This repository contains the code for the paper Customizable and interactive visualizations for investigating spatio-temporal single-cell information by Seiffarth et al.. The paper introduces the construction of highly customizable and interactive visualizations for single-cell information in live-cell imaging. Interconnected visualization components provide an in-depth view into live-cell experiments at different information layers and level of detail. We utilize the html visualization capabilities of plotly and dash libraries to implement three customizable visuailzations in python jupyter notebooks. These three visualizations notebooks serve as templates to gain insights in future live-cell experiments.
Have a look for yourself, execute the notebooks and dive into the joy of interactive and interconnected visualizations. We provide the three notebook examples, they can be execute locally or in colab and Binder without a single installation instruction. Just click at the respective badges.
The lineage view combines lineage tree, microscope image, single-cell area development and cell trajectory video components into a single and interconnected visualization.
The descendants view allows to explore the progeny (offspring) of individual cells using overlay visualizations on microscope images and interconnects the information with the temporal development of selected population sizes (area & and count).
The distribution view allows to filter large amounts of single-cell data for rare or extreme events of cell size, cell lenght and cell age. These filters are combined with the microscopy images and the temporal visualization of the filtered cell amounts.
You can use the buttons (open in binder) to launch the visualization notebooks in a binder environment.
You can use the buttons Open in colab to launch the visualization notebooks in a colab environment.
Note: Rendered videos cannot be displayed in Colab.
You can also run the interactive data visualizations in your local jupyter notebook lab. For this, please install the conda environment and start the jupyter server using the following command:
conda env create -f environment.yml
conda activate viz-lab
jupyter labNote: Rendered videos CAN be displayed in the local jupyter lab instance.