Download Experiments PossionS + PossionHC Mouse Retinal

國立雲林科技大學
National Yunlin University of Science and Technology
Poisson-Based Self-Organizing Feature
Maps and Hierarchical Clustering for
Serial Analysis of Gene Expression Data
Presenter : Shao-Wei Cheng
Authors : Haiying Wang, Huiru Zheng, and Francisco Azuaje
TCBB 2007
Intelligent Database Systems Lab
Outline
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Motivation
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Objective
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Methodology
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Experiments and Results
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Conclusion
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Personal Comments
N.Y.U.S.T.
I. M.
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Motivation
N.Y.U.S.T.
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Serial analysis of gene expression (SAGE)
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PoissonC is an adaptation the k-means. But PoissonC fails
to provide users with a platform to explore complex
relationships between clusters.
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SOM can be used for visualization. But SOM has shown
poor performance on SAGE data analysis by euclidean
distance.
Intelligent Database Systems Lab
Objectives
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To implement and evaluate an adaptation of the SOM
algorithm (PoissonS)
incorporates Poisson-statistics-based distance function into the SOM
learning process to support SAGE data analysis.
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N.Y.U.S.T.
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To integrate Poisson-based distance into a hierarchical
clustering system (PoissonHC)
Be combined with PoissonS to further improve pattern discovery and
visualization for large SAGE data sets.
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Methodology
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Poisson distribution
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PoissonS
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Methodology
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N.Y.U.S.T.
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PoissonHC
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Experiments
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N.Y.U.S.T.
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Data sets
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Synthetic Data
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Mouse Retinal SAGE Data
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Human Cancer SAGE Data
Compared algorithms：
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SOM with Euclidean distance
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SOM with Pearson Correlation
Intelligent Database Systems Lab
Experiments
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N.Y.U.S.T.
I. M.
PossionS
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Synthetic Data
Euclidean distance
Pearson Correlation
Intelligent Database Systems Lab
Experiments
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N.Y.U.S.T.
I. M.
PossionHC
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Mouse Retinal SAGE Data
Euclidean distance
Pearson Correlation
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Experiments
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SAGE libraries
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PossionHC
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Human Cancer
SAGE tags
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Experiments
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N.Y.U.S.T.
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PossionS + PossionHC  Mouse Retinal SAGE Data
Intelligent Database Systems Lab
Conclusion
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N.Y.U.S.T.
I. M.
By incorporating a Poisson statistics-based distance into
the SOM learning algorithm and hierarchical clustering
techniques, significant improvements in pattern discovery
and visualization for SAGE data are accomplished.
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Intelligent Database Systems Lab
Personal Comments
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Advantage

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Incorporates Poisson-statistics-based distance function into the SOM
and hierarchical clustering.
Drawback
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N.Y.U.S.T.
I. M.
Can’t determine the optimal number of clusters automatically.
Application
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Clustering about SAGE data. .
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Intelligent Database Systems Lab