Visualization of SNPs with t-SNE

PLoS One. 2013;8(2):e56883. doi: 10.1371/journal.pone.0056883. Epub 2013 Feb 15.

Abstract

Background: Single Nucleotide Polymorphisms (SNPs) are one of the largest sources of new data in biology. In most papers, SNPs between individuals are visualized with Principal Component Analysis (PCA), an older method for this purpose.

Principal findings: We compare PCA, an aging method for this purpose, with a newer method, t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of large SNP datasets. We also propose a set of key figures for evaluating these visualizations; in all of these t-SNE performs better.

Significance: To transform data PCA remains a reasonably good method, but for visualization it should be replaced by a method from the subfield of dimension reduction. To evaluate the performance of visualization, we propose key figures of cross-validation with machine learning methods, as well as indices of cluster validity.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cluster Analysis
  • Computational Biology / methods*
  • Computer Graphics*
  • Databases, Genetic
  • Humans
  • Polymorphism, Single Nucleotide*
  • Principal Component Analysis
  • Reproducibility of Results

Grant support

Funding was provided by the Austrian Academy of Science (http://www.oeaw.ac.at/english/home.html). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.