Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

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

Frigyesi, Attila LU ; Veerla, Srinivas LU ; Lindgren, David LU and Höglund, Mattias LU (2006) In BMC Bioinformatics 7.
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... (More)
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 coherence and hence be of biological significance. Generally ICA achieved a higher resolution when compared with results based on correlated expression and a larger number of gene clusters with significantly enriched for gene ontology ( GO) categories. In addition, components characteristic for molecular subtypes and for tumors with specific chromosomal translocations were identified. ICA also identified more than one gene clusters significant for the same GO categories and hence disclosed a higher level of biological heterogeneity, even within coherent groups of genes. Conclusion: Although the ICA approach primarily detects hidden variables, these surfaced as highly correlated genes in time series data and in one instance in the tumor data. This further strengthens the biological relevance of latent variables detected by ICA. (Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
BMC Bioinformatics
volume
7
publisher
BioMed Central (BMC)
external identifiers
  • wos:000240207000001
  • pmid:16762055
  • scopus:33748201591
ISSN
1471-2105
DOI
10.1186/1471-2105-7-290
language
English
LU publication?
yes
id
21be228f-9d4d-44f6-a6f5-ff1ca4a65a30 (old id 908502)
date added to LUP
2016-04-01 15:41:22
date last changed
2020-12-08 02:56:11
@article{21be228f-9d4d-44f6-a6f5-ff1ca4a65a30,
  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 coherence and hence be of biological significance. Generally ICA achieved a higher resolution when compared with results based on correlated expression and a larger number of gene clusters with significantly enriched for gene ontology ( GO) categories. In addition, components characteristic for molecular subtypes and for tumors with specific chromosomal translocations were identified. ICA also identified more than one gene clusters significant for the same GO categories and hence disclosed a higher level of biological heterogeneity, even within coherent groups of genes. Conclusion: Although the ICA approach primarily detects hidden variables, these surfaced as highly correlated genes in time series data and in one instance in the tumor data. This further strengthens the biological relevance of latent variables detected by ICA.},
  author       = {Frigyesi, Attila and Veerla, Srinivas and Lindgren, David and Höglund, Mattias},
  issn         = {1471-2105},
  language     = {eng},
  publisher    = {BioMed Central (BMC)},
  series       = {BMC Bioinformatics},
  title        = {Independent component analysis reveals new and biologically significant structures in micro array data},
  url          = {http://dx.doi.org/10.1186/1471-2105-7-290},
  doi          = {10.1186/1471-2105-7-290},
  volume       = {7},
  year         = {2006},
}