Proceedings Article10.1109/HICSS.2003.1174805
Grid-layout visualization method in the microarray data analysis interactive graphics toolkit
Li Xiao,O. Shats,S. Sherman +2 more
- 06 Jan 2003
- Vol. 10, pp 276
TL;DR: A new, grid-layout method for the visualization results of hierarchical cluster analysis of DNA microarray data is proposed and incorporated in the microarray interactive graphics toolkit (MIGT).
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Abstract: The expression levels of thousands of genes in different tissues or cells in different conditions can be detected all at one time by DNA microarray technology A new, grid-layout method for the visualization results of hierarchical cluster analysis of DNA microarray data is proposed and incorporated in the microarray interactive graphics toolkit (MIGT) The grid-layout consists of a set of regular, two-dimensional grid units Each unit represents a cluster or a group of gene clusters The units are connected to adjacent ones by the neighborhood relation of the clusters in a hierarchical tree Nodes lying near each other in the hierarchical tree are mapped onto nearby grid-layout units The number of units may vary on a scale from a few dozen up to several thousands, depending on the number of the nodes in a hierarchical tree Different colors are assigned to the units with RGB value according to the coordinates of the units, and the inter-distances, which are the distances between clusters in a hierarchical tree, and the intra-distances, which are the distances between genes within one cluster The closer the inter-distances, the more similar the color of the units are, the smaller the intra-distances, the warmer the color of the unit is
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Citations
Automatically Locating Spots in DNAMicroarray Image Using Genetic Algorithm without Gridding
A. Sreedevi,Dakshayani S. Jangamashetti +1 more
- 17 Apr 2009
TL;DR: An approach using Genetic algorithm is proposed, in which searching for the spots in sub image is done and the relevant information of the spot is extracted and the process is repeated for the entire image.
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Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
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Self-Organizing Maps
Teuvo Kohonen
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TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
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Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
Ash A. Alizadeh,Michael B. Eisen,R. Eric Davis,Izidore S. Lossos,Andreas Rosenwald,Jennifer C. Boldrick,Hajeer Sabet,Truc Tran,Xin Yu,John Powell,Liming Yang,Gerald E. Marti,Troy Moore,James I. Hudson,Li-Sheng Lu,David B. Lewis,Robert Tibshirani,Gavin Sherlock,Wing C. Chan,Timothy C. Greiner,Dennis D. Weisenburger,James O. Armitage,Roger A. Warnke,Ronald Levy,Wyndham H. Wilson,M. R. Grever,John C. Byrd,David Botstein,Patrick O. Brown,Louis M. Staudt +29 more
TL;DR: It is shown that there is diversity in gene expression among the tumours of DLBCL patients, apparently reflecting the variation in tumour proliferation rate, host response and differentiation state of the tumour.
Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation
Pablo Tamayo,Donna K. Slonim,Jill P. Mesirov,Qing Zhu,Sutisak Kitareewan,Ethan Dmitrovsky,Eric S. Lander,Todd R. Golub,Todd R. Golub +8 more
TL;DR: In this article, the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidi-mensional data, is described.
Exploring the new world of the genome with DNA microarrays
Patrick O. Brown,David Botstein +1 more
TL;DR: Exploration of the genome using DNA microarrays and other genome–scale technologies should narrow the gap in the knowledge of gene function and molecular biology between the currently–favoured model organisms and other species.