Sleepwalk: a tool to interactive explore dimension-reduced embeddings
When working with single-cell data, e.g., from single-cell RNA-Seq, we need an intuitive visual representation of our data. To do so, dimensionality reduction approaches are useful, and dozens of these have been developed by now: PCA, Diffusion Map, t-SNE, UMAP and many others.
The aim of these dimension-reduced embeddings is simple: Each cell is represented by a point on two-dimensional plot, and the points are arranged such that similar cells appear close to each other and cells that are different farer apart.
To read more, got to the Sleepwalk github...
So, can you be sure that the visualisation you get by using t-SNE, UMAP, MDS or the like really give you a faithful representation of your data? Are the points that lie almost on top of each other really all similar? Does the large distance on your 2D representation always mean lots of dissimilarities? Our sleepwalk package for the R statistical programming environment can help you answer these questions.
Svetlana is a PhD student in the lab of Simon Anders at the ZMBH (Centre for Molecular Biology of University Heidelberg). A preprint of her work is available on BioRxiv: S. Ovchinnikova and S. Anders: Exploring dimension-reduced embeddings with Sleepwalk. BioRvix 603589 (2019). doi:10.1101/603589.