High-dimensional datasets are often squeezed through dimensionality reduction algorithms like PCA, UMAP, T-SNE or others to visualize them on a 2D scatterplot. These procedures let us expose heterogeneity in datasets. However, we would like to know the source of this heterogeneity as well. This is where Buzz can help.

Buzz is an interactive tool that lets you explore low dimensional embeddings of your dataset by:

• coloring it according to features you provide,
• and selecting populations between which you can compare feature distributions.

See USAGE for instructions on how to use Buzz.

Brushing Select groups of points and compare their feature distributions using histograms.

Coloring Easily color your scatterplot according to feature values.

WebGL Visualize embeddings of 10,000s of datapoints thanks to GPU acceleration.

lightweight Buzz is entirely written in JavaScript making it a snappy and lightweight tool.

Arquero Compatible with any language thanks to Arquero and Apache Arrow. Just export your dataset to an Arrow-file and import in Buzz. See USAGE for more info on how to import your dataset.

Buzz uses a number of open-source projects for its functionalities.

D3.js The feature histograms are plotted using D3.js.

D3FC This is a wrapper library around D3.js. In this project we use d3fc-webgl, which is a component of D3FC that makes it easy to create scatterplots in WebGL.

Brushing is also possible thanks to D3FC.

Alpine.js Application controls are implemented with Alpine.js. It is a lightweight framework with easy DOM manipulation, allowing for fast development of interactive applications.

TailwindCSS A utility-first CSS framework that lets you easily add style to HTML documents.

NW.js This library lets you package JavaScript apps into cross-platform, standalone desktop apps with access to native system APIs. In the near future, Buzz will be available as a NW.js desktop app.

Arquero This is a fast JavaScript library for loading array-backed data tables. Buzz is being rewritten to use Arquero under the hood for faster data processing, and to allow Apache Arrow input formats.

Buzz is in beta-phase. The initial features and aim of the app are laid out, and a first functional version of the app is implemented.

In the long term, Buzz functionality needs to be expanded. However, the focus should always remain on the exploration of low-dimensional embeddings on a local machine.

The aim is not, for instance, to implement dimensionality reduction techniques within Buzz. It is also not the goal to the make Buzz part of data processing pipelines run on HPC clusters. Other methods are more suitable for such goals.

Good expansions could be to allow Buzz to automatically suggest the most interesting features when comparing populations, or to let users export a report about their embedding.

Buzz could also be integrated in a Napari plugin to allow for exploration of imaging datasets. Clicking on a point in the scatterplot could then open the corresponding image in Napari, letting the user go back to the original data.