
Dr. Carolina Wählby
Professor of Quantitative Microscopy
Uppsala University

Dr. Carolina Wählby is a professor in quantitative microscopy at the Dept. of Information Technology at the Uppsala University in Uppsala, Sweden and at the SciLifeLab of Sweden. Carolina’s research group is focused on developing computational image analysis approaches based on AI and deep learning for extracting information from microscopy images. The images her lab typically uses come from experiments aimed to understand biology or diagnose disease and projects in her lab range from large-scale cell-based screens for drug development to in situ RNA sequencing. You can see more at her lab web page.
TissUUmaps 3: Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics data
September 19, 2023
Virtual
Spatially resolved techniques for exploring the molecular landscape of tissue samples, such as spatial transcriptomics, often result in millions of data points and images too large to view on a regular desktop computer, limiting the possibilities in visual interactive data exploration. TissUUmaps is a free, open-source browser-based tool for GPU-accelerated visualization and interactive exploration of 107+ data points overlaying tissue samples. TissUUmaps 3 provides instant multiresolution image viewing and can be customized, shared, and also integrated into Jupyter Notebooks. TissUUmaps introduces new modules where users can visualize markers and regions, explore spatial statistics, perform quantitative analyses of tissue morphology, and assess the quality of decoding in situ transcriptomics data. Thanks to targeted optimizations the time and cost associated with interactive data exploration were reduced, TissUUmaps 3 is enabled to handle the scale of today's spatial transcriptomics methods. TissUUmaps 3 provides significantly improved performance for large multiplex datasets as compared to previous versions and we envision TissUUmaps to contribute to broader dissemination and flexible sharing of large scale spatial omics data. https://www.sciencedirect.com/science/article/pii/S2405844023025136?via%3Dihub