vs.norm: Variance stabilizing normalization

In LXQin/precision: PaiREd miCrorna sImulation on Study desIgn for mOlecular classificatioN

Description

Normalize training dataset with vsn and store the fitted vsn model from the training dataset as the reference to frozen variance stabilizing normalize test dataset. Also two other options are available: to only normalize a training dataset but not frozen normalize a test dataset, or vise versa.

Usage

vs.norm(train = NULL, test = NULL, ref.dis = NULL)

Arguments

train	training dataset to be variance stabilizing normalized. The dataset must have rows as probes and columns as samples. This can be left unspecified if ref.dis is suppied for frozen normalize test set.
test	test dataset to be frozen variance stabilizing normalized. The dataset must have rows as probes and columns as samples. The number of rows must equal to the number of rows in the training set. By default, the test set is not specified (test = NULL) and no frozen normalization will be performed.
ref.dis	reference distribution for frozen variance stabilizing normalize test set against previously normalized training set. This is required when train is not supplied. By default, ref.dis = NULL.

Value

a list of two datasets and one reference distribution:

train.mn	the normalized training set
test.fmn	the frozen normalized test set, if test set is specified
ref.dis	the reference distribution

References

Wolfgang Huber, Anja von Heydebreck, Holger Sueltmann, Annemarie Poustka and Martin Vingron. Variance Stabilization Applied to Microarray Data Calibration and to the Quantification of Differential Expression. Bioinformatics 18, S96-S104 (2002).

Examples

## Not run: 
set.seed(101)
group.id <- substr(colnames(nuhdata.pl), 7, 7)
train.ind <- colnames(nuhdata.pl)[c(sample(which(group.id == "E"), size = 64),
                               sample(which(group.id == "V"), size = 64))]
train.dat <- nuhdata.pl[, train.ind]
test.dat <- nuhdata.pl[, !colnames(nuhdata.pl) %in% train.ind]

# normalize only training set
data.vsn <- vs.norm(train = train.dat)
str(data.vsn)

# normalize training set and frozen normalize test set
data.vsn <- vs.norm(train = train.dat, test = test.dat)
str(data.vsn)

# frozen normalize test set with reference distribution
ref <- vs.norm(train = train.dat)$ref.dis
data.vsn <- vs.norm(test = test.dat, ref.dis = ref)
str(data.vsn)

## End(Not run)

LXQin/precision documentation built on May 11, 2019, 6:24 p.m.