t-SNE of a 99% sparse data set I have a sparse matrix of representing 12 cancer types. It's a very sparse with about 99% of elements are zeros.
I have a t-SNE looks like:

What can I interpret from this t-SNE?

So scikit suggests to use TruncatedSCD for sparse data. (Althouh that suggestion is only made when you have a large amount of initial features)
https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html

It is highly recommended to use another dimensionality reduction method (e.g. PCA for dense data or TruncatedSVD for sparse data) to reduce the number of dimensions to a reasonable amount (e.g. 50) if the number of features is very high.

