Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images.

ABSTRACT: Spatial transcriptomics technologies enable high-throughput quantification of gene expression at specific locations across tissue sections, facilitating insights into the spatial organization of biological processes. However, high costs associated with these technologies have motivated the development of deep learning methods to predict spatial gene expression from inexpensive hematoxylin and eosin-stained histology images. While most efforts have focused on modifying model architectures to boost predictive performance, the influence of training data quality remains largely unexplored. Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based

SUBMITTER: Hallinan C 

PROVIDER: S-EPMC12439975 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature