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 knearest 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 