branchTest: Test for branch-dependent expression

In monocle: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq

Description

Testing for branch-dependent expression with BEAM() first involves constructing a CellDataSet that assigns each cell to a branch, and then performing a likelihood ratio test to see if the branch assignments significantly improves the fit over a null model that does not split the cells. branchTest() implements these two steps.

Usage

branchTest(cds, fullModelFormulaStr = "~sm.ns(Pseudotime, df = 3)*Branch",
  reducedModelFormulaStr = "~sm.ns(Pseudotime, df = 3)",
  branch_states = NULL, branch_point = 1, relative_expr = TRUE,
  cores = 1, branch_labels = NULL, verbose = FALSE, ...)

Arguments

cds	a CellDataSet object upon which to perform this operation
fullModelFormulaStr	a formula string specifying the full model in differential expression tests (i.e. likelihood ratio tests) for each gene/feature.
reducedModelFormulaStr	a formula string specifying the reduced model in differential expression tests (i.e. likelihood ratio tests) for each gene/feature.
branch_states	states corresponding to two branches
branch_point	The ID of the branch point to analyze. Can only be used when reduceDimension is called with method = "DDRTree".
relative_expr	a logic flag to determine whether or not the relative gene expression should be used
cores	the number of cores to be used while testing each gene for differential expression
branch_labels	the name for each branch, for example, AT1 or AT2
verbose	Whether to show VGAM errors and warnings. Only valid for cores = 1.
...	Additional arguments passed to differentialGeneTest

Value

a data frame containing the p values and q-values from the likelihood ratio tests on the parallel arrays of models.

monocle documentation built on Nov. 8, 2020, 5:06 p.m.