Software - Revision history

WikiSysop at 18:13, 23 February 2012

2012-02-23T18:13:30Z

Gabor: /* The Iterative Signature Algorithm */

2010-03-16T07:58:04Z

‎The Iterative Signature Algorithm

Gabor at 17:18, 30 November 2009

2009-11-30T17:18:10Z

Gabor at 17:00, 30 November 2009

2009-11-30T17:00:19Z

Gabor at 16:36, 30 November 2009

2009-11-30T16:36:13Z

Gabor: Created page with '== The Iterative Signature Algorithm == Large sets of data, like expression profile from many samples, require analytic tools to reduce their complexity. Classical (bi-)…'

2009-11-30T16:28:20Z

Created page with '== The Iterative Signature Algorithm == Large sets of data, like expression profile from many samples, require analytic tools to reduce their complexity. Classical (bi-)…'

New page

== [[ISA|The Iterative Signature Algorithm]] ==

Large sets of data, like expression profile from many samples, require
analytic tools to reduce their complexity. Classical (bi-)clustering
algorithms typically attribute elements (genes, arrays) to disjoint groups
("clusters"). Yet, in some cases overlapping cluster assignments would suit
the biological reality much better.

The '''[[ISA|Iterative Signature Algorithm (ISA)]]''' was designed to overcome this and
other limitations of standard clustering algorithms. It aims 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.

== [[ExpressionView]] ==

[[image:Expressionview.screenshot.png|Screenshot of the ExpressionView applet|500px|left]]
<br style="clear: both" />
<br>
[[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]] ==
-[[image:Expressionview.screenshot.png|Screenshot of the ExpressionView applet|500px|left|link:ExpressionView]]
+[[image:Expressionview.screenshot.png|Screenshot of the ExpressionView applet|500px|left|link=ExpressionView]]
 <br style="clear: both" />
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 <br/>
 Large sets of data, like expression profile from many samples, require
-analytic tools to reduce their complexity. Classical (bi-)clustering
+analytic tools to reduce their complexity.
-algorithms typically attribute elements (genes, arrays) to disjoint groups
+The '''Iterative Signature Algorithm (ISA)''' was designed to reduce the
-("clusters"). Yet, in some cases overlapping cluster assignments would suit
-the biological reality much better.
-−
-The '''[[ISA|Iterative Signature Algorithm (ISA)]]''' was designed to overcome this and
-other limitations of standard clustering algorithms. It aims 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
@@ Line 19: / Line 14: @@
 rely on the computation of correlation matrices (like many other tools), it
 is extremely fast even for very large datasets.
 '''[[ISA|See more here]]'''