Rxivist logo

Segmentation-free inference of cell-types from in situ transcriptomics data

By Jeongbin Park, Wonyl Choi, Sebastian Tiesmeyer, Brian Long, Lars E. Borm, Emma Garren, Thuc Nghi Nguyen, Simone Codeluppi, Matthias Schlesner, Bosiljka Tasic, Roland Eils, Naveed Ishaque

Posted 13 Oct 2019
bioRxiv DOI: 10.1101/800748

Multiplexed fluorescence in situ hybridization techniques have enabled cell class or type identification by mRNA quantification in situ. However, inaccurate cell segmentation can result in incomplete cell-type and tissue characterization. Here, we present a robust segmentation-free computational framework, applicable to a variety of in situ transcriptomics platforms, called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM). SSAM assumes that spatial distribution of mRNAs relates to organization of higher complexity structures (e.g. cells or tissue layers) and performs de novo cell-type and tissue domain identification. Optionally, SSAM can also integrate prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. SSAM outperforms segmentation-based results, demonstrating that segmentation of cells is not required for inferring cell-type signatures, cell-type organization or tissue domains.

Download data

  • Downloaded 1,549 times
  • Download rankings, all-time:
    • Site-wide: 6,027 out of 100,263
    • In bioinformatics: 1,061 out of 9,215
  • Year to date:
    • Site-wide: 4,059 out of 100,263
  • Since beginning of last month:
    • Site-wide: None out of 100,263

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News

  • 20 Oct 2020: Support for sorting preprints using Twitter activity has been removed, at least temporarily, until a new source of social media activity data becomes available.
  • 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
  • 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
  • 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
  • 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
  • 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
  • 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
  • 22 Jan 2019: Nature just published an article about Rxivist and our data.
  • 13 Jan 2019: The Rxivist preprint is live!