Application of t-SNE to Human Genetic Data
By Wentian Li, J. Cerise, Yaning Yang, Henry Han. 2017
167 citations recorded by the source corpus.
The t-SNE (t-distributed stochastic neighbor embedding) 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.
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Citations in the corpus
- A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, methodology): “easure visually displayed as query-result activation map pair. t-SNE has been applied on human genetic data and shown to provide more robust dimensionality reduction compared to PCA and other methods [136]. Multiple maps t-SNE (mm-tSNE) is introduced by [137], performing clustering on phenotype simi”
- Revealing multi-scale population structure in large cohorts (2018, methodology): “Using data from 37 the 1000 Genome Project (1KGP)(7), it groups individuals corresponding roughly to their continent 38 of origin, with smaller ethnic sub-groups visible within the larger continental clusters(10).”