Perform dataset integration using a precomputed Anchorset of specified low dimensional representations.
IntegrateEmbeddings(anchorset, ...) # S3 method for IntegrationAnchorSet IntegrateEmbeddings( anchorset, new.reduction.name = "integrated_dr", reductions = NULL, dims.to.integrate = NULL, k.weight = 100, weight.reduction = NULL, sd.weight = 1, sample.tree = NULL, preserve.order = FALSE, verbose = TRUE, ... ) # S3 method for TransferAnchorSet IntegrateEmbeddings( anchorset, reference, query, new.reduction.name = "integrated_dr", reductions = "pcaproject", dims.to.integrate = NULL, k.weight = 100, weight.reduction = NULL, reuse.weights.matrix = TRUE, sd.weight = 1, preserve.order = FALSE, verbose = TRUE, ... )
anchorset  An AnchorSet object 

...  Reserved for internal use 
new.reduction.name  Name for new integrated dimensional reduction. 
reductions  Name of reductions to be integrated. For a TransferAnchorSet, this should be the name of a reduction present in the anchorset object (for example, "pcaproject"). For an IntegrationAnchorSet, this should be a 
dims.to.integrate  Number of dimensions to return integrated values for 
k.weight  Number of neighbors to consider when weighting anchors 
weight.reduction  Dimension reduction to use when calculating anchor weights. This can be one of:

sd.weight  Controls the bandwidth of the Gaussian kernel for weighting 
sample.tree  Specify the order of integration. If NULL, will compute automatically. 
preserve.order  Do not reorder objects based on size for each pairwise integration. 
verbose  Print progress bars and output 
reference  Reference object used in anchorset construction 
query  Query object used in anchorset construction 
reuse.weights.matrix  Can be used in conjunction with the store.weights parameter in TransferData to reuse a precomputed weights matrix. 
When called on a TransferAnchorSet (from FindTransferAnchors), this will return the query object with the integrated embeddings stored in a new reduction. When called on an IntegrationAnchorSet (from IntegrateData), this will return a merged object with the integrated reduction stored.
The main steps of this procedure are identical to IntegrateData
with one key distinction. When computing the weights matrix, the distance calculations are performed in the full space of integrated embeddings when integrating more than two datasets, as opposed to a reduced PCA space which is the default behavior in IntegrateData
.