@article {67871,
	title = {Image-based representation of massive spatial transcriptomics datasets.},
	journal = {bioRxiv},
	year = {2021},
	month = {2021 Dec 07},
	abstract = {<p>We present STIM, an imaging-based computational framework for exploring, visualizing, and processing high-throughput spatial sequencing datasets. STIM is built on the powerful ImgLib2, N5 and BigDataViewer (BDV) frameworks enabling transfer of computer vision techniques to datasets with irregular measurement-spacing and arbitrary spatial resolution, such as spatial transcriptomics data generated by multiplexed targeted hybridization or spatial sequencing technologies. We illustrate STIM{\textquoteright}s capabilities by representing, visualizing, and automatically registering publicly available spatial sequencing data from 14 serial sections of mouse brain tissue.</p>
},
	doi = {10.1101/2021.12.07.471629},
	url = {https://doi.org/10.1101/2021.12.07.471629},
	author = {Stephan Preibisch and Nikos Karaiskos and Nikolaus Rajewsky}
}
