09-11, 17:00–18:00 (Europe/Berlin), Workshop room I - MW 0337
Biological data visualization is challenged by the growing complexity and size of datasets. While single-plot visualization methods struggle to capture the full picture of datasets, researchers turn to composable visualizations that are usually specialized to a domain requiring familiarity with multiple visualization tools. To unify the creation of composable visualization, we introduce a novel and intuitive general visualization paradigm termed "cross-layout,” which integrates multiple plot types in a cross-like structure. This paradigm allows for a central main plot surrounded by secondary plots, each capable of layering additional features for enhanced context and understanding. To operationalize this paradigm, we present "Marsilea", a Python library designed for creating composable visualizations with ease. Marsilea is notable for its modularity, diverse plot types, and compatibility with various data formats. This talk will bring attendees insights into composable visualizations, and they will learn how to use Marsilea to express different aspects of their single-cell or spatial omics data into a composable visualization. Marsilea is accessible to everyone with basic knowledge of Python, open-sourced at https://github.com/Marsilea-viz/marsilea.
In this talk, we will learn to use Marsilea, a newly developed Python package to transform your omics data into composable visualization.
Agenda:
1. Introduction of “cross-layout” and Marsilea
The talk will start by discussing the limitations of traditional single-plot methods in visualizing complex scientific data and highlighting the advantages of composable visualization techniques. Next, we will provide an in-depth overview of Marsilea by introducing the concept of “cross-layout” paradigm to create the composable visualization. After that, we will see how “cross-layout” is applied in Marsilea. We will then talk about the design and advantages of Marsilea over other plotting libraries. A minimum example will be used to illustrate how a composable visualization can be built with Marsilea incrementally in a declarative way.
2. Interactive Hands-on Session:
We like to engage attendees to recreate three composable visualizations in single-cell omics with provided datasets and Jupyter Notebook in a preconfigured environment (etc, Google Colab). They will get hands-on creating a complex heatmap visualizing the expression profile of single cells, a dot heatmap to visualize the cell-cell communications, and a track plot to visualize ATAC-seq data. When attendees are familiar with the usage of Marsilea, they will be asked to use a provided dataset and maximize their creativity to build a visualization using Marsilea. All masterpieces will be showcased on the Marsilea website.
3. Conclusion and Wrap-Up:
In the end, The talk will be wrapped up with a cheat sheet to review the “cross-layout” concept and Marsilea’s APIs, and more examples of the broad applications of Marsilea beyond omics data will be showcased.
Previous knowledge expected
I'm a Postdoctoral Fellow at CeMM, Vienna, my current research focus is on digital pathology and spatial omics. I'm an open-sourced software developer in my free time and a big fan of scientific visualization.