Perform dataset integration using a pre-computed anchorset
IntegrateData(anchorset, new.assay.name = "integrated",
normalization.method = c("LogNormalize", "SCT"), features = NULL,
features.to.integrate = NULL, dims = 1:30, k.weight = 100,
weight.reduction = NULL, sd.weight = 1, sample.tree = NULL,
preserve.order = FALSE, do.cpp = TRUE, eps = 0, verbose = TRUE)Results from FindIntegrationAnchors
Name for the new assay containing the integrated data
Name of normalization method used: LogNormalize or SCT
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
Vector of features to integrate. By default, will use the features used in anchor finding.
Number of PCs to use in the weighting procedure
Number of neighbors to consider when weighting
Dimension reduction to use when calculating anchor weights. This can be either:
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.
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.
Run cpp code where applicable
Error bound on the neighbor finding algorithm (from RANN)
Print progress bars and output
Returns a Seurat object with a new integrated Assay