ISANormalize {eisa} | R Documentation

## Normalize expression data for 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 csardi.gabor@gmail.com

### 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)

[Package eisa version 1.36.0 Index]