DESeq2BIC: Compute the Bayesian information criterion (BIC)

In grandR: Comprehensive Analysis of Nucleotide Conversion Sequencing Data

Compute the Bayesian information criterion (BIC)

Description

Compute the delta BIC for a list of potential models

Usage

DESeq2BIC(
  data,
  name = "BIC",
  mode = "total",
  normalization = mode,
  formulas = list(Condition = ~Condition, Background = ~1),
  no4sU = FALSE,
  columns = NULL,
  verbose = FALSE
)

Arguments

data	A grandR object
name	the user defined analysis name to store the results
mode	either "total", "new" or "old"
normalization	normalize on "total", "new", or "old" (see details)
formulas	list of formulas specifying the models (you can use any column name from the Coldata(data))
no4sU	Use no4sU columns (TRUE) or not (FALSE)
columns	logical vector of which columns (samples or cells) to use (or NULL: use all)
verbose	Print status updates

Details

DESeq2 by default performs size factor normalization. When computing differential expression of new RNA, it might be sensible to normalize w.r.t. to total RNA, i.e. use the size factors computed from total RNA instead of computed from new RNA. This can be accomplished by setting mode to "new", and normalization to "total"!

Value

a new grandR object including a new analysis table. The columns of the new analysis table are named as <name in list>.dBIC

grandR documentation built on April 4, 2025, 2:27 a.m.