Image-based representation of massive spatial transcriptomics datasets.

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bioRxiv. 2021 Dec 07;. doi: 10.1101/2021.12.07.471629

Image-based representation of massive spatial transcriptomics datasets. Scientific Computing Software

Preibisch Stephan, Karaiskos Nikos, Rajewsky Nikolaus

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Abstract

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’s capabilities by representing, visualizing, and automatically registering publicly available spatial sequencing data from 14 serial sections of mouse brain tissue.

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Image-based representation of massive spatial transcriptomics datasets.

Abstract

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