https://github.com/satijalab/seurat
Tip revision: ff03fdf21f1b8fea9ee247d0fd83df5811507027 authored by AustinHartman on 05 December 2022, 22:48:27 UTC
Merge branch 'master' into release/4.3.0
Merge branch 'master' into release/4.3.0
Tip revision: ff03fdf
PrepLDA.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mixscape.R
\name{PrepLDA}
\alias{PrepLDA}
\title{Function to prepare data for Linear Discriminant Analysis.}
\usage{
PrepLDA(
object,
de.assay = "RNA",
pc.assay = "PRTB",
labels = "gene",
nt.label = "NT",
npcs = 10,
verbose = TRUE,
logfc.threshold = 0.25
)
}
\arguments{
\item{object}{An object of class Seurat.}
\item{de.assay}{Assay to use for selection of DE genes.}
\item{pc.assay}{Assay to use for running Principle components analysis.}
\item{labels}{Meta data column with target gene class labels.}
\item{nt.label}{Name of non-targeting cell class.}
\item{npcs}{Number of principle components to use.}
\item{verbose}{Print progress bar.}
\item{logfc.threshold}{Limit testing to genes which show, on average, at least
X-fold difference (log-scale) between the two groups of cells. Default is 0.25
Increasing logfc.threshold speeds up the function, but can miss weaker signals.}
}
\value{
Returns a list of the first 10 PCs from each projection.
}
\description{
This function performs unsupervised PCA on each mixscape class separately and projects each subspace onto all
cells in the data.
}
\concept{mixscape}