Remove t-SNE from docs

README.md

@@ -304,14 +304,11 @@ Tree2
 ## Dimensionality reduction
 
 For visualizing high-dimensional data, e.g. in two dimensions, potentially getting more insights into your data, you can
-reduce the dimensionality of the measurements, using these algorithms:
+reduce the dimensionality of the measurements, using this algorithm:
 
 * UMAP
     * [Uniform Manifold Approximation Projection (UMAP)](https://arxiv.org/abs/1802.03426)
     * [UMAP Python implementation](https://umap-learn.readthedocs.io/en/latest/)
-* t-SNE
-    * [t-distributed Stochastic Neighbor Embedding (t-SNE)](https://lvdmaaten.github.io/tsne/)
-    * [t-SNE Python implementation](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)
 
 ### Usage
 
@@ -363,17 +360,6 @@ By default, all measurements are selected in the box.
   representation. This parameter controls how tightly UMAP is allowed to pack points together.
   Further reading: [Minimum Distance](https://umap-learn.readthedocs.io/en/latest/parameters.html#min-dist).
 
-#### t-SNE Parameters
-
-* Perplexity: The perplexity is related to the number of nearest neighbors that are used in other manifold learning
-  algorithms. Larger datasets usually require a larger perplexity. The recommended range is between 5 and 50.
-  Further
-  reading: [Perplexity](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE).
-* Maximum number of iterations: The maximum number of iterations for the optimization. The default is 1000. More
-  iterations will give more accurate results, but will also take longer to compute.
-  Further
-  reading: [Maximum Number of Iterations](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE).
-
 When you are done with the selection, click on `Compute`.
 The resulting values will be added as additional columns to the selected table.