `nPC` is given it will be added to the output name.

## Details

runUMAP

See [Melville J (2023). uwot: The Uniform Manifold Approximation and Projection (UMAP) Method for Dimensionality Reduction. R package version 0.1.16](https://github.com/jlmelville/uwot) for more details on the umap method.