IntegrateData.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/integration.R
\name{IntegrateData}
\alias{IntegrateData}
\title{Integrate data}
\usage{
IntegrateData(anchorset, 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{
\item{anchorset}{Results from FindIntegrationAnchors}

\item{new.assay.name}{Name for the new assay containing the integrated data}

\item{normalization.method}{Name of normalization method used: LogNormalize
or SCT}

\item{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}

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

\item{dims}{Number of PCs to use in the weighting procedure}

\item{k.weight}{Number of neighbors to consider when weighting}

\item{weight.reduction}{Dimension reduction to use when calculating anchor weights.
This can be either:
\itemize{
   \item{A string, specifying the name of a dimension reduction present in all objects to be integrated}
   \item{A vector of strings, specifying the name of a dimension reduction to use for each object to be integrated}
   \item{A vector of Dimreduc objects, specifying the object to use for each object in the integration}
   \item{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.}

\item{sd.weight}{Controls the bandwidth of the Gaussian kernel for weighting}

\item{sample.tree}{Specify the order of integration. If NULL, will compute automatically.}

\item{preserve.order}{Do not reorder objects based on size for each pairwise integration.}

\item{do.cpp}{Run cpp code where applicable}

\item{eps}{Error bound on the neighbor finding algorithm (from \code{\link{RANN}})}

\item{verbose}{Print progress bars and output}
}
\value{
Returns a Seurat object with a new integrated Assay
}
\description{
Perform dataset integration using a pre-computed anchorset
}