We are excited to announce our latest work on spatial omics data analysis has just been published! In this paper, we developed a new method called ONTraC for studying the spatially continuous variations of tissue microenvironment (ME).

RT @yodai_takei: I'm happy to share a highlight from our recent work!.

RT @NatureRevGenet: New online! Spatial multi-omics of nuclear architecture with two-layer seqFISH+ .

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Great work by @wwang_94 @josschavezf1 , Shiwei Zheng, and Crystal Shin!.

If you are interested to use ONTraC in your own data analysis, please visit the project website: and/or GitHub:

The ONTraC framework is generally applicable because it only requires cell-type and spatial information as the input.

ONTraC enables the detection of ME mediated cell-state and cell-cell communication pattern variation within the same cell type.

We also showed that ONTraC can detect more refined spatially-associated variations than using discrete methods such as spatial domain analysis.

We showed that ONTraC outperforms traditional pseudotime analysis methods and correctly recapitulates the anatomic structure without using prior knowledge.

Unlike traditional trajectory analysis, ONTraC does not attempt to predict either temporal cell-state changes or lineage relationship but focuses on modeling the spatial landscape of tissue microenvironment changes independent of the cell lineage.

ONTraC treats a multi-cellular niche, rather than a single cell, as the basic unit of ME. The main output is a niche trajectory, which is a spatially continuous path that connects niches with similar cell-type composition properties.

Our lab is recruiting a new postdoc. If you have a strong machine learning/statistics background and are interested in developing methods for analyzing spatial omic data, this could be a good opportunity for you.

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