Benjamin Rombaut
Computer science engineer with a passion for interactive analysis of multidimensional bioimaging and hackathons.
Sessions
In order to use the best performing methods for each step of the single-cell analysis process, bioinformaticians need to use multiple ecosystems and programming languages. This is unfortunately not that straightforward. We will give an overview of the different levels of interoperability, and how it is possible to integrate them in a single workflow.
For package developers, making methods accessible is important. We will provide information on how to do this well on the package and method level.
Recent automated bioimaging platforms generate complex and high-throughput spatial omics data. Especially whole-slide images with dozens to hundreds of fluorescent protein image channels can be difficult to process and analyze at the single-cell level.
We present an analysis workflow with three key components: (1) secure dataset management using remote Big Data object storage and SpatialData, (2) scalable cell segmentation and feature calculation utilizing cellpose, Dask and high-performance computing, and (3) interactive expert-in-the-loop hyperparameter tuning and cell annotation using FlowSOM, napari and Jupyter notebooks. Feedback on data quality is provided through various plots, offering insights at each stage of the workflow such as overview of channel quality and comparisons of different segmentation methods.
Overall, this powerful yet user-friendly workflow empowers users to efficiently analyze large volumes of imaging data, significantly reducing the time and effort required to go from microscope to meaningful quantitative insights.