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Data Visualization

This category contains 6 posts

VIZBI 2014

2013.07.15 — VIZBI 2014 will be 5-7 March 2014 in Heidelberg, Germany.

Visualizing Uncertainty, unresolved.

Data from an experiment may appear rock solid. Upon further examination, the data may morph into something much less firm. A knee-jerk reaction to this conundrum may be to try and hide uncertain scientific results, which are unloved fellow travelers of science. After all, words can afford ambiguity, but with visuals, “we are damned to be concrete,” says Bang Wong, who is the creative director of the Broad Institute of MIT and Harvard. The alternative is to face the ambiguity head-on through visual means.

Illumina iDEA Challenge

7 December 2010 – Judge for Illumina’s challenge to develop new and creative visualization and data analysis techniques

Vis Skunkworks

Effective visualization makes complex data clear. There is an opportunity to use visual representation in innovative ways to gain insights into biological data to effectively communicate science. To do this requires discovering novel visual encoding systems and applying existing ones in new ways. The challenge facing us in biology today is to make sense of […]

VIZBI Conference Series

The 2011 Visualizing Biological Data workshop will be held March 16-18, 2011 at Broad Institute in Cambridge, MA. For further details, www.vizbi.org.

EuroVis 2010 Symposium

10 June 2010 — Presenting with Miriah Meyer at EuroVis Symposium on Visualization, Bordeaux France.

Pathline
Biologists pioneering the new field of comparative functional genomics attempt to infer the mechanisms of gene regulation by looking for similarities and differences of gene activity over time across multiple species. They use three kinds of data: functional data such as gene activity measurements, pathway data that represent a series of reactions within a cellular process, and phylogenetic relationship data that describe the relatedness of species. No existing visualization tool can visually encode the biologically interesting relationships between multiple path- ways, multiple genes, and multiple species. We tackle the challenge of visualizing all aspects of this comparative functional genomics dataset with a new interactive tool called Pathline. In addition to the overall characterization of the problem and design of Pathline, our contributions include two new visual encoding techniques. One is a new method for linearizing metabolic pathways that provides appropriate topological information and supports the comparison of quantitative data along the pathway. The second is the curvemap view, a depiction of time series data for comparison of gene activity and metabolite levels across multiple species. Pathline was developed in close collaboration with a team of genomic scientists. We validate our approach with case studies of the biologists’ use of Pathline and report on how they use the tool to confirm existing findings and to discover new scientific insights.