ISAExpressionSet: Expression Set, normalized for using with ISA

In eisa: Expression data analysis via the Iterative Signature Algorithm

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 [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

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)

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