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Visualization of SNPs with t-SNE

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  • Alexander Platzer

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.

Suggested Citation

  • Alexander Platzer, 2013. "Visualization of SNPs with t-SNE," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-6, February.
    • Handle: RePEc:plo:pone00:0056883
    DOI: 10.1371/journal.pone.0056883
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    References listed on IDEAS

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    1. Warren Torgerson, 1952. "Multidimensional scaling: I. Theory and method," Psychometrika, Springer;The Psychometric Society, vol. 17(4), pages 401-419, December.
    2. Struyf, Anja & Hubert, Mia & Rousseeuw, Peter, 1997. "Clustering in an Object-Oriented Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 1(i04).
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    Cited by:

    1. Rong Ma & Eric D. Sun & James Zou, 2023. "A spectral method for assessing and combining multiple data visualizations," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Ramezani, Zahra & Pourdarvish, Ahmad, 2021. "Transfer learning using Tsallis entropy: An application to Gravity Spy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).

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