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Compute the Bayesian information criterion (BIC)

Source: R/diffexp.R
DESeq2BIC.Rd

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

Value

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

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"!