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Optimizing the design of spatial genomic studies

View ORCID ProfileAndrew Jones, Diana Cai, View ORCID ProfileDidong Li, View ORCID ProfileBarbara E. Engelhardt
doi: https://doi.org/10.1101/2023.01.29.526115
Andrew Jones
1Department of Computer Science, Princeton University
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Diana Cai
1Department of Computer Science, Princeton University
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Didong Li
2Department of Biostatistics, University of North Carolina at Chapel Hill
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Barbara E. Engelhardt
3Gladstone Institutes
4Department of Biomedical Data Science, Stanford University
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  • For correspondence: barbarae@stanford.edu
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Abstract

Spatially-resolved genomic technologies have shown promise for studying the relationship between the structural arrangement of cells and their functional behavior. While numerous sequencing and imaging platforms exist for performing spatial transcriptomics and spatial proteomics profiling, these experiments remain expensive and labor-intensive. Thus, when performing spatial genomics experiments using multiple tissue slices, there is a need to select the tissue cross sections that will be maximally informative for the purposes of the experiment. In this work, we formalize the problem of experimental design for spatial genomics experiments, which we generalize into a problem class that we call structured batch experimental design. We propose approaches for optimizing these designs in two types of spatial genomics studies: one in which the goal is to construct a spatially-resolved genomic atlas of a tissue and another in which the goal is to localize a region of interest in a tissue, such as a tumor. We demonstrate the utility of these optimal designs, where each slice is a two-dimensional plane, on several spatial genomics datasets.

Competing Interest Statement

BEE is on the SAB of Creyon Bio, Arrepath, and Freenome. BEE is a consultant with Neumora and Cellarity.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted January 31, 2023.
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Optimizing the design of spatial genomic studies
Andrew Jones, Diana Cai, Didong Li, Barbara E. Engelhardt
bioRxiv 2023.01.29.526115; doi: https://doi.org/10.1101/2023.01.29.526115
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Optimizing the design of spatial genomic studies
Andrew Jones, Diana Cai, Didong Li, Barbara E. Engelhardt
bioRxiv 2023.01.29.526115; doi: https://doi.org/10.1101/2023.01.29.526115

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