Latent Space Inference For Spatial Transcriptomics
J. Ding S. N. Zaman P. Y. Chen D. Wang
In order to understand the complexities of cellular biology, researchers are
interested in two important metrics: the genetic expression information of
cells and their spatial coordinates within a tissue sample. However,
state-of-the art methods, namely single-cell RNA sequencing and image based
spatial transcriptomics can only recover a subset of this information, either
full genetic expression with loss of spatial information, or spatial
information with loss of resolution in sequencing data. In this project, we
investigate a probabilistic machine learning method to obtain the full genetic
expression information for tissues samples while also preserving their spatial
coordinates. This is done through mapping both datasets to a joint latent space
representation with the use of variational machine learning methods. From here,
the full genetic and spatial information can be decoded and to give us greater
insights on the understanding of cellular processes and pathways.