35,744 research outputs found
Mapping Topographic Structure in White Matter Pathways with Level Set Trees
Fiber tractography on diffusion imaging data offers rich potential for
describing white matter pathways in the human brain, but characterizing the
spatial organization in these large and complex data sets remains a challenge.
We show that level set trees---which provide a concise representation of the
hierarchical mode structure of probability density functions---offer a
statistically-principled framework for visualizing and analyzing topography in
fiber streamlines. Using diffusion spectrum imaging data collected on
neurologically healthy controls (N=30), we mapped white matter pathways from
the cortex into the striatum using a deterministic tractography algorithm that
estimates fiber bundles as dimensionless streamlines. Level set trees were used
for interactive exploration of patterns in the endpoint distributions of the
mapped fiber tracks and an efficient segmentation of the tracks that has
empirical accuracy comparable to standard nonparametric clustering methods. We
show that level set trees can also be generalized to model pseudo-density
functions in order to analyze a broader array of data types, including entire
fiber streamlines. Finally, resampling methods show the reliability of the
level set tree as a descriptive measure of topographic structure, illustrating
its potential as a statistical descriptor in brain imaging analysis. These
results highlight the broad applicability of level set trees for visualizing
and analyzing high-dimensional data like fiber tractography output
Superheat: An R package for creating beautiful and extendable heatmaps for visualizing complex data
The technological advancements of the modern era have enabled the collection
of huge amounts of data in science and beyond. Extracting useful information
from such massive datasets is an ongoing challenge as traditional data
visualization tools typically do not scale well in high-dimensional settings.
An existing visualization technique that is particularly well suited to
visualizing large datasets is the heatmap. Although heatmaps are extremely
popular in fields such as bioinformatics for visualizing large gene expression
datasets, they remain a severely underutilized visualization tool in modern
data analysis. In this paper we introduce superheat, a new R package that
provides an extremely flexible and customizable platform for visualizing large
datasets using extendable heatmaps. Superheat enhances the traditional heatmap
by providing a platform to visualize a wide range of data types simultaneously,
adding to the heatmap a response variable as a scatterplot, model results as
boxplots, correlation information as barplots, text information, and more.
Superheat allows the user to explore their data to greater depths and to take
advantage of the heterogeneity present in the data to inform analysis
decisions. The goal of this paper is two-fold: (1) to demonstrate the potential
of the heatmap as a default visualization method for a wide range of data types
using reproducible examples, and (2) to highlight the customizability and ease
of implementation of the superheat package in R for creating beautiful and
extendable heatmaps. The capabilities and fundamental applicability of the
superheat package will be explored via three case studies, each based on
publicly available data sources and accompanied by a file outlining the
step-by-step analytic pipeline (with code).Comment: 26 pages, 10 figure
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A Single Visualization Technique for Displaying Multiple Metabolite-Phenotype Associations.
To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel 'rain plot' approach to display the results of these analyses. The 'rain plot' combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically
Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures
Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data
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