DE and EnrichR pathway visualization barplot
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,
...
)Returns one (only enriched) or two (both enriched and depleted) barplots with the top enriched/depleted GO terms from EnrichR.
Name of object class Seurat.
Cell class identity 1.
Cell class identity 2.
Option to display pathway enrichments for both negative and positive DE genes.If false, only positive DE gene will be displayed.
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 to use in differential expression testing
Maximum number of genes to use as input to enrichR.
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)
"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
Cutoff to select DE genes.
A list of colors to use for barplots.
Database to use from enrichR.
Number of pathways to display in barplot.
Return list of DE genes
Arguments passed to other methods and to specific DE methods