Design-informed size factor estimation
Usage
disize(
design_formula,
counts,
metadata,
batch_name,
obs_name = "obs_id",
n_feats = 10000L,
n_subset = 50L,
n_iters = "auto",
n_threads = 1L,
init_alpha = 1e-06,
verbose = 3L
)Arguments
- design_formula
The formula describing the experimental design.
- counts
A (observation x feature) count matrix.
- metadata
A dataframe containing observation-level metadata.
- batch_name
The identifier for the batch column in 'metadata'.
- obs_name
The identifier for the observation column in 'metadata', defaults to "obs_id".
- n_feats
The number of features used during estimation, defaults to all features (default caps to 10000).
- n_subset
The number of observations per experimental unit used during estimation, defaults to 50 (useful for scRNA-seq experiments).
- n_iters
The number of iterations used for estimation.
- n_threads
The number of threads to use for parallel processing.
- init_alpha
The initial step-size for the optimizer, lower values can sometimes make it easier to estimate size factors for more complex designs.
- verbose
The verbosity level (
1: only errors,2: also allows warnings,3: also allows messages,4: also prints additional output useful for debugging).