The recent advent of methods for multiplexing in situ transcriptomics (Lee et al., 2014; Lovatt et al., 2014; Lubeck et al., 2014) opens up the possibility of a highly detailed characterisation of tissue architecture based on gene expression. However, the sparse, high-dimensional nature of this data, combined with various technical sources of noise, necessitates the development of advanced statistical methods in order to reliably identify patterns of spatial variability.
In collaboration with researchers in the Church Lab, we are developing hierarchical Bayesian methods for detecting key patterns of spatial variability in gene expression levels, as well as identifying sets of spatially coordinated genes.
Lee, J. H., Daugharthy, E. R., et al. (2014). Highly multiplexed subcellular RNA sequencing in situ. Science, 343(6177), 1360-1363.
Lovatt, D., Ruble, B. K., et al. (2014). Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nature Methods, 11(2), 190-196.
Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M., & Cai, L. (2014). Single-cell in situ RNA profiling by sequential hybridization. Nature Methods, 11(4), 360-361.