A new version of Jupyter Scatter is out: v0.21.0

A new version of Jupyter Scatter is out: v0.21.0. 🥳 This version introduces fun new ways to select points using either a brush or rectangle. You can now also more easily extend or reduce a selection by holding down the meta (CMD on macOS) key or alt key respectively.

The brush selection can be useful when working with temporal or sequential patterns. Huge kudos to Andres Colubri who brought up that idea and the initial implementation for regl-scatterplot (Jupyter Scatter’s rendering engine).

To test the new brush selection mode, I integrated Jupyter Scatter with a novel algorithm called DimBridge by Mingwei Li and Remco Chang et al. DimBridge aspires to identify key dimensions in the high-dimensional space that explain a subset of points in the lower-dimensional embedding. For instance, imagine working with a 2D UMAP or t-SNE plot of a 100 dimensional dataset and you see an interesting pattern. The idea of DimBridge is to tell you which handful of dimensions from the dataset can explain the pattern you’re seeing in the embedding plot.

Using a single-cell surface protein dataset from Florian Mair et al. that we analyzed with Ozette’s Cell Discovery method (an extension of Evan Greene et al.’s FAUST method) and embedded with t-SNE, you can use Jupyter Scatter to select a cluster and have DimBridge compute the key protein expressions of that cluster. You can also contrast two or more selections to identify proteins that differentiate them. Or you can use the brush selection to study a sequence of selections in the dataset.

To test it out yourself, head over to https://github.com/flekschas/jupyter-scatter, clone the repo, cd into notebooks, and run juv run dimbridge.ipynb. This requires you to have juv installed, a super handy new tool from Trevor Manz that makes Jupyter Notebooks reproducible! I highly recommend you check it out if you haven’t.