This function finds markers for all splits at or below the specified node
FindAllMarkersNode(object, node = NULL, genes.use = NULL,
thresh.use = 0.25, test.use = "bimod", min.pct = 0.1,
min.diff.pct = 0.05, print.bar = TRUE, only.pos = FALSE,
max.cells.per.ident = Inf, return.thresh = 0.01, do.print = FALSE,
random.seed = 1)Seurat object. Must have object@cluster.tree slot filled. Use BuildClusterTree() if not.
Node from which to start identifying split markers, default is top node.
Genes to test. Default is to use variable genes (object@var.genes)
Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells.
Denotes which test to use. Seurat currently implements "bimod" (likelihood-ratio test for single cell gene expression, McDavid et al., Bioinformatics, 2011, default), "roc" (standard AUC classifier), "t" (Students t-test), and "tobit" (Tobit-test for differential gene expression, as in Trapnell et al., Nature Biotech, 2014), 'poisson', and 'negbinom'. The latter two options should only be used on UMI datasets, and assume an underlying poisson or negative-binomial distribution.
- 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 expression
Print a progress bar once expression testing begins (uses pbapply to do this)
Only return positive markers (FALSE by default)
Down sample each identity class to a max number. Default is no downsampling.
Only return markers that have a p-value < return.thresh, or a power > return.thresh (if the test is ROC)
Random seed for downsampling
Returns a dataframe with a ranked list of putative markers for each node and associated statistics