- object
A Seurat
object
- assay
Name of Assay
in the Seurat
object
- layers
Names of layers in assay
- orig
A dimensional reduction to correct
- new.reduction
Name of new integrated dimensional reduction
- reference
A reference Seurat
object
- features
A vector of features to use for integration
- normalization.method
Name of normalization method used: LogNormalize
or SCT
- dims
Dimensions of dimensional reduction to use for integration
- k.filter
Number of anchors to filter
- scale.layer
Name of scaled layer in Assay
- 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
- ...
Arguments passed on to FindIntegrationAnchors