RPCAIntegration: Seurat-RPCA Integration

In satijalab/seurat: Tools for Single Cell Genomics

Seurat-RPCA Integration

Description

Seurat-RPCA Integration

Usage

RPCAIntegration(
  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,
  verbose = TRUE,
  ...
)

Arguments

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

Examples

## Not run: 
# 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 run integration
obj <- IntegrateLayers(object = obj, method = RPCAIntegration,
  orig.reduction = "pca", new.reduction = 'integrated.rpca',
  verbose = FALSE)

# Reference-based Integration
# Here, we use the first layer as a reference for integraion
# Thus, we only identify anchors between the reference and the rest of the datasets,
# saving computational resources
obj <- IntegrateLayers(object = obj, method = RPCAIntegration,
  orig.reduction = "pca", new.reduction = 'integrated.rpca',
  reference = 1, verbose = FALSE)

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

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

## End(Not run)

satijalab/seurat documentation built on May 11, 2024, 4:04 a.m.