Blind source separation methods for deconvolution of complex signals in cancer biology

Andrei Zinovyev; Ulykbek Kairov; Tatyana Karpenyuk; Erlan Ramanculov

doi:10.1016/j.bbrc.2012.12.043

Blind source separation methods for deconvolution of complex signals in cancer biology

Andrei Zinovyev, Ulykbek Kairov, Tatyana Karpenyuk, Erlan Ramanculov

Research output: Contribution to journal › Short survey › peer-review

16 Citations (Scopus)

Abstract

Two blind source separation methods (Independent Component Analysis and Non-negative Matrix Factorization), developed initially for signal processing in engineering, found recently a number of applications in analysis of large-scale data in molecular biology. In this short review, we present the common idea behind these methods, describe ways of implementing and applying them and point out to the advantages compared to more traditional statistical approaches. We focus more specifically on the analysis of gene expression in cancer. The review is finalized by listing available software implementations for the methods described.

Original language	English
Pages (from-to)	1182-1187
Number of pages	6
Journal	Biochemical and Biophysical Research Communications
Volume	430
Issue number	3
DOIs	https://doi.org/10.1016/j.bbrc.2012.12.043
Publication status	Published - Jan 18 2013
Externally published	Yes

Keywords

Cancer
Data analysis
Gene expression
Independent component analysis
Linear data approximation
Non-negative matrix factorization

ASJC Scopus subject areas

Biophysics
Biochemistry
Molecular Biology
Cell Biology

Access to Document

10.1016/j.bbrc.2012.12.043

Fingerprint Dive into the research topics of 'Blind source separation methods for deconvolution of complex signals in cancer biology'. Together they form a unique fingerprint.

Cite this

@article{6852c357762a42c496c684149b4ad0d6,

title = "Blind source separation methods for deconvolution of complex signals in cancer biology",

abstract = "Two blind source separation methods (Independent Component Analysis and Non-negative Matrix Factorization), developed initially for signal processing in engineering, found recently a number of applications in analysis of large-scale data in molecular biology. In this short review, we present the common idea behind these methods, describe ways of implementing and applying them and point out to the advantages compared to more traditional statistical approaches. We focus more specifically on the analysis of gene expression in cancer. The review is finalized by listing available software implementations for the methods described.",

keywords = "Cancer, Data analysis, Gene expression, Independent component analysis, Linear data approximation, Non-negative matrix factorization",

author = "Andrei Zinovyev and Ulykbek Kairov and Tatyana Karpenyuk and Erlan Ramanculov",

note = "Funding Information: This work is supported by the grant INCA LABEL Canc{\'e}ropole Ile-de-France , by the grant INVADE from ITMO Cancer (Call Systems Biology 2012) and by the grant “Projet Incitatif et Collaboratif Computational Systems Biology Approach for Cancer” from Institut Curie. AZ is a member of the team “Computational Systems Biology of Cancer”, Equipe labellis{\'e}e par la Ligue Nationale Contre le Cancer. UK is supported by the governmental grant of the Ministry of Education and Science of the Republic of Kazakhstan. ",

year = "2013",

month = jan,

day = "18",

doi = "10.1016/j.bbrc.2012.12.043",

language = "English",

volume = "430",

pages = "1182--1187",

journal = "Biochemical and Biophysical Research Communications",

issn = "0006-291X",

publisher = "Academic Press Inc.",

number = "3",

}

TY - JOUR

T1 - Blind source separation methods for deconvolution of complex signals in cancer biology

AU - Zinovyev, Andrei

AU - Kairov, Ulykbek

AU - Karpenyuk, Tatyana

AU - Ramanculov, Erlan

N1 - Funding Information: This work is supported by the grant INCA LABEL Cancéropole Ile-de-France , by the grant INVADE from ITMO Cancer (Call Systems Biology 2012) and by the grant “Projet Incitatif et Collaboratif Computational Systems Biology Approach for Cancer” from Institut Curie. AZ is a member of the team “Computational Systems Biology of Cancer”, Equipe labellisée par la Ligue Nationale Contre le Cancer. UK is supported by the governmental grant of the Ministry of Education and Science of the Republic of Kazakhstan.

PY - 2013/1/18

Y1 - 2013/1/18

N2 - Two blind source separation methods (Independent Component Analysis and Non-negative Matrix Factorization), developed initially for signal processing in engineering, found recently a number of applications in analysis of large-scale data in molecular biology. In this short review, we present the common idea behind these methods, describe ways of implementing and applying them and point out to the advantages compared to more traditional statistical approaches. We focus more specifically on the analysis of gene expression in cancer. The review is finalized by listing available software implementations for the methods described.

AB - Two blind source separation methods (Independent Component Analysis and Non-negative Matrix Factorization), developed initially for signal processing in engineering, found recently a number of applications in analysis of large-scale data in molecular biology. In this short review, we present the common idea behind these methods, describe ways of implementing and applying them and point out to the advantages compared to more traditional statistical approaches. We focus more specifically on the analysis of gene expression in cancer. The review is finalized by listing available software implementations for the methods described.

KW - Cancer

KW - Data analysis

KW - Gene expression

KW - Independent component analysis

KW - Linear data approximation

KW - Non-negative matrix factorization

UR - http://www.scopus.com/inward/record.url?scp=84872502291&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84872502291&partnerID=8YFLogxK

U2 - 10.1016/j.bbrc.2012.12.043

DO - 10.1016/j.bbrc.2012.12.043

M3 - Short survey

C2 - 23261450

AN - SCOPUS:84872502291

VL - 430

SP - 1182

EP - 1187

JO - Biochemical and Biophysical Research Communications

JF - Biochemical and Biophysical Research Communications

SN - 0006-291X

IS - 3

ER -