- anchorset
An AnchorSet
object generated by
FindIntegrationAnchors
- new.assay.name
Name for the new assay containing the integrated data
- normalization.method
Name of normalization method used: LogNormalize
or SCT
- features
Vector of features to use when computing the PCA to determine
the weights. Only set if you want a different set from those used in the
anchor finding process
- features.to.integrate
Vector of features to integrate. By default,
will use the features used in anchor finding.
- dims
Number of dimensions to use in the anchor weighting procedure
- 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 a new PCA will be calculated and used to
calculate anchor weights
Note that, if specified, the requested dimension reduction will only be used
for calculating anchor weights in the first merge between reference and
query, as the merged object will subsequently contain more cells than was in
query, and weights will need to be calculated for all cells in the object.
- 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.
- eps
Error bound on the neighbor finding algorithm (from
RANN
)
- verbose
Print progress bars and output