swh:1:snp:0da231f3ffdb3226650880f1b61d5d5cdcbd749b
Tip revision: 656fc8b562d53e5d0cedda9e09d9dda81e8c00e9 authored by David Collins on 29 February 2024, 13:44:33 UTC
Merge pull request #8550 from satijalab/develop
Merge pull request #8550 from satijalab/develop
Tip revision: 656fc8b
JointPCAIntegration.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/integration5.R
\name{JointPCAIntegration}
\alias{JointPCAIntegration}
\title{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{
\item{object}{A \code{Seurat} object}
\item{assay}{Name of \code{Assay} in the \code{Seurat} object}
\item{layers}{Names of layers in \code{assay}}
\item{orig}{A \link[SeuratObject:DimReduc]{dimensional reduction} to correct}
\item{new.reduction}{Name of new integrated dimensional reduction}
\item{reference}{A reference \code{Seurat} object}
\item{features}{A vector of features to use for integration}
\item{normalization.method}{Name of normalization method used: LogNormalize
or SCT}
\item{dims}{Dimensions of dimensional reduction to use for integration}
\item{k.anchor}{How many neighbors (k) to use when picking anchors}
\item{scale.layer}{Name of scaled layer in \code{Assay}}
\item{dims.to.integrate}{Number of dimensions to return integrated values for}
\item{k.weight}{Number of neighbors to consider when weighting anchors}
\item{weight.reduction}{Dimension reduction to use when calculating anchor
weights. This can be one of:
\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 \code{\link{DimReduc}} objects, specifying the object to
use for each object in the integration}
\item{NULL, in which case the full corrected space is used for computing
anchor weights.}
}}
\item{sd.weight}{Controls the bandwidth of the Gaussian kernel for weighting}
\item{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 \code{\link{hclust}} function output). For example:
\code{matrix(c(-2, 1, -3, -1), ncol = 2)} gives:
\if{html}{\out{<div class="sourceCode">}}\preformatted{ [,1] [,2]
[1,] -2 -3
[2,] 1 -1
}\if{html}{\out{</div>}}
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.}
\item{preserve.order}{Do not reorder objects based on size for each pairwise
integration.}
\item{verbose}{Print progress}
\item{...}{Arguments passed on to \code{FindIntegrationAnchors}}
}
\description{
Seurat-Joint PCA Integration
}