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
- 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