fit_predictive_model: Generate simulated differential expression for two conditions

In kimberlyroche/codaDE: What the Package Does in One 'Title Case' Line

Generate simulated differential expression for two conditions

Description

Generate simulated differential expression for two conditions

Usage

fit_predictive_model(
  DE_methods = c("ALDEx2", "DESeq2", "scran"),
  use_baseline = "oracle",
  use_totals = FALSE,
  use_renorm_counts = FALSE,
  output_weights = TRUE,
  train_percent = 0.8,
  use_cpm = FALSE
)

Arguments

DE_methods	which DE calling method's results to predict; if "all", prediction is over all results together
use_baseline	"self" or "oracle"
use_totals	pull fold change in total estimate from absolute counts
use_renorm_counts	pull features generated from method-renormalized data
output_weights	flag indicating whether or not to plot some visualization of feature weights
train_percent	percent of simulated datasets to train on
use_cpm	use counts per million for relative abundances

Value

NULL (fitted models are saved in output directory)

kimberlyroche/codaDE documentation built on May 11, 2022, 12:40 a.m.