ISAExpressionSet-class {eisa} | R Documentation

## Expression Set, normalized for using with ISA

### Description

An `ExpressionSet` object (`Biobase` package) that contains expression values normalized for use with the Iterative Signature Algorithm.

### Usage

    ## S4 method for signature 'ISAExpressionSet'
    featExprs(object)
    ## S4 method for signature 'ISAExpressionSet'
    sampExprs(object)

    ## S4 method for signature 'ISAExpressionSet'
    hasNA(object)
    ## S4 replacement method for signature 'ISAExpressionSet'
    hasNA(object) <- value

    ## S4 method for signature 'ISAExpressionSet'
    prenormalized(object)
    ## S4 replacement method for signature 'ISAExpressionSet'
    prenormalized(object) <- value

### Arguments

`object` | An `ISAExpressionSet` object.
`value` | A logical scalar, new value of the `hasNA` or `prenormalized` attribute.

### Details

An `ISAExpressionSet` contains three expression matrices.

In most cases, when then `ISAExpressionSet` was produced by the `ISANormalize` function, these are: the original, raw data, the feature-wise scaled and centered data and the sample-wise scaled and centered data.

Two additional methods were defined to access the extra matrices: `featExprs` returns the feature-wise standardized data, `sampExprs` the sample-wise standardized one.

The `hasNA` function returns `TRUE` if `NA` or `NaN` values appear in at least one of the expression matrices.

The `prenormalized` function returns `TRUE` if the data was prenormalized, see `ISANormalize` for details.

### Value

`featExprs` and `sampExprs` both return a matrix.

`hasNA` and `prenormalized` return a logical vector of length one.

### 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

`ISANormalize`, `ExpressionSet` in the `Biobase` package.

### 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]