This is a public repository for the following study available here:
Marco Salas et al. Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows (bioRxiv), 2023.
Abstract
Xenium is a new spatial transcriptomics product commercialized by 10X Genomics capable of generating maps of hundreds of transcripts in situ at a subcellular resolution. Herein, we explore 5 Xenium datasets of the mouse brain and 2 of human breast cancer by comparing scalability, resolution, data quality, capacities and limitations, with other spatially resolved transcriptomics (SRT) technologies. In addition, we benchmarked the performance of multiple open source computational tools when applied to Xenium datasets in tasks including cell segmentation, segmentation-free analysis, selection of spatially variable genes and domain identification, among others. To our knowledge, this study serves as the first independent analysis of the performance of Xenium.
The Xenium datasets used in this analysis were provided by 10X Genomics. The three mouse brain coronal sections (“ms brain multisection”) are publically available datasets and can be downloaded at from the 10X website (https://www.10xgenomics.com/xenium-preview-data , https://www.10xgenomics.com/resources/datasets). The mouse brain full coronal and half coronal sections (named as “ms brain coronal” and “ms brain ROI” in Figure 1B), as well as the human breast sections are available upon request to 10X Genomics.
In a clean conda environment with pip installed, run in the terminal:
git clone https://github.com/Moldia/Xenium_benchmarking.git
Navigate to the folder:
cd Xenium_benchmarking
And install using pip:
pip install -e .