DE_MAST_RE_seurat: MAST differential expression test with random effect for...

In CBMR-Single-Cell-Omics-Platform/SCOPfunctions: Single Cell Omics Platform Functions

Description

Uses Seurat::FindMarkers MAST test hurdle model with added random effects variables

Usage

DE_MAST_RE_seurat(
  object,
  random_effect.vars,
  ident.1 = NULL,
  ident.2 = NULL,
  cells.1 = NULL,
  cells.2 = NULL,
  group.by = NULL,
  logfc.threshold = 0.25,
  base = exp(1),
  assay = NULL,
  slot = "data",
  features = NULL,
  min.pct = 0.1,
  max.cells.per.ident = NULL,
  random.seed = 1,
  latent.vars = NULL,
  n_cores = NULL,
  verbose = TRUE,
  p.adjust.method = "fdr",
  ...
)

Arguments

object	Seurat >=3 object, with log-normalized data in the 'data' slot
random_effect.vars	Character vector of variables to add as random intersects in MAST model i.e. ~ ... + (1|random_effect.var1) + (1|random_effect.var2)
ident.1	Identity class to define markers for
ident.2	A second identity class for comparison. Leave as NULL to compare with all other cells
cells.1	Vector of cell names belonging to group 1. Alternative way to specify ident.1
cells.2	Vector of cell names belonging to group 2. Alternative way to specify ident.2
group.by	Regroup cells into a different identity class prior to performing differential expression
logfc.threshold	Only return results with a DE exceeding threshold
base	base for log when computing mean and for output, default exp(1). NB: Seurat has changed to log2 in V4.
assay	Assay to pull data from; defaults to default assay
slot	Slot to pull data from; defaults to "data" as MAST expects log-normalized data
features	Genes to test. Default is to use all genes
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
max.cells.per.ident	Downsample each identity class to a max number. Default is no downsampling. Not activated by default (set to Inf)
random.seed	Random seed to use for down sampling
latent.vars	Variables to test
n_cores	How many cores to use (temporarily sets options(mc.cores=n_cores))
verbose	whether to print stdout from MAST functions
p.adjust.method	passed to p.adjust. Note that n is all the genes in the object
...	Additional parameters (other than formula, sca, method, ebayes, strictConvergence) to pass to MAST::zlm

Value

data.frame with column names p_val, avg_log[base]FC, pct.1, pct.2, p_val_adj (identical format to Seurat::FindMarkers)

References

Zimmerman, K.D., Espeland, M.A. & Langefeld, C.D. . A practical solution to pseudoreplication bias in single-cell studies. . Nat Commun 12, 738 (2021). https://doi.org/10.1038/s41467-021-21038-1

. McDavid A, Finak G, Yajima M (2020). MAST: Model-based Analysis of Single Cell Transcriptomics. R package version 1.16.0, https://github.com/RGLab/MAST/.

. Stuart and Butler et al. Comprehensive Integration of Single-Cell Data. Cell (2019)

. Seurat 3 source at https://github.com/satijalab/seurat/blob/master/R/differential_expression.R

CBMR-Single-Cell-Omics-Platform/SCOPfunctions documentation built on May 29, 2021, 3:52 p.m.