title: Visualization of SNPs with t-SNE.
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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors: Gregor Mendel Institute, Vienna, Austria. alexander.platzer@gmi.oeaw.ac.at
authors: Alexander
authors: A
authors: Platzer
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D030541:Databases, Genetic; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis; D015203:Reproducibility of Results