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", slot = "data", 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, mean.fxn = NULL, fc.name = NULL, base = 2, return.thresh = 0.01, ... )
Assay to use in differential expression testing
Genes to test. Default is to use all genes
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.
Denotes which test to use. Available options are:
Slot to pull data from; note that if
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
only test genes that show a minimum difference in the fraction of detection between the two groups. Set to -Inf by default
A node to find markers for and all its children; requires
Print a progress bar once expression testing begins
Only return positive markers (FALSE by default)
Down sample each identity class to a max number. Default is no downsampling. Not activated by default (set to Inf)
Random seed for downsampling
Variables to test, used only when
Minimum number of cells expressing the feature in at least one of the two groups, currently only used for poisson and negative binomial tests
Minimum number of cells in one of the groups
Pseudocount to add to averaged expression values when calculating logFC. 1 by default.
Function to use for fold change or average difference calculation. If NULL, the appropriate function will be chose according to the slot used
Name of the fold change, average difference, or custom function column in the output data.frame. If NULL, the fold change column will be named according to the logarithm base (eg, "avg_log2FC"), or if using the scale.data slot "avg_diff".
The base with respect to which logarithms are computed.
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.)
data("pbmc_small") # Find markers for all clusters all.markers <- FindAllMarkers(object = pbmc_small)#>#>#>head(x = all.markers)#> p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene #> HLA-DPB1 9.572778e-13 -5.820829 0.083 0.909 2.201739e-10 0 HLA-DPB1 #> HLA-DRB1 7.673127e-12 -5.425935 0.083 0.864 1.764819e-09 0 HLA-DRB1 #> HLA-DPA1 3.673172e-11 -4.374436 0.111 0.864 8.448296e-09 0 HLA-DPA1 #> HLA-DRA 1.209114e-10 -4.263126 0.417 0.909 2.780962e-08 0 HLA-DRA #> HLA-DRB5 9.547049e-10 -4.356374 0.056 0.773 2.195821e-07 0 HLA-DRB5 #> HLA-DQB1 3.035198e-08 -4.329174 0.028 0.659 6.980956e-06 0 HLA-DQB1