Seurat-CCA Integration

Usage

CCAIntegration(
  object = NULL,
  assay = NULL,
  layers = NULL,
  orig = NULL,
  new.reduction = "integrated.dr",
  reference = NULL,
  features = NULL,
  normalization.method = c("LogNormalize", "SCT"),
  dims = 1:30,
  k.filter = NA,
  scale.layer = "scale.data",
  dims.to.integrate = NULL,
  k.weight = 100,
  weight.reduction = NULL,
  sd.weight = 1,
  sample.tree = NULL,
  preserve.order = FALSE,
  svd.method = NULL,
  verbose = TRUE,
  ...
)

Arguments

object

A Seurat object

assay

Name of Assay in the Seurat object

layers

Names of layers in assay

orig

A DimReduc 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.

svd.method

SVD backend to use when reduction = "cca". If NULL, defaults to "irlba" except for pairs of dense CCA input matrices whose cell-by-cell cross-product matrix has at least 1,000,000 entries, in which case it defaults to "rspectra". "irlba" uses an explicit cross-product CCA path; "rspectra" uses an implicit operator to avoid forming the cross-product matrix.

verbose

Print progress

...

Arguments passed on to FindIntegrationAnchors

Examples

if (FALSE) { # \dontrun{
# Preprocessing
obj <- SeuratData::LoadData("pbmcsca")
obj[["RNA"]] <- split(obj[["RNA"]], f = obj$Method)
obj <- NormalizeData(obj)
obj <- FindVariableFeatures(obj)
obj <- ScaleData(obj)
obj <- RunPCA(obj)

# After preprocessing, we integrate layers.
obj <- IntegrateLayers(object = obj, method = CCAIntegration,
  orig.reduction = "pca", new.reduction = "integrated.cca",
  verbose = FALSE)

# Modifying parameters
# We can also specify parameters such as `k.anchor` to increase the strength of integration
obj <- IntegrateLayers(object = obj, method = CCAIntegration,
  orig.reduction = "pca", new.reduction = "integrated.cca",
  k.anchor = 20, verbose = FALSE)

# Integrating SCTransformed data
obj <- SCTransform(object = obj)
obj <- IntegrateLayers(object = obj, method = CCAIntegration,
  orig.reduction = "pca", new.reduction = "integrated.cca",
  assay = "SCT", verbose = FALSE)
} # }