NormalizeBaseline: Normalization to a baseline

Description

Normalizes data in a grandR object to a baseline and puts the normalized data into a new slot

Usage

NormalizeBaseline(
  data,
  baseline = FindReferences(data, reference = Condition == levels(Condition)[1]),
  name = "baseline",
  slot = DefaultSlot(data),
  set.to.default = FALSE,
  LFC.fun = lfc::PsiLFC,
  ...
)

Value

a new grandR object with an additional slot

Arguments

data: the grandR object
baseline: matrix defining the corresponding baseline (row) for each column (sample or cell; see details)
name: the name of the new slot for the normalized data
slot: the name of the slot for the data to normalize
set.to.default: set the new slot as the default slot
LFC.fun: either NormLFC or PsiLFC from the lfc package
...: forwarded to LFC.fun

Details

Baseline normalization computes the log2 fold change for a column (i.e. sample or cell) to a baseline columns (or several baseline columns). This is by default done using the PsiLFC function from the lfc package, which, by default, also normalizes log2 fold changes by adding a constant such that the median is zero.

Baselines are defined by a square logical matrix, defining for each sample or cell of the grandR object, represented by the column of the matrix, which samples or cells are indeed the baseline (represented by the rows). Such matrices can conveniently be obtained by FindReferences.

Examples

Run this code

sars <- ReadGRAND(system.file("extdata", "sars.tsv.gz", package = "grandR"),
                  design=c("Cell",Design$dur.4sU,Design$Replicate))
blmat <- FindReferences(sars,reference = duration.4sU==0, group = "Cell")
# the Mock.no4sU or SARS.no4sU sample are the baselines for each sample
sars <- NormalizeBaseline(sars,baseline=blmat)
head(GetTable(sars,type="baseline"))