Trajectory Differential Expression Test

Perform trajectory-based differential expression analysis using negative binomial regression. This function tests for genes that change expression along a continuous trajectory variable.

Usage

TrajDETest(object, ...)

# Default S3 method
TrajDETest(
  object,
  traj.var = NULL,
  latent.vars = NULL,
  features = NULL,
  fc.results = NULL,
  verbose = TRUE,
  ...
)

# S3 method for class 'Assay'
TrajDETest(
  object,
  layer = "counts",
  traj.var = NULL,
  latent.vars = NULL,
  samples = NULL,
  features = NULL,
  logfc.threshold = 0,
  prob.break.point = c(1/3, 2/3),
  min.pct = 0.1,
  min.count = 10,
  pseudocount.use = 1,
  verbose = TRUE,
  ...
)

# S3 method for class 'StdAssay'
TrajDETest(
  object,
  layer = "counts",
  traj.var = NULL,
  latent.vars = NULL,
  samples = NULL,
  features = NULL,
  logfc.threshold = 0,
  prob.break.point = c(1/3, 2/3),
  min.pct = 0.1,
  min.count = 10,
  pseudocount.use = 1,
  verbose = TRUE,
  ...
)

# S3 method for class 'Seurat'
TrajDETest(
  object,
  assay = NULL,
  layer = "counts",
  traj.var = NULL,
  latent.vars = NULL,
  samples = NULL,
  features = NULL,
  logfc.threshold = 0,
  prob.break.point = c(1/3, 2/3),
  min.pct = 0.1,
  min.count = 10,
  pseudocount.use = 1,
  complete.resutls = FALSE,
  verbose = TRUE,
  ...
)

Arguments

object: Expression data matrix, Assay, StdAssay, or Seurat object
...: additional parameters to be passed to glmGamPoi::glm_gp or filterByExpr
traj.var: a data frame containing the trajectory variable of the samples to be tested against
latent.vars: a data frame containing the latent variables (e.g., covariates that might affect the gene expression) to include in regression.
features: Genes to test. Default is to use all genes (after QC)
fc.results: fc.results calculated by FoldChange(); if not null QC will be performed based on this
verbose: Print a progress bar once expression testing begins
layer: the data layer to be used for trajectory DE test. Currently only 'counts' is supported.
samples: the cells/samples to be included in the DE test
logfc.threshold: Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the top and bottom groups of samples (see pro.break.point for details). Default is 0 (i.e., no filtering).
prob.break.point: a numeric vector of probability break points with values in (0, 1). It will be used to calculate 2 quantiles along the trajectory and to calculate the logfc.
min.pct: only test genes that are detected in a minimum fraction of min.pct cells in either of the two populations.
min.count: Minimum count threshold for gene filtering
pseudocount.use: Pseudocount to add to averaged expression values when calculating logFC. 1 by default.
assay: the assay to be used (for Seurat objects)
complete.resutls: Whether to return complete results or summary

Value

A data frame with differential expression results

Examples

if (FALSE) { # \dontrun{
# Run trajectory DE test on a Seurat object
de_results <- TrajDETest(seurat_obj, traj.var = "pseudotime")
} # }