object |
Name of object class Seurat.
|
ident.1 |
Cell class identity 1.
|
ident.2 |
Cell class identity 2.
|
balanced |
Option to display pathway enrichments for both negative and
positive DE genes.If false, only positive DE gene will be displayed.
|
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.
|
assay |
Assay to use in differential expression testing
|
max.genes |
Maximum number of genes to use as input to enrichR.
|
test.use |
Denotes which test to use. Available options are:
"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
"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
"bimod" : Likelihood-ratio test for single cell gene expression,
(McDavid et al., Bioinformatics, 2013)
"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.
"t" : Identify differentially expressed genes between two groups of
cells using the Student's t-test.
"negbinom" : Identifies differentially expressed genes between two
groups of cells using a negative binomial generalized linear model.
Use only for UMI-based datasets
"poisson" : Identifies differentially expressed genes between two
groups of cells using a poisson generalized linear model.
Use only for UMI-based datasets
"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.
"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.
"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
|
p.val.cutoff |
Cutoff to select DE genes.
|
cols |
A list of colors to use for barplots.
|
enrich.database |
Database to use from enrichR.
|
num.pathway |
Number of pathways to display in barplot.
|
return.gene.list |
Return list of DE genes
|
... |
Arguments passed to other methods and to specific DE methods
|
Returns one (only enriched) or two (both enriched and depleted)
barplots with the top enriched/depleted GO terms from EnrichR.