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SPArrOW: a flexible, interactive and scalable pipeline for spatial transcriptomics analysis

Pollaris, L.; Vanneste, B.; Rombaut, B.; Defauw, A.; Vernaillen, F.; Mortier, J.; Vanhenden, W.; Thone, T.; Martens, L.; Hastir, J.-F.; Bujko, A.; Saelens, W.; Marine, J.-C.; Nelissen, H.; Van Hamme, E.; Seurinck, R.; Scott, C. L.; Guilliams, M.; Saeys, Y.

2024-07-06 bioinformatics
10.1101/2024.07.04.601829 bioRxiv
Show abstract

Current spatial transcriptomics technologies are increasingly able to measure large gene panels at subcellular resolution, but a major bottleneck in this rapidly advancing field is the computational analysis and interpretation of the data. To bridge this gap, here we present SPArrOW, a flexible, modular and scalable pipeline for processing spatial transcriptomics data. SPArrOW improves cell segmentation and leads to better overall data quality, resulting in more accurate cell annotations at the single-cell level. Furthermore, it provides the users with numerous visual quality checks that are crucial for the correct interpretation of the data, offering users more control in processing their data. Our workflow is designed to accommodate the various available spatial transcriptomics platforms. Finally, SPArrOW offers interactive visualization and data exploration, enabling sample-specific pipeline optimization by various tuneable parameters and an efficient comparison of different staining and gene allocation strategies.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.