ICA  Independent Component Analysis
ICA and Bioinformatics
Matthias Scholz, Ph.D. thesis

Applying ICA is motivated by the idea that the variation in molecular data
is generated by divers factors s.
These may include internal biological factors as well as external
environmental or technical factors. Each observed variable x (e.g., gene)
can therefore be seen as derived from a specific combination of these factors.
The illustrated factors may represent an increase of
temperature (s1), an internal circadian
rhythm (s2), and different
ecotypes (s3).
With the assumption that the factors are independent of each other,
ICA can be applied to a data set X in order to identify the original factors s
and the dependencies given by the
matrix A.
Bioinformatics publication 
matlab code

Resources
Tutorials
Books
Conferences
 International Workshop on Independent Component Analysis
and Blind Signal Separation
[2000 
2003 
2004 
2006 
2007 
2010
]
 European Meeting on Independent Component Analysis
[ 2002 
2003 ]

People

list by Paris Smaragdis

list by Allan Kardec Barros
Mailing List
Algorithms

FastICA, Matlab, R, C++, Python (A. Hyvarinen and E. Oja, 2000)
[pdf]

JADE, Matlab (JeanFrançois Cardoso and A. Souloumiac, 1993)
[pdf]

Infomax, Matlab (A.J. Bell and T.J. Sejnowski, 1995)
[ps]

TDSEP, Matlab (A. Ziehe and K.R. Müller, 1998)

CuBICA, Matlab (T. Blaschke and L. Wiskott, 2004)
[pdf]

KernelICA, Matlab
(F.R. Bach and M.I. Jordan, 2002)
[pdf]

see also: Principal Component Analysis (PCA)