scCODA differential abundance

Usage

RunscCODA(
  srt,
  group.by,
  split.by,
  sample.by,
  comparison = NULL,
  reference_cell_type = NULL,
  credible_effect_threshold = 0.95,
  n_mcmc_samples = 20000L,
  reuse_reverse_comparisons = TRUE,
  envname = "sccoda_env",
  conda = "auto",
  seed = 11,
  verbose = TRUE
)

Arguments

srt: A Seurat object.
group.by: Metadata column(s) used to color cells.
split.by: Metadata column that identifies the condition groups to compare. For sample-level methods, if split.by is omitted and sample.by is provided, sample.by is treated as the condition column and virtual samples are created within each condition.
sample.by: Metadata column that identifies biological samples. For "milo", "sccoda", and "propeller", when sample.by is omitted or identical to split.by, virtual samples are created within each split.by group for convenience.
comparison: Optional pairs of condition labels. Each pair is c(group1, group2). obs_log2FD is log2(group1 / group2), matching RunDEtest / Seurat ident.1 vs ident.2: positive values mean the cell type is more abundant in group1. If NULL, all pairwise comparisons are generated in both directions.
reference_cell_type: Optional reference cell type for scCODA.
credible_effect_threshold: Inclusion probability threshold for credible effects.
n_mcmc_samples: Number of MCMC samples requested in scCODA.
reuse_reverse_comparisons: Reuse each fitted pair for the reverse direction by negating effect estimates and swapping interval bounds. This avoids fitting the same two-group model twice.
envname: Name of the conda-compatible environment used by scCODA. Defaults to "sccoda_env" to keep the TensorFlow/scCODA stack isolated from the default Python environment.
conda: The path or command name of a conda-compatible executable.
seed: Random seed.
verbose: Whether to print the message. Default is TRUE.

Value

A method result bundle used internally by RunProportionTest.