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
ProjectCellEmbeddings.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/integration.R
\name{ProjectCellEmbeddings}
\alias{ProjectCellEmbeddings}
\alias{ProjectCellEmbeddings.Seurat}
\alias{ProjectCellEmbeddings.Assay}
\alias{ProjectCellEmbeddings.SCTAssay}
\alias{ProjectCellEmbeddings.StdAssay}
\alias{ProjectCellEmbeddings.default}
\alias{ProjectCellEmbeddings.IterableMatrix}
\title{Project query data to the reference dimensional reduction}
\usage{
ProjectCellEmbeddings(query, ...)
\method{ProjectCellEmbeddings}{Seurat}(
query,
reference,
query.assay = NULL,
reference.assay = NULL,
reduction = "pca",
dims = 1:50,
normalization.method = c("LogNormalize", "SCT"),
scale = TRUE,
verbose = TRUE,
nCount_UMI = NULL,
feature.mean = NULL,
feature.sd = NULL,
...
)
\method{ProjectCellEmbeddings}{Assay}(
query,
reference,
reference.assay = NULL,
reduction = "pca",
dims = 1:50,
scale = TRUE,
normalization.method = NULL,
verbose = TRUE,
nCount_UMI = NULL,
feature.mean = NULL,
feature.sd = NULL,
...
)
\method{ProjectCellEmbeddings}{SCTAssay}(
query,
reference,
reference.assay = NULL,
reduction = "pca",
dims = 1:50,
scale = TRUE,
normalization.method = NULL,
verbose = TRUE,
nCount_UMI = NULL,
feature.mean = NULL,
feature.sd = NULL,
...
)
\method{ProjectCellEmbeddings}{StdAssay}(
query,
reference,
reference.assay = NULL,
reduction = "pca",
dims = 1:50,
scale = TRUE,
normalization.method = NULL,
verbose = TRUE,
nCount_UMI = NULL,
feature.mean = NULL,
feature.sd = NULL,
...
)
\method{ProjectCellEmbeddings}{default}(
query,
reference,
reference.assay = NULL,
reduction = "pca",
dims = 1:50,
scale = TRUE,
normalization.method = NULL,
verbose = TRUE,
features = NULL,
nCount_UMI = NULL,
feature.mean = NULL,
feature.sd = NULL,
...
)
\method{ProjectCellEmbeddings}{IterableMatrix}(
query,
reference,
reference.assay = NULL,
reduction = "pca",
dims = 1:50,
scale = TRUE,
normalization.method = NULL,
verbose = TRUE,
features = features,
nCount_UMI = NULL,
feature.mean = NULL,
feature.sd = NULL,
block.size = 10000,
...
)
}
\arguments{
\item{query}{An object for query cells}
\item{reference}{An object for reference cells}
\item{query.assay}{Assay name for query object}
\item{reference.assay}{Assay name for reference object}
\item{reduction}{Name of dimensional reduction from reference object}
\item{dims}{Dimensions used for reference dimensional reduction}
\item{scale}{Determine if scale query data based on reference data variance}
\item{verbose}{Print progress}
\item{feature.mean}{Mean of features in reference}
\item{feature.sd}{Standard variance of features in reference}
}
\value{
A matrix with projected cell embeddings
}
\description{
Project query data to the reference dimensional reduction
}
\keyword{internal}