ProjectUMAP: Project query into UMAP coordinates of a reference
In mrod0101/seurat: Tools for Single Cell Genomics

This function will take a query dataset and project it into the coordinates of a provided reference UMAP. This is essentially a wrapper around two steps:

FindNeighbors - Find the nearest reference cell neighbors and their distances for each query cell.
RunUMAP - Perform umap projection by providing the neighbor set calculated above and the umap model previously computed in the reference.

ProjectUMAP(query, ...)

## Default S3 method:
ProjectUMAP(
  query,
  query.dims = NULL,
  reference,
  reference.dims = NULL,
  k.param = 30,
  nn.method = "annoy",
  n.trees = 50,
  annoy.metric = "cosine",
  l2.norm = FALSE,
  cache.index = TRUE,
  index = NULL,
  neighbor.name = "query_ref.nn",
  reduction.model,
  ...
)

## S3 method for class 'DimReduc'
ProjectUMAP(
  query,
  query.dims = NULL,
  reference,
  reference.dims = NULL,
  k.param = 30,
  nn.method = "annoy",
  n.trees = 50,
  annoy.metric = "cosine",
  l2.norm = FALSE,
  cache.index = TRUE,
  index = NULL,
  neighbor.name = "query_ref.nn",
  reduction.model,
  ...
)

## S3 method for class 'Seurat'
ProjectUMAP(
  query,
  query.reduction,
  query.dims = NULL,
  reference,
  reference.reduction,
  reference.dims = NULL,
  k.param = 30,
  nn.method = "annoy",
  n.trees = 50,
  annoy.metric = "cosine",
  l2.norm = FALSE,
  cache.index = TRUE,
  index = NULL,
  neighbor.name = "query_ref.nn",
  reduction.model,
  reduction.name = "ref.umap",
  reduction.key = "refUMAP_",
  ...
)

`query`	Query dataset
`...`	Additional parameters to `RunUMAP`
`query.dims`	Dimensions (columns) to use from query
`reference`	Reference dataset
`reference.dims`	Dimensions (columns) to use from reference
`k.param`	Defines k for the k-nearest neighbor algorithm
`nn.method`	Method for nearest neighbor finding. Options include: rann, annoy
`n.trees`	More trees gives higher precision when using annoy approximate nearest neighbor search
`annoy.metric`	Distance metric for annoy. Options include: euclidean, cosine, manhattan, and hamming
`l2.norm`	Take L2Norm of the data
`cache.index`	Include cached index in returned Neighbor object (only relevant if return.neighbor = TRUE)
`index`	Precomputed index. Useful if querying new data against existing index to avoid recomputing.
`neighbor.name`	Name to store neighbor information in the query
`reduction.model`	`DimReduc` object that contains the umap model
`query.reduction`	Name of reduction to use from the query for neighbor finding
`reference.reduction`	Name of reduction to use from the reference for neighbor finding
`reduction.name`	Name of projected UMAP to store in the query
`reduction.key`	Value for the projected UMAP key