Independent component analysis reveals new and biologically significant structures in micro array data.

ABSTRACT:

Background

An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS) problem. BSS attempts to separate a mixture of signals into their different sources and refers to the problem of recovering signals from several observed linear mixtures. In the context of micro array data, "sources" may correspond to specific cellular responses or to co-regulated genes.

Results

We applied independent component analysis (ICA) to three different microarray data sets; two tumor data sets and one time series experiment. To obtain reliable components we used iterated ICA to estimate component centrotypes. We found that many of the low ranking components indeed may show a strong biological coher

SUBMITTER: Frigyesi A 

PROVIDER: S-EPMC1557674 | biostudies-literature | 2006 Jun

REPOSITORIES: biostudies-literature