Spatial experimental design

New preprint on optimizing the design of spatial genomics studies! This represents the hard work of Andy Jones, Diana Cai, and Didong Li. When gathering spatial sequencing data from a tissue, typically parallel axis-aligned slices are selected, but these slices may contain redundant information. Can we make spatial genomics experiments more cost-efficient by profiling tissue slices that are maximally informative?

We propose a Bayesian optimal experimental design method for two separate spatial genomics contexts: building atlases of tissues/organs and identifying the boundaries of regions of interest---such as tumors---in tissue samples.

For a given genomic assay, our method iteratively selects the tissue slice that maximizes the expected information gain, i.e., the slice that is most likely to provide the greatest amount of new information on top of the data already collected.

To demonstrate our approach in the atlas-building context, we used the Allen Brain Atlas’s spatial gene expression data from mouse brain. We found that our sequential approach selects slices with more information in fewer slices compared to traditional serial slicing strategies.

We also applied our model to an experimental setting in which a tumor is being localized. Applied to Visium data from a prostate cancer sample, we find that our approach is able to achieve near-optimal bounding with four slices, as opposed to serial approaches, which take eight.

Feedback welcome! Try out our approach – we hope it will design an efficient and informative experiment for you!