IntegrateEmbeddings: Integrate low dimensional embeddings

In satijalab/seurat: Tools for Single Cell Genomics

Integrate low dimensional embeddings

Description

Perform dataset integration using a pre-computed Anchorset of specified low dimensional representations.

Usage

IntegrateEmbeddings(anchorset, ...)

## S3 method for class '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 class 'TransferAnchorSet'
IntegrateEmbeddings(
  anchorset,
  reference,
  query,
  query.assay = NULL,
  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,
  ...
)

Arguments

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 DimReduc object containing all cells present in the anchorset object.
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: A string, specifying the name of a dimension reduction present in all objects to be integrated A vector of strings, specifying the name of a dimension reduction to use for each object to be integrated A vector of DimReduc objects, specifying the object to use for each object in the integration NULL, in which case the full corrected space is used for computing anchor weights.
sd.weight	Controls the bandwidth of the Gaussian kernel for weighting
sample.tree	Specify the order of integration. Order of integration should be encoded in a matrix, where each row represents one of the pairwise integration steps. Negative numbers specify a dataset, positive numbers specify the integration results from a given row (the format of the merge matrix included in the hclust function output). For example: matrix(c(-2, 1, -3, -1), ncol = 2) gives: [,1] [,2] [1,] -2 -3 [2,] 1 -1 Which would cause dataset 2 and 3 to be integrated first, then the resulting object integrated with dataset 1. If NULL, the sample tree will be computed 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
query.assay	Name of the Assay to use from query
reuse.weights.matrix	Can be used in conjunction with the store.weights parameter in TransferData to reuse a precomputed weights matrix.

Details

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.

Value

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.

satijalab/seurat documentation built on May 11, 2024, 4:04 a.m.