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Seurat (version 3.1.1)

MapQuery: Map queries to reference

Description

Map query objects onto assembled reference dataset

Usage

MapQuery(anchorset, reference, new.assay.name = "integrated",
  normalization.method = c("LogNormalize", "SCT"), features = NULL,
  features.to.integrate = NULL, dims = 1:30, k.weight = 100,
  weight.reduction = NULL, sd.weight = 1, sample.tree = NULL,
  preserve.order = FALSE, do.cpp = TRUE, eps = 0, verbose = TRUE)

Arguments

anchorset

Anchorset found by FindIntegrationAnchors

reference

Pre-integrated reference dataset to map query datasets to

new.assay.name

Name for the new assay containing the integrated data

normalization.method

Name of normalization method used: LogNormalize or SCT

features

Vector of features to use when computing the PCA to determine the weights. Only set if you want a different set from those used in the anchor finding process

features.to.integrate

Vector of features to integrate. By default, will use the features used in anchor finding.

dims

Number of PCs to use in the weighting procedure

k.weight

Number of neighbors to consider when weighting

weight.reduction

Dimension reduction to use when calculating anchor weights. This can be either:

  • 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

  • NULL, in which case a new PCA will be calculated and used to calculate anchor weights

Note that, if specified, the requested dimension reduction will only be used for calculating anchor weights in the first merge between reference and query, as the merged object will subsequently contain more cells than was in query, and weights will need to be calculated for all cells in the object.

sd.weight

Controls the bandwidth of the Gaussian kernel for weighting

sample.tree

Specify the order of integration. If NULL, will compute automatically.

preserve.order

Do not reorder objects based on size for each pairwise integration.

do.cpp

Run cpp code where applicable

eps

Error bound on the neighbor finding algorithm (from RANN)

verbose

Print progress bars and output

Value

Returns an integrated matrix