We introduce ggbio, a new methodology to visualize and explore genomics annotations
and high-throughput data. The plots provide detailed views of genomic regions,
summary views of sequence alignments and splicing patterns, and genome-wide overviews
with karyogram, circular and grand linear layouts. The methods leverage the
statistical functionality available in R, the grammar of graphics and the data
handling capabilities of the Bioconductor project. The plots are specified within a
modular framework that enables users to construct plots in a systematic way, and are
generated directly from Bioconductor data structures. The ggbio R package is
available at
http://www.bioconductor.org/packages/2.11/bioc/html/ggbio.html.
We propose to furnish visual statistical methods with an inferential framework and protocol, modelled on confirmatory statistical testing. In this framework, plots take on the role of test statistics, and human cognition the role of statistical tests. Statistical significance of 'discoveries' is measured by having the human viewer compare the plot of the real dataset with collections of plots of simulated datasets. A simple but rigorous protocol that provides inferential validity is modelled after the 'lineup' popular from criminal legal procedures. Another protocol modelled after the 'Rorschach' inkblot test, well known from (pop-)psychology, will help analysts acclimatize to random variability before being exposed to the plot of the real data. The proposed protocols will be useful for exploratory data analysis, with reference datasets simulated by using a null assumption that structure is absent. The framework is also useful for model diagnostics in which case reference datasets are simulated from the model in question. This latter point follows up on previous proposals. Adopting the protocols will mean an adjustment in working procedures for data analysts, adding more rigour, and teachers might find that incorporating these protocols into the curriculum improves their students' statistical thinking.
GGobi is a direct descendent of a data visualization system called XGobi that has been around since the early 1990's. GGobi's new features include multiple plotting windows, a color lookup table manager, and an XML (Extensible Markup Language) file format for data. Perhaps the biggest advance is that GGobi can be easily extended, either by being embedded in other software or by the addition of plugins; either way, it can be controlled using an API (Application Programming Interface). An illustration of its extensibility is that it can be embedded in R. The result is a full marriage between GGobi's direct manipulation graphical environment and R's familiar extensible environment for statistical data analysis.
How do we know if what we see is really there? When visualizing data, how do we avoid falling into the trap of apophenia where we see patterns in random noise? Traditionally, infovis has been concerned with discovering new relationships, and statistics with preventing spurious relationships from being reported. We pull these opposing poles closer with two new techniques for rigorous statistical inference of visual discoveries. The "Rorschach" helps the analyst calibrate their understanding of uncertainty and "line-up" provides a protocol for assessing the significance of visual discoveries, protecting against the discovery of spurious structure.
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