SortModes {mlica}R Documentation

Sorting of ICA modes

Description

Sorts inferred ICA modes using two criteria: Relative data power or the Liebermeister criterion, which is based on a measure that is a weighted linear combination of non-gaussianity and data variance measures.

Usage

SortModes(a.best,c.val = 0.25)

Arguments

a.best The output object of mlica.
c.val A parameter to control the relative weight of the two measures when using the Liebermeister criterion. Should be between 0 (pure data variance measure) and 1 (pure non-gaussianity).

Value

A list with components:

a.best The output of mlica.
rdp The relative data power values obtained for each independent component.
lbm The Liebermeister contrast value for each component.

Author(s)

Andrew Teschendorff aet21@cam.ac.uk

References

1
Hyvaerinen A., Karhunen J., and Oja E.: Independent Component Analysis, John Wiley and Sons, New York, (2001).
2
Kreil D. and MacKay D. (2003): Reproducibility Assessment of Independent Component Analysis of Expression Ratios from DNA microarrays, Comparative and Functional Genomics 4 (3),300–317.
3
Liebermeister W. (2002): Linear Modes of gene expression determined by independent component analysis, Bioinformatics 18, no.1, 51–60.


[Package mlica version 0.6.1 Index]