find_all_markers: Gene expression markers for all identity classes

Description Usage Arguments Value

Description

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

Usage

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find_all_markers(object, thresh.test = 1, test.use = "bimod",
  return.thresh = 0.01, do.print = FALSE, min.pct = 0, print.bar = TRUE,
  only.pos = FALSE)

Arguments

object

Seurat object

thresh.test

Limit testing to genes which show, on average, at least X-fold difference (log-scale) between cells in an identity class, and all other cells.

test.use

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)

return.thresh

Only return markers that have a p-value < return.thresh, or a power > return.thresh (if the test is ROC)

do.print

FALSE by default. If TRUE, outputs updates on progress.

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 expression

print.bar

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

only.pos

Only return positive markers (FALSE by default)

Value

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


paodan/studySeu documentation built on May 23, 2019, 3:06 p.m.