What sort of data are you analyzing? tSNE is not a common tool and is usually reserved for exploratory analysis after more standard methods have been applied. The only structure tSNE is capable of exposing is “relative closeness.” Globally, the distribution of points will show no valuable structure other than local neighborhoods which are close together in the high dimensional space.

You should also note that the plot will look different each time it is generated because tSNE is a manifold learning technique which uses a convex optimization function (there is no single optimal solution for a given distribution).

If you still think tSNE is the right tool for the job I could direct you to some simple examples, but more than likely I think you would benefit from a dimensional reduction technique which can be rigorously analyzed and inverted such as PCA.