Visualizing dimensionality reduction of systems biology data

TL;DR

This paper introduces a visual analytics framework for interpreting linear and non-linear dimensionality reduction methods applied to high-dimensional biological data, enhancing signal detection and interpretation.

Contribution

The authors developed a comprehensive framework within SpRay for visualizing and interpreting multiple dimension reduction techniques in systems biology data analysis.

Findings

Effective visualization of high-dimensional data signals.

Application to microarray data of Streptomyces coelicolor.

Analysis of human trisomy microarray datasets.

Abstract

One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system and which were hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects of the data need to be applied. We have developed a framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in the antibiotic-producing organism Streptomyces coelicolor as well as to microarray data measuring expression of cells with normal karyotype and cells with trisomies of human chromosomes 13 and 21.