Abstract
Serial analysis of gene expression (SAGE) is a powerful technique for global gene expression profiling, allowing simultaneous analysis of thousands of transcripts without prior structural and functional knowledge. Pattern discovery and visualization have become fundamental approaches to analyzing such large-scale gene expression data. From the pattern discovery perspective, clustering techniques have received great attention. However, due to the statistical nature of SAGE data (i.e., underlying distribution), traditional clustering techniques may not be suitable for SAGE data analysis. Based on the adaptation and improvement of Self-Organizing Maps and hierarchical clustering techniques, this paper presents two new clustering algorithms, namely, PoissonS and PoissonHC, for SAGE data analysis. Tested on synthetic and experimental SAGE data, these algorithms demonstrate several advantages over traditional pattern discovery techniques. The results indicate that, by incorporating statistical properties of SAGE data, PoissonS and PoissonHC, as well as a hybrid approach (neuro-hierarchical approach) based on the combination of PoissonS and PoissonHC, offer significant improvements in pattern discovery and visualization for SAGE data. Moreover, a user-friendly platform, which may improve and accelerate SAGE data mining, was implemented. The system is freely available on request from the authors for nonprofit use.
| Language | English |
|---|---|
| Pages | 163-175 |
| Number of pages | 13 |
| Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
| Volume | 4 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 7 May 2007 |
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Keywords
- Hybrid machine learning
- Pattern discovery and visualization
- Poisson distribution
- Self-organizing maps
- Serial analysis of gene expression
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Poisson-based self-organizing feature maps and hierarchical clustering for serial analysis of gene expression data. / Wang, Haiying; Zheng, Huiru; Azuaje, Francisco.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 4, No. 2, 07.05.2007, p. 163-175.Research output: Contribution to journal › Article
TY - JOUR
T1 - Poisson-based self-organizing feature maps and hierarchical clustering for serial analysis of gene expression data
AU - Wang, Haiying
AU - Zheng, Huiru
AU - Azuaje, Francisco
PY - 2007/5/7
Y1 - 2007/5/7
N2 - Serial analysis of gene expression (SAGE) is a powerful technique for global gene expression profiling, allowing simultaneous analysis of thousands of transcripts without prior structural and functional knowledge. Pattern discovery and visualization have become fundamental approaches to analyzing such large-scale gene expression data. From the pattern discovery perspective, clustering techniques have received great attention. However, due to the statistical nature of SAGE data (i.e., underlying distribution), traditional clustering techniques may not be suitable for SAGE data analysis. Based on the adaptation and improvement of Self-Organizing Maps and hierarchical clustering techniques, this paper presents two new clustering algorithms, namely, PoissonS and PoissonHC, for SAGE data analysis. Tested on synthetic and experimental SAGE data, these algorithms demonstrate several advantages over traditional pattern discovery techniques. The results indicate that, by incorporating statistical properties of SAGE data, PoissonS and PoissonHC, as well as a hybrid approach (neuro-hierarchical approach) based on the combination of PoissonS and PoissonHC, offer significant improvements in pattern discovery and visualization for SAGE data. Moreover, a user-friendly platform, which may improve and accelerate SAGE data mining, was implemented. The system is freely available on request from the authors for nonprofit use.
AB - Serial analysis of gene expression (SAGE) is a powerful technique for global gene expression profiling, allowing simultaneous analysis of thousands of transcripts without prior structural and functional knowledge. Pattern discovery and visualization have become fundamental approaches to analyzing such large-scale gene expression data. From the pattern discovery perspective, clustering techniques have received great attention. However, due to the statistical nature of SAGE data (i.e., underlying distribution), traditional clustering techniques may not be suitable for SAGE data analysis. Based on the adaptation and improvement of Self-Organizing Maps and hierarchical clustering techniques, this paper presents two new clustering algorithms, namely, PoissonS and PoissonHC, for SAGE data analysis. Tested on synthetic and experimental SAGE data, these algorithms demonstrate several advantages over traditional pattern discovery techniques. The results indicate that, by incorporating statistical properties of SAGE data, PoissonS and PoissonHC, as well as a hybrid approach (neuro-hierarchical approach) based on the combination of PoissonS and PoissonHC, offer significant improvements in pattern discovery and visualization for SAGE data. Moreover, a user-friendly platform, which may improve and accelerate SAGE data mining, was implemented. The system is freely available on request from the authors for nonprofit use.
KW - Hybrid machine learning
KW - Pattern discovery and visualization
KW - Poisson distribution
KW - Self-organizing maps
KW - Serial analysis of gene expression
UR - http://www.scopus.com/inward/record.url?scp=34248386878&partnerID=8YFLogxK
U2 - 10.1109/TCBB.2007.070204
DO - 10.1109/TCBB.2007.070204
M3 - Article
VL - 4
SP - 163
EP - 175
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
T2 - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
SN - 1545-5963
IS - 2
ER -