`R/generics.R`

, `R/dimensional_reduction.R`

`ProjectUMAP.Rd`

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, ...)
# S3 method for default
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 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 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