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Activity Number: 594 - Methods for Analysis of High-Dimensional Data
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #328808 Presentation
Title: Finding Best Low Dimensional Angles for Visualizing High-Dimensional Data
Author(s): Yanming Di* and Wanli Zhang
Companies: Oregon State University and Oregon State University
Keywords: visualization; dimension reducton; RNA-Seq; scatterplot; clustering; mclust

The accumulation of RNA-Seq gene expression data in recent years has resulted in large and complex data sets of high dimensions. The scatterplot matrix is a commonly used tool for visualizing multivariate data and shows all possible bivariate projections. However, the scatterplot matrix becomes less effective for high dimensional data because the number of bivariate displays increases quadratically with data dimensionality. In this talk, we discuss a criterion and an algorithm for selecting best low dimensional angles for visualizing high-dimensional data. We apply our method to a multi-experiment Arabidopsis RNA-Seq data set with the goal of identifying experimental features that best distinguish two pre-defined groups of genes.

Authors who are presenting talks have a * after their name.

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