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
DEenrichRPlot.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mixscape.R
\name{DEenrichRPlot}
\alias{DEenrichRPlot}
\title{DE and EnrichR pathway visualization barplot}
\usage{
DEenrichRPlot(
object,
ident.1 = NULL,
ident.2 = NULL,
balanced = TRUE,
logfc.threshold = 0.25,
assay = NULL,
max.genes,
test.use = "wilcox",
p.val.cutoff = 0.05,
cols = NULL,
enrich.database = NULL,
num.pathway = 10,
return.gene.list = FALSE,
...
)
}
\arguments{
\item{object}{Name of object class Seurat.}
\item{ident.1}{Cell class identity 1.}
\item{ident.2}{Cell class identity 2.}
\item{balanced}{Option to display pathway enrichments for both negative and
positive DE genes.If false, only positive DE gene will be displayed.}
\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.}
\item{assay}{Assay to use in differential expression testing}
\item{max.genes}{Maximum number of genes to use as input to enrichR.}
\item{test.use}{Denotes which test to use. Available options are:
\itemize{
\item{"wilcox"} : Identifies differentially expressed genes between two
groups of cells using a Wilcoxon Rank Sum test (default); will use a fast
implementation by Presto if installed
\item{"wilcox_limma"} : Identifies differentially expressed genes between two
groups of cells using the limma implementation of the Wilcoxon Rank Sum test;
set this option to reproduce results from Seurat v4
\item{"bimod"} : Likelihood-ratio test for single cell gene expression,
(McDavid et al., Bioinformatics, 2013)
\item{"roc"} : Identifies 'markers' of gene expression using ROC analysis.
For each gene, evaluates (using AUC) a classifier built on that gene alone,
to classify between two groups of cells. An AUC value of 1 means that
expression values for this gene alone can perfectly classify the two
groupings (i.e. Each of the cells in cells.1 exhibit a higher level than
each of the cells in cells.2). An AUC value of 0 also means there is perfect
classification, but in the other direction. A value of 0.5 implies that
the gene has no predictive power to classify the two groups. Returns a
'predictive power' (abs(AUC-0.5) * 2) ranked matrix of putative differentially
expressed genes.
\item{"t"} : Identify differentially expressed genes between two groups of
cells using the Student's t-test.
\item{"negbinom"} : Identifies differentially expressed genes between two
groups of cells using a negative binomial generalized linear model.
Use only for UMI-based datasets
\item{"poisson"} : Identifies differentially expressed genes between two
groups of cells using a poisson generalized linear model.
Use only for UMI-based datasets
\item{"LR"} : Uses a logistic regression framework to determine differentially
expressed genes. Constructs a logistic regression model predicting group
membership based on each feature individually and compares this to a null
model with a likelihood ratio test.
\item{"MAST"} : Identifies differentially expressed genes between two groups
of cells using a hurdle model tailored to scRNA-seq data. Utilizes the MAST
package to run the DE testing.
\item{"DESeq2"} : Identifies differentially expressed genes between two groups
of cells based on a model using DESeq2 which uses a negative binomial
distribution (Love et al, Genome Biology, 2014).This test does not support
pre-filtering of genes based on average difference (or percent detection rate)
between cell groups. However, genes may be pre-filtered based on their
minimum detection rate (min.pct) across both cell groups. To use this method,
please install DESeq2, using the instructions at
https://bioconductor.org/packages/release/bioc/html/DESeq2.html
}}
\item{p.val.cutoff}{Cutoff to select DE genes.}
\item{cols}{A list of colors to use for barplots.}
\item{enrich.database}{Database to use from enrichR.}
\item{num.pathway}{Number of pathways to display in barplot.}
\item{return.gene.list}{Return list of DE genes}
\item{...}{Arguments passed to other methods and to specific DE methods}
}
\value{
Returns one (only enriched) or two (both enriched and depleted)
barplots with the top enriched/depleted GO terms from EnrichR.
}
\description{
DE and EnrichR pathway visualization barplot
}
\concept{mixscape}