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Seurat (version 3.1.4)

FindIntegrationAnchors: Find integration anchors

Description

Finds the integration anchors

Usage

FindIntegrationAnchors(
  object.list = NULL,
  assay = NULL,
  reference = NULL,
  anchor.features = 2000,
  scale = TRUE,
  normalization.method = c("LogNormalize", "SCT"),
  sct.clip.range = NULL,
  reduction = c("cca", "rpca"),
  l2.norm = TRUE,
  dims = 1:30,
  k.anchor = 5,
  k.filter = 200,
  k.score = 30,
  max.features = 200,
  nn.method = "rann",
  eps = 0,
  verbose = TRUE
)

Arguments

object.list

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

assay

A vector of assay names specifying which assay to use when constructing anchors. If NULL, the current default assay for each object is used.

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

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

sct.clip.range

Numeric of length two specifying the min and max values the Pearson residual will be clipped to

reduction

Dimensional reduction to perform when finding anchors. Can be one of:

  • cca: Canonical correlation analysis

  • rpca: Reciprocal PCA

l2.norm

Perform L2 normalization on the CCA cell embeddings after dimensional reduction

dims

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

k.anchor

How many neighbors (k) to use when picking anchors

k.filter

How many neighbors (k) to use when filtering anchors

k.score

How many neighbors (k) to use when scoring anchors

max.features

The maximum number of features to use when specifying the neighborhood search space in the anchor filtering

nn.method

Method for nearest neighbor finding. Options include: rann, annoy

eps

Error bound on the neighbor finding algorithm (from RANN)

verbose

Print progress bars and output

Value

Returns an AnchorSet object