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,
eps = 0,
verbose = TRUE
)

## Arguments

anchorset An AnchorSet object generated by 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 dimensions to use in the anchor weighting procedure Number of neighbors to consider when weighting anchors 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. Controls the bandwidth of the Gaussian kernel for weighting 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. Do not reorder objects based on size for each pairwise integration. Error bound on the neighbor finding algorithm (from RANN) Print progress bars and output

## Value

Returns a Seurat object with a new integrated Assay. If normalization.method = "LogNormalize", the integrated data is returned to the data slot and can be treated as log-normalized, corrected data. If normalization.method = "SCT", the integrated data is returned to the scale.data slot and can be treated as centered, corrected Pearson residuals.

## Details

The main steps of this procedure are outlined below. For a more detailed description of the methodology, please see Stuart, Butler, et al Cell 2019. doi: 10.1016/j.cell.2019.05.031 ; doi: 10.1101/460147

For pairwise integration:

• Construct a weights matrix that defines the association between each query cell and each anchor. These weights are computed as 1 - the distance between the query cell and the anchor divided by the distance of the query cell to the k.weightth anchor multiplied by the anchor score computed in FindIntegrationAnchors. We then apply a Gaussian kernel width a bandwidth defined by sd.weight and normalize across all k.weight anchors.

• Compute the anchor integration matrix as the difference between the two expression matrices for every pair of anchor cells

• Compute the transformation matrix as the product of the integration matrix and the weights matrix.

• Subtract the transformation matrix from the original expression matrix.

For multiple dataset integration, we perform iterative pairwise integration. To determine the order of integration (if not specified via sample.tree), we

• Define a distance between datasets as the total number of cells in the smaller dataset divided by the total number of anchors between the two datasets.

• Compute all pairwise distances between datasets

• Cluster this distance matrix to determine a guide tree

## References

Stuart T, Butler A, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177:1888-1902 doi: 10.1016/j.cell.2019.05.031

## Examples

if (FALSE) {
# to install the SeuratData package see https://github.com/satijalab/seurat-data
library(SeuratData)
data("panc8")

# panc8 is a merged Seurat object containing 8 separate pancreas datasets
# split the object by dataset
pancreas.list <- SplitObject(panc8, split.by = "tech")

# perform standard preprocessing on each object
for (i in 1:length(pancreas.list)) {
pancreas.list[[i]] <- NormalizeData(pancreas.list[[i]], verbose = FALSE)
pancreas.list[[i]] <- FindVariableFeatures(
pancreas.list[[i]], selection.method = "vst",
nfeatures = 2000, verbose = FALSE
)
}

# find anchors
anchors <- FindIntegrationAnchors(object.list = pancreas.list)

# integrate data
integrated <- IntegrateData(anchorset = anchors)
}