TY - GEN
T1 - Analysis of temporal gene expression profiles using time-dependent music algorithm
AU - Bouaynaya, Nidhal
AU - Schonfeld, Dan
AU - Nagarajan, Radhakrishnan
PY - 2009
Y1 - 2009
N2 - Identifying periodically expressed genes and their subsequent transcriptional circuitry can shed new lights in studying the molecular basis of many diseases including cancer; and subsequently provide potential drug targets to treat them. Classical approaches for detecting periodically expressed transcripts in paradigms such as cell-cycle implicitly assume the given data to be stationary. However, it has been experimentally shown that modulation in the magnitude of gene expression is ubiquitous and defy stationary assumptions. In this paper, we formulate the problem of estimating the frequencies of multicomponent amplitude modulated (AM) signals as a hypothesis testing problem based on a time-dependent extension of the MUSIC algorithm. We subsequently propose a test statistic to detect periodic components in AM time-series. The power of the proposed algorithm is assessed in synthetic test signals and in real cell-cycle gene profiles extracted from microarray data.
AB - Identifying periodically expressed genes and their subsequent transcriptional circuitry can shed new lights in studying the molecular basis of many diseases including cancer; and subsequently provide potential drug targets to treat them. Classical approaches for detecting periodically expressed transcripts in paradigms such as cell-cycle implicitly assume the given data to be stationary. However, it has been experimentally shown that modulation in the magnitude of gene expression is ubiquitous and defy stationary assumptions. In this paper, we formulate the problem of estimating the frequencies of multicomponent amplitude modulated (AM) signals as a hypothesis testing problem based on a time-dependent extension of the MUSIC algorithm. We subsequently propose a test statistic to detect periodic components in AM time-series. The power of the proposed algorithm is assessed in synthetic test signals and in real cell-cycle gene profiles extracted from microarray data.
UR - http://www.scopus.com/inward/record.url?scp=70349481964&partnerID=8YFLogxK
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U2 - 10.1109/GENSIPS.2009.5174337
DO - 10.1109/GENSIPS.2009.5174337
M3 - Conference contribution
AN - SCOPUS:70349481964
SN - 9781424447619
T3 - 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009
BT - 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009
T2 - 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009
Y2 - 17 May 2009 through 21 May 2009
ER -