Application of t-SNE to human genetic data

The t-distributed stochastic neighbor embedding t-SNE is a new dimension reduction and visualization technique for high-dimensional data. t-SNE is rarely applied to human genetic data, even though it is commonly used in other data-intensive biological fields, such as single-cell genomics. We explore...

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Veröffentlicht in:Journal of bioinformatics and computational biology Jg. 15; H. 4; S. 1750017
Hauptverfasser: Li, Wentian, Cerise, Jane E, Yang, Yaning, Han, Henry
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore 01.08.2017
Schlagworte:
Computational Biology - methods
Computer Graphics
Genetics, Population
Genome-Wide Association Study
Human Genetics
Humans
Polymorphism, Single Nucleotide
Principal Component Analysis
t-SNE
SNP
dimension reduction
PCA
ISSN:1757-6334, 1757-6334
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Zusammenfassung:The t-distributed stochastic neighbor embedding t-SNE is a new dimension reduction and visualization technique for high-dimensional data. t-SNE is rarely applied to human genetic data, even though it is commonly used in other data-intensive biological fields, such as single-cell genomics. We explore the applicability of t-SNE to human genetic data and make these observations: (i) similar to previously used dimension reduction techniques such as principal component analysis (PCA), t-SNE is able to separate samples from different continents; (ii) unlike PCA, t-SNE is more robust with respect to the presence of outliers; (iii) t-SNE is able to display both continental and sub-continental patterns in a single plot. We conclude that the ability for t-SNE to reveal population stratification at different scales could be useful for human genetic association studies.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1757-6334
1757-6334
DOI:10.1142/S0219720017500172