FindAllMarkers: Gene expression markers for all identity classes
In atakanekiz/Seurat3.0: Tools for Single Cell Genomics

Description Usage Arguments Value Examples

Finds markers (differentially expressed genes) for each of the identity classes in a dataset

FindAllMarkers(object, assay = NULL, features = NULL,
  logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1,
  min.diff.pct = -Inf, node = NULL, verbose = TRUE,
  only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1,
  latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3,
  pseudocount.use = 1, return.thresh = 0.01, ...)

`object`	An object
`assay`	Assay to use in differential expression testing
`features`	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" : 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)) 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
`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
`node`	A node to find markers for and all its children; requires `BuildClusterTree` to have been run previously; replaces `FindAllMarkersNode`
`verbose`	Print a progress bar once expression testing begins
`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, used only when `test.use` is one of 'LR', 'negbinom', 'poisson', or 'MAST'
`min.cells.feature`	Minimum number of cells expressing the feature in at least one of the two groups, currently only used for poisson and negative binomial tests
`min.cells.group`	Minimum number of cells in one of the groups
`pseudocount.use`	Pseudocount to add to averaged expression values when calculating logFC. 1 by default.
`return.thresh`	Only return markers that have a p-value < return.thresh, or a power > return.thresh (if the test is ROC)
`...`	Arguments passed to other methods and to specific DE methods

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

# Find markers for all clusters
all.markers <- FindAllMarkers(object = pbmc_small)
head(x = all.markers)

# Pass a value to node as a replacement for FindAllMarkersNode
pbmc_small <- BuildClusterTree(object = pbmc_small)
all.markers <- FindAllMarkers(object = pbmc_small, node = 4)
head(x = all.markers)