Coexpression uncovers a unified single-cell transcriptomic landscape

Hie, B.; Cho, H.; Bryson, B.; Berger, B.

doi:10.1101/719088

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bioRxiv · 10.1101/719088

Coexpression uncovers a unified single-cell transcriptomic landscape

Hie, B.·Cho, H.·Bryson, B.·Berger, B.

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Abstract

Single-cell transcriptomic studies of diverse and complex systems are becoming ubiquitous. Algorithms now attempt to integrate patterns across these studies by removing all study-specific information, without distinguishing unwanted technical bias from relevant biological variation. Integration remains difficult when capturing biological variation that is distributed across studies, as when combining disparate temporal snapshots into a panoramic, multi-study trajectory of cellular development. Here, we show that a fundamental analytic shift to gene coexpression within clusters of cells, rather than gene expression within individual cells, balances robustness to bias with preservation of meaningful inter-study differences. We leverage this insight in Trajectorama, an algorithm which we use to unify trajectories of neuronal development and hematopoiesis across studies that each profile separate developmental stages, a highly challenging task for existing methods. Trajectorama also reveals systems-level processes relevant to disease pathogenesis within the microglial response to myelin injury. Trajectorama benefits from efficiency and scalability, processing nearly one million cells in around an hour.

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Hie, B., Cho, H., Bryson, B., Berger, B.. 2019-07-30. Coexpression uncovers a unified single-cell transcriptomic landscape. https://doi.org/10.1101/719088

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