Improving cluster visualization in self-organizing maps: Application in gene expression data analysis

Authors:
Elmer A. Fernandez;Monica Balzarini
Affiliations:
Faculty of Engineering, Catholic University of Córdoba, Córdoba, Camino Alta Gracia Km 10, Cordoba, Argentina and National Council of Scientific and Technological Research (CONICET), Arg ...;National Council of Scientific and Technological Research (CONICET), Argentina and Statistics and Biometry, National University of Córdoba, Córdoba, Argentina
Venue:
Computers in Biology and Medicine
Year:
2007

Citing 6
Cited 6

Self-organizing maps

Self-organizing maps
Clustering Algorithms

Clustering Algorithms
Cluster validation techniques for genome expression data

Signal Processing - Special issue: Genomic signal processing
Microarray data mining: facing the challenges

ACM SIGKDD Explorations Newsletter
How Many Clusters? An Information-Theoretic Perspective

Neural Computation
ViSOM - a novel method for multivariate data projection and structure visualization

IEEE Transactions on Neural Networks

Discovering the transcriptional modules using microarray data by penalized matrix decomposition

Computers in Biology and Medicine
Application of visual clustering properties of self organizing map in machine-part cell formation

Applied Soft Computing
Research article: Self-organizing map of gene regulatory networks for cell phenotypes during reprogramming

Computational Biology and Chemistry
A new approach for data clustering and visualization using self-organizing maps

Expert Systems with Applications: An International Journal
Multi-stage filtering for improving confidence level and determining dominant clusters in clustering algorithms of gene expression data

Computers in Biology and Medicine
Inferring transcriptional modules from microarray and ChIP-chip data using penalized matrix decomposition

ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Cluster analysis is one of the crucial steps in gene expression pattern (GEP) analysis. It leads to the discovery or identification of temporal patterns and coexpressed genes. GEP analysis involves highly dimensional multivariate data which demand appropriate tools. A good alternative for grouping many multidimensional objects is self-organizing maps (SOM), an unsupervised neural network algorithm able to find relationships among data. SOM groups and maps them topologically. However, it may be difficult to identify clusters with the usual visualization tools for SOM. We propose a simple algorithm to identify and visualize clusters in SOM (the RP-Q method). The RP is a new node-adaptive attribute that moves in a two dimensional virtual space imitating the movement of the codebooks vectors of the SOM net into the input space. The Q statistic evaluates the SOM structure providing an estimation of the number of clusters underlying the data set. The SOM-RP-Q algorithm permits the visualization of clusters in the SOM and their node patterns. The algorithm was evaluated in several simulated and real GEP data sets. Results show that the proposed algorithm successfully displays the underlying cluster structure directly from the SOM and is robust to different net sizes.