JointPCAIntegration: Seurat-Joint PCA Integration
In satijalab/seurat: Tools for Single Cell Genomics

JointPCAIntegration

R Documentation

Seurat-Joint PCA Integration

Description

Seurat-Joint PCA Integration

Usage

JointPCAIntegration(
  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.anchor = 20,
  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.anchor`	How many neighbors (k) to use when picking anchors
`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`