Latent Space Inference For Spatial Transcriptomics
This addresses a bottleneck in cellular biology research by enabling more comprehensive analysis of tissue samples, though it appears incremental as it builds on existing variational methods.
The paper tackled the problem of integrating single-cell RNA sequencing and spatial transcriptomics data to obtain full genetic expression with spatial coordinates, using a probabilistic machine learning method to map both datasets to a joint latent space representation.
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.