NMF (version 0.20.6)

NMFOffset-class: NMF Model - Nonnegative Matrix Factorization with Offset

Description

This class implements the Nonnegative Matrix Factorization with Offset model, required by the NMF with Offset algorithm.

Usage

## S3 method for class 'NMFOffset':
initialize(.Object, ..., offset)

Arguments

offset

optional numeric vector used to initialise slot offset.

.Object

An object: see the Details section.

...

data to include in the new object. Named arguments correspond to slots in the class definition. Unnamed arguments must be objects from classes that this class extends.

Creating objects from the Class

Object of class NMFOffset can be created using the standard way with operator new

However, as for all NMF model classes -- that extend class NMF, objects of class NMFOffset should be created using factory method nmfModel :

new('NMFOffset')

nmfModel(model='NMFOffset')

nmfModel(model='NMFOffset', W=w, offset=rep(1, nrow(w)))

See nmfModel for more details on how to use the factory method.

Initialize method

The initialize method for NMFOffset objects tries to correct the initial value passed for slot offset, so that it is consistent with the dimensions of the NMF model: it will pad the offset vector with NA values to get the length equal to the number of rows in the basis matrix.

Details

The NMF with Offset algorithm is defined by Badea (2008) as a modification of the euclidean based NMF algorithm from Lee2001 (see section Details and references below). It aims at obtaining 'cleaner' factor matrices, by the introduction of an offset matrix, explicitly modelling a feature specific baseline -- constant across samples.

References

Badea L (2008). "Extracting gene expression profiles common to colon and pancreatic adenocarcinoma using simultaneous nonnegative matrix factorization." _Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing_, *290*, pp. 267-78. ISSN 1793-5091, .

See Also

Other NMF-model: NMFns-class, NMFstd-class

Examples

# roxygen generated flag
options(R_CHECK_RUNNING_EXAMPLES_=TRUE)

# create a completely empty NMF object
new('NMFOffset')

# create a NMF object based on random (compatible) matrices
n <- 50; r <- 3; p <- 20
w <- rmatrix(n, r)
h <- rmatrix(r, p)
nmfModel(model='NMFOffset', W=w, H=h, offset=rep(0.5, nrow(w)))

# apply Nonsmooth NMF algorithm to a random target matrix
V <- rmatrix(n, p)
nmf(V, r, 'offset')

# random NMF model with offset
rnmf(3, 10, 5, model='NMFOffset')