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
RunSPCA.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/dimensional_reduction.R
\name{RunSPCA}
\alias{RunSPCA}
\alias{RunSPCA.default}
\alias{RunSPCA.Assay}
\alias{RunSPCA.Assay5}
\alias{RunSPCA.Seurat}
\title{Run Supervised Principal Component Analysis}
\usage{
RunSPCA(object, ...)
\method{RunSPCA}{default}(
object,
assay = NULL,
npcs = 50,
reduction.key = "SPC_",
graph = NULL,
verbose = FALSE,
seed.use = 42,
...
)
\method{RunSPCA}{Assay}(
object,
assay = NULL,
features = NULL,
npcs = 50,
reduction.key = "SPC_",
graph = NULL,
verbose = TRUE,
seed.use = 42,
...
)
\method{RunSPCA}{Assay5}(
object,
assay = NULL,
features = NULL,
npcs = 50,
reduction.key = "SPC_",
graph = NULL,
verbose = TRUE,
seed.use = 42,
layer = "scale.data",
...
)
\method{RunSPCA}{Seurat}(
object,
assay = NULL,
features = NULL,
npcs = 50,
reduction.name = "spca",
reduction.key = "SPC_",
graph = NULL,
verbose = TRUE,
seed.use = 42,
...
)
}
\arguments{
\item{object}{An object}
\item{...}{Arguments passed to other methods and IRLBA}
\item{assay}{Name of Assay SPCA is being run on}
\item{npcs}{Total Number of SPCs to compute and store (50 by default)}
\item{reduction.key}{dimensional reduction key, specifies the string before
the number for the dimension names. SPC by default}
\item{graph}{Graph used supervised by SPCA}
\item{verbose}{Print the top genes associated with high/low loadings for
the SPCs}
\item{seed.use}{Set a random seed. By default, sets the seed to 42. Setting
NULL will not set a seed.}
\item{features}{Features to compute SPCA on. If features=NULL, SPCA will be run
using the variable features for the Assay.}
\item{layer}{Layer to run SPCA on}
\item{reduction.name}{dimensional reduction name, spca by default}
}
\value{
Returns Seurat object with the SPCA calculation stored in the reductions slot
}
\description{
Run a supervised PCA (SPCA) dimensionality reduction supervised by a cell-cell kernel.
SPCA is used to capture a linear transformation which maximizes its dependency to
the given cell-cell kernel. We use SNN graph as the kernel to supervise the linear
matrix factorization.
}
\references{
Barshan E, Ghodsi A, Azimifar Z, Jahromi MZ.
Supervised principal component analysis: Visualization, classification and
regression on subspaces and submanifolds.
Pattern Recognition. 2011 Jul 1;44(7):1357-71. \url{https://www.sciencedirect.com/science/article/pii/S0031320310005819?casa_token=AZMFg5OtPnAAAAAA:_Udu7GJ7G2ed1-XSmr-3IGSISUwcHfMpNtCj-qacXH5SBC4nwzVid36GXI3r8XG8dK5WOQui};
}
\concept{dimensional_reduction}