ISA.normalize: Normalize expression data for the Iterative Signature...

In eisa: Expression data analysis via the Iterative Signature Algorithm

Description

ISA works best if the input data is centered and scaled. ISANormalize performs this transformation.

Usage

ISANormalize (data, prenormalize = FALSE)

Arguments

data	An ExpressionSet object.
prenormalize	If this argument is set to TRUE, then feature-wise scaling is calculated on the sample-wise scaled matrix and not on the input matrix directly.

Details

It was observed that the ISA works better if the input matrix is scaled and its rows have mean zero and standard deviation one.

An ISA step consists of two sub-steps, and this implies two different normalizations, in the first the rows (=features), in the second the columns (=samples) of the input matrix will be scaled and centered.

Value

An ISAExpressionSet object.

Author(s)

Gabor Csardi [e-mail]

References

Bergmann S, Ihmels J, Barkai N: Iterative signature algorithm for the analysis of large-scale gene expression data Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Mar;67(3 Pt 1):031902. Epub 2003 Mar 11.

See Also

The ISA function for an easier ISA workflow.

Examples

library(ALL)
data(ALL)

# Do the normalization
ALL.normed <- ISANormalize(ALL)
class(ALL.normed)
dim(exprs(ALL.normed))
dim(featExprs(ALL.normed))
dim(sampExprs(ALL.normed))

# Check that we indeed have Z-scores
all(abs(apply(featExprs(ALL.normed), 2, mean) ) < 1e-12)
all(abs(1-apply(featExprs(ALL.normed), 2, sd)) < 1e-12)

all(abs(apply(sampExprs(ALL.normed), 1, mean) ) < 1e-12)
all(abs(1-apply(sampExprs(ALL.normed), 1, sd)) < 1e-12)

eisa documentation built on Nov. 8, 2020, 6:47 p.m.