bioRxivBioinformaticsPreprint
Identification of perturbation-responsive regions and genes in comparative spatial transcriptomics atlases
Alan Yue Yang TeoiD, Matthieu GautieriD, Laurent Pierre BrockiD, Jennifer TsaiiD, Alexandra de CoucyiD, Achilleas LaskaratosiD, Nicola RegazziiD, Quentin BarraudiD, Michael V. SofroniewiD, Mark A. AndersoniD, Grégoire CourtineiD, Jordan W. SquairiD, Michael A. SkinnideriD
Abstract
We introduce Vespucci, a machine-learning method to identify perturbation-responsive regions, genes and gene programs within comparative spatial transcriptomics atlases. We validate Vespucci on simulated and published datasets and show that it outperforms 19 published computational methods for spatial transcriptomics. We apply Vespucci to expose the spatial organization of gene programs activated by therapies that guide repair of the injured spinal cord.
- assay: spatial transcriptomics
- research focus: gene regulatory network inference
- research focus: perturbation response prediction
- approach: machine learning (classical)
- Citations
- 3
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