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 |
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 |
|
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 |