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[[Category:Homepage]]
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== [[ISA|The Iterative Signature Algorithm]] ==
 
== [[ISA|The Iterative Signature Algorithm]] ==
  
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[[image:expmat.png|An ISA transcription module|300px|right|link=ISA]]
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<br/>
 
Large sets of data, like expression profile from many samples, require
 
Large sets of data, like expression profile from many samples, require
analytic tools to reduce their complexity. Classical (bi-)clustering
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analytic tools to reduce their complexity.  
algorithms typically attribute elements (genes, arrays) to disjoint groups
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The '''Iterative Signature Algorithm (ISA)''' was designed to reduce the
("clusters"). Yet, in some cases overlapping cluster assignments would suit
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the biological reality much better.
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The '''[[ISA|Iterative Signature Algorithm (ISA)]]''' was designed to overcome this and
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other limitations of standard clustering algorithms. It aims to reduce the
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complexity of very large sets of data by decomposing it into so-called
 
complexity of very large sets of data by decomposing it into so-called
 
"modules". In the context of gene expression data these modules consist of
 
"modules". In the context of gene expression data these modules consist of
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rely on the computation of correlation matrices (like many other tools), it
 
rely on the computation of correlation matrices (like many other tools), it
 
is extremely fast even for very large datasets.
 
is extremely fast even for very large datasets.
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'''[[ISA|See more here]]'''
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== [[ExpressionView]] ==
 
== [[ExpressionView]] ==
  
[[image:Expressionview.screenshot.png|Screenshot of the ExpressionView applet|500px|left]]
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[[image:Expressionview.screenshot.png|Screenshot of the ExpressionView applet|500px|left|link=ExpressionView]]
 
<br style="clear: both" />
 
<br style="clear: both" />
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[[ExpressionView]] is an R package that provides an interactive environment to explore biclusters identified in gene expression data. A sophisticated ordering algorithm is used to present the biclusters in a visually appealing layout. From this overview, the user can select individual biclusters and access all the biologically relevant data associated with it. The package is aimed to facilitate the collaboration between bioinformaticians and life scientists who are not familiar with the R language.
 
[[ExpressionView]] is an R package that provides an interactive environment to explore biclusters identified in gene expression data. A sophisticated ordering algorithm is used to present the biclusters in a visually appealing layout. From this overview, the user can select individual biclusters and access all the biologically relevant data associated with it. The package is aimed to facilitate the collaboration between bioinformaticians and life scientists who are not familiar with the R language.
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'''[[ExpressionView|See more here]]'''

Latest revision as of 19:13, 23 February 2012


[edit] The Iterative Signature Algorithm

An ISA transcription module


Large sets of data, like expression profile from many samples, require analytic tools to reduce their complexity. The Iterative Signature Algorithm (ISA) was designed to reduce the complexity of very large sets of data by decomposing it into so-called "modules". In the context of gene expression data these modules consist of subsets of genes that exhibit a coherent expression profile only over a subset of microarray experiments. Genes and arrays may be attributed to multiple modules and the level of required coherence can be varied resulting in different "resolutions" of the modular mapping. Since the ISA does not rely on the computation of correlation matrices (like many other tools), it is extremely fast even for very large datasets. See more here


[edit] ExpressionView

Screenshot of the ExpressionView applet



ExpressionView is an R package that provides an interactive environment to explore biclusters identified in gene expression data. A sophisticated ordering algorithm is used to present the biclusters in a visually appealing layout. From this overview, the user can select individual biclusters and access all the biologically relevant data associated with it. The package is aimed to facilitate the collaboration between bioinformaticians and life scientists who are not familiar with the R language.

See more here