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t-SNE

expect future deprecation

The next major release of PopGen.jl will remove TSNE.jl as a dependency and this page will instead be a guide on how to use PopGen.jl and TSNE.jl together.

t-distributed stochastic neighbor embedding (a.k.a. t-SNE) is a dimensionality reduction technique for visualizing high-dimensional data. It does this by giving each datapoint a location in a two or three-dimensional map by minimizing the Kullback–Leibler divergence between the high and low dimensionality probability distributions with respect to the locations of the points in the map. It models each high-dimensional object by a two- or three-dimensional point so similar objects are appear nearer and dissimilar objects appear further apart. Read more about it here.

careful parameterization

Visual clusters can be seriously influenced by the parameters. For example, parameters can be chosen in such a way to identify clusters in data that has none. So, a good understanding of the parameters for t-SNE is necessary. Although a useful tool, t-SNE is not commonly used in population genetic analysis. It has been included in this package as a wrapper for TSNE.jl due to its utility in other disciplines.

tnse​

tsne(data::PopData, args...; kwargs...)

Perform t-SNE (t-Stochastic Neighbor Embedding) on a PopData object, returning a DataFrame. Converts the PopData object into a matrix of allele frequencies with missing values replaced with the global mean frequency of that allele. First performs PCA on that matrix, retaining reduce_dims dimensions of the PCA prior to t-SNE analysis. The other positional and keyword arguments are the same as tsne from TSne.jl.

Arguments​

  • data: a PopData object
  • ndims: Dimension of the embedded space (default: 2)
  • reduce_dims the number of the first dimensions of X PCA to use for t-SNE, if 0, all available dimension are used (default: 0)
  • max_iter: Maximum number of iterations for the optimization (default: 1000)
  • perplexity: The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms. Larger datasets usually require a larger perplexity. Consider selecting a value between 5 and 50. Different values can result in significantly different results (default: 30)

Keyword Arguments (optional)​

  • distance: type Function or Distances.SemiMetric, specifies the function to use for calculating the distances between the rows
  • pca_init: whether to use the first ndims of the PCA as the initial t-SNE layout, if false (the default), the method is initialized with the random layout
  • max_iter: how many iterations of t-SNE to do
  • verbose: output informational and diagnostic messages
  • progress: display progress meter during t-SNE optimization (default: true)
  • min_gain: eta: initial_momentum, final_momentum, momentum_switch_iter, stop_cheat_iter: cheat_scale low-level parameters of t-SNE optimization
  • extended_output: if true, returns a tuple of embedded coordinates matrix, point perplexities and final Kullback-Leibler divergence

Acknowledgements​

This function is a wrapper for the another package, so really, all the brilliance and effort should be credited to the authors of TNSE.jl.