10,999 research outputs found
Visualizing dimensionality reduction of systems biology data
One of the challenges in analyzing high-dimensional expression data is the
detection of important biological signals. A common approach is to apply a
dimension reduction method, such as principal component analysis. Typically,
after application of such a method the data is projected and visualized in the
new coordinate system, using scatter plots or profile plots. These methods
provide good results if the data have certain properties which become visible
in the new coordinate system and which were hard to detect in the original
coordinate system. Often however, the application of only one method does not
suffice to capture all important signals. Therefore several methods addressing
different aspects of the data need to be applied. We have developed a framework
for linear and non-linear dimension reduction methods within our visual
analytics pipeline SpRay. This includes measures that assist the interpretation
of the factorization result. Different visualizations of these measures can be
combined with functional annotations that support the interpretation of the
results. We show an application to high-resolution time series microarray data
in the antibiotic-producing organism Streptomyces coelicolor as well as to
microarray data measuring expression of cells with normal karyotype and cells
with trisomies of human chromosomes 13 and 21
Predicting evolution and visualizing high-dimensional fitness landscapes
The tempo and mode of an adaptive process is strongly determined by the
structure of the fitness landscape that underlies it. In order to be able to
predict evolutionary outcomes (even on the short term), we must know more about
the nature of realistic fitness landscapes than we do today. For example, in
order to know whether evolution is predominantly taking paths that move upwards
in fitness and along neutral ridges, or else entails a significant number of
valley crossings, we need to be able to visualize these landscapes: we must
determine whether there are peaks in the landscape, where these peaks are
located with respect to one another, and whether evolutionary paths can connect
them. This is a difficult task because genetic fitness landscapes (as opposed
to those based on traits) are high-dimensional, and tools for visualizing such
landscapes are lacking. In this contribution, we focus on the predictability of
evolution on rugged genetic fitness landscapes, and determine that peaks in
such landscapes are highly clustered: high peaks are predominantly close to
other high peaks. As a consequence, the valleys separating such peaks are
shallow and narrow, such that evolutionary trajectories towards the highest
peak in the landscape can be achieved via a series of valley crossingsComment: 12 pages, 7 figures. To appear in "Recent Advances in the Theory and
Application of Fitness Landscapes" (A. Engelbrecht and H. Richter, eds.).
Springer Series in Emergence, Complexity, and Computation, 201
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