Uniform Manifold Approximation and Projection(UMAP) algorithm for
dimension reduction.

getUMAP(
inSCE,
useAssay = "counts",
useAltExp = NULL,
sample = NULL,
reducedDimName = "UMAP",
logNorm = TRUE,
nNeighbors = 30,
nIterations = 200,
alpha = 1,
minDist = 0.01,
spread = 1,
pca = TRUE,
initialDims = 50
)

## Arguments

inSCE |
Input SingleCellExperiment object. |

useAssay |
Assay to use for UMAP computation. If `useAltExp` is
specified, `useAssay` has to exist in
`assays(altExp(inSCE, useAltExp))` . Default `"counts"` . |

useAltExp |
The subset to use for UMAP computation, usually for the
selected.variable features. Default `NULL` . |

sample |
Character vector. Indicates which sample each cell belongs to.
If given a single character, will take the annotation from
`colData` . Default `NULL` . |

reducedDimName |
A name to store the results of the dimension reduction
coordinates obtained from this method. Default `"UMAP"` . |

logNorm |
Whether the counts will need to be log-normalized prior to
generating the UMAP via `logNormCounts` . Default
`TRUE` . |

nNeighbors |
The size of local neighborhood used for manifold
approximation. Larger values result in more global views of the manifold,
while smaller values result in more local data being preserved. Default
`30` . See `?uwot::umap` for more information. |

nIterations |
The number of iterations performed during layout
optimization. Default is `200` . |

alpha |
The initial value of "learning rate" of layout optimization.
Default is `1` . |

minDist |
The effective minimum distance between embedded points.
Smaller values will result in a more clustered/clumped embedding where nearby
points on the manifold are drawn closer together, while larger values will
result on a more even dispersal of points. Default `0.01` . See
`?uwot::umap` for more information. |

spread |
The effective scale of embedded points. In combination with
minDist, this determines how clustered/clumped the embedded points are.
Default `1` . See `?uwot::umap` for more information. |

pca |
Logical. Whether to perform dimension reduction with PCA before
UMAP. Default `TRUE` |

initialDims |
Number of dimensions from PCA to use as input in UMAP.
Default `50` . |

## Value

A SingleCellExperiment object with UMAP computation
updated in `reducedDim(inSCE, reducedDimName)`

.

## Examples

#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

#> Also defined by 'spam'

#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

#> Also defined by 'spam'

#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'

#> Also defined by 'spam'

reducedDims(umap_res)

#> List of length 1
#> names(1): UMAP