Perform dataset integration using a pre-computed 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, ... )
An AnchorSet object
Reserved for internal use
Name for new integrated dimensional reduction.
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
Number of dimensions to return integrated values for
Number of neighbors to consider when weighting anchors
Dimension reduction to use when calculating anchor weights. This can be one of:
Controls the bandwidth of the Gaussian kernel for weighting
Specify the order of integration. If NULL, will compute automatically.
Do not reorder objects based on size for each pairwise integration.
Print progress bars and output
Reference object used in anchorset construction
Query object used in anchorset construction
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