FindMarkers: Gene expression markers of identity classes

Description

Finds markers (differentially expressed genes) for identity classes

Usage

FindMarkers(object, ident.1, ident.2 = NULL, genes.use = NULL,
  logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1,
  min.diff.pct = -Inf, print.bar = TRUE, only.pos = FALSE,
  max.cells.per.ident = Inf, random.seed = 1, latent.vars = "nUMI",
  min.cells = 3, pseudocount.use = 1, assay.type = "RNA", ...)

Arguments

object

Seurat object

ident.1

Identity class to define markers for

ident.2

A second identity class for comparison. If NULL (default) - use all other cells for comparison.

genes.use

Genes to test. Default is to use all genes

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.

test.use

Denotes which test to use. Available options are:

"wilcox" : Wilcoxon rank sum test (default)
"bimod" : Likelihood-ratio test for single cell gene expression, (McDavid et al., Bioinformatics, 2013)
"roc" : Standard AUC classifier
"t" : Student's t-test
"tobit" : Tobit-test for differential gene expression (Trapnell et al., Nature Biotech, 2014)
"poisson" : Likelihood ratio test assuming an underlying poisson distribution. Use only for UMI-based datasets
"negbinom" : Likelihood ratio test assuming an underlying negative binomial distribution. Use only for UMI-based datasets
"MAST : GLM-framework that treates cellular detection rate as a covariate (Finak et al, Genome Biology, 2015)
"DESeq2 : DE based on a model using the negative binomial distribution (Love et al, Genome Biology, 2014)

min.pct

only test genes that are detected in a minimum fraction of min.pct cells in either of the two populations. Meant to speed up the function by not testing genes that are very infrequently expressed. Default is 0.1

min.diff.pct

only test genes that show a minimum difference in the fraction of detection between the two groups. Set to -Inf by default

print.bar

Print a progress bar once expression testing begins (uses pbapply to do this)

only.pos

Only return positive markers (FALSE by default)

max.cells.per.ident

Down sample each identity class to a max number. Default is no downsampling. Not activated by default (set to Inf)

random.seed

Random seed for downsampling

latent.vars

Variables to test

min.cells

Minimum number of cells expressing the gene in at least one of the two groups

pseudocount.use

Pseudocount to add to averaged expression values when calculating logFC. 1 by default.

assay.type

Type of assay to fetch data for (default is RNA)

…

Additional parameters to pass to specific DE functions

Value

Matrix containing a ranked list of putative markers, and associated statistics (p-values, ROC score, etc.)

Details

p-value adjustment is performed using bonferroni correction based on the total number of genes in the dataset. Other correction methods are not recommended, as Seurat pre-filters genes using the arguments above, reducing the number of tests performed. Lastly, as Aaron Lun has pointed out, p-values should be interpreted cautiously, as the genes used for clustering are the same genes tested for differential expression.

Examples

Run this code

# NOT RUN {
markers <- FindMarkers(object = pbmc_small, ident.1 = 3)
head(markers)

# }