Perform integration on the joint PCA cell embeddings.

Source: R/integration.R

This is a convenience wrapper function around the following three functions that are often run together when perform integration. #' FindIntegrationAnchors, RunPCA, IntegrateEmbeddings.

FastRPCAIntegration(
  object.list,
  reference = NULL,
  anchor.features = 2000,
  k.anchor = 20,
  dims = 1:30,
  scale = TRUE,
  normalization.method = c("LogNormalize", "SCT"),
  new.reduction.name = "integrated_dr",
  npcs = 50,
  findintegrationanchors.args = list(),
  verbose = TRUE
)

Arguments

object.list

A list of Seurat objects between which to find anchors for downstream integration.

reference

A vector specifying the object/s to be used as a reference during integration. If NULL (default), all pairwise anchors are found (no reference/s). If not NULL, the corresponding objects in object.list will be used as references. When using a set of specified references, anchors are first found between each query and each reference. The references are then integrated through pairwise integration. Each query is then mapped to the integrated reference.

anchor.features

Can be either:

  • A numeric value. This will call SelectIntegrationFeatures to select the provided number of features to be used in anchor finding

  • A vector of features to be used as input to the anchor finding process

k.anchor

How many neighbors (k) to use when picking anchors

dims

Which dimensions to use from the CCA to specify the neighbor search space

scale

Whether or not to scale the features provided. Only set to FALSE if you have previously scaled the features you want to use for each object in the object.list

normalization.method

Name of normalization method used: LogNormalize or SCT

new.reduction.name

Name of integrated dimensional reduction

npcs

Total Number of PCs to compute and store (50 by default)

findintegrationanchors.args

A named list of additional arguments to FindIntegrationAnchors

verbose

Print messages and progress

Value

Returns a Seurat object with integrated dimensional reduction