Dimensionality reduction techniques are often used to visualize the underlying geometry of a high-dimensional dataset. These methods usually rely on specific similarity measures. In this project, we first approximate the geodesic distance using a diffusion process over the underlying manifold, then we use Multi-Dimentionnal Scaling combined with our previously defined pairwise 'distances' to embed our Manifold in a lower dimensional space. We compare our model with popular algorithms such as PHATE, UMAP, and Isomap on toy datasets and RNA-seq dataset.
The external python libraries needed are:
- umap-learn
- pyDiffMap
- seaborn
However, you can simply run the attached Notebook Jupiter that will download everything for you :)
Run the Notebook.