Published on Wed Jul 14 2021

Statistical Transfer Learning with Generative Encoding for Spatial Transcriptomics

Banh, D.

The technique can transfer single cells to unmeasured histology tissue. It can learn from the alignment to predict gene expression on new histology.

2
2
5
Abstract

A new method proposes to align single cell reference datasets to spatial tran- scriptomic genes and tissue images. The technique can transfer single cells to unmeasured histology tissue by first aligning a single cell reference dataset to known Spatial Transcriptomic tissue, and learn from the alignment to predict gene expression on new histology. The model can invert the alignment transformation to generate new histology images from gene expression vectors, allowing for in-silico perturbation analyses through dynamically altering the levels of gene expression. Leveraging the cell atlas can lead to annotation of pathology and clinical specimens, enabling a mapping from the cellular and transcriptomic level to imaging tissue.