Searching for best lower dimensional visualization angles for high dimensional RNA-Seq data.

ABSTRACT: The accumulation of RNA sequencing (RNA-Seq) gene expression data in recent years has resulted in large and complex data sets of high dimensions. Exploratory analysis, including data mining and visualization, reveals hidden patterns and potential outliers in such data, but is often challenged by the high dimensional nature of the data. The scatterplot matrix is a commonly used tool for visualizing multivariate data, and allows us to view multiple bivariate relationships simultaneously. However, the scatterplot matrix becomes less effective for high dimensional data because the number of bivariate displays increases quadratically with data dimensionality. In this study, we introduce a selection criterion for each bivariate scatterplot and design/implement an algorithm that automatically sca

SUBMITTER: Zhang W 

PROVIDER: S-EPMC6046202 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature