Accurate Spatial Gene Expression Prediction by integrating
  Multi-resolution features
Youngmin Chung Ji Hun Ha Kyeong Chan Im Joo Sang Lee
Recent advancements in Spatial Transcriptomics (ST) technology have
facilitated detailed gene expression analysis within tissue contexts. However,
the high costs and methodological limitations of ST necessitate a more robust
predictive model. In response, this paper introduces TRIPLEX, a novel deep
learning framework designed to predict spatial gene expression from Whole Slide
Images (WSIs). TRIPLEX uniquely harnesses multi-resolution features, capturing
cellular morphology at individual spots, the local context around these spots,
and the global tissue organization. By integrating these features through an
effective fusion strategy, TRIPLEX achieves accurate gene expression
prediction. Our comprehensive benchmark study, conducted on three public ST
datasets and supplemented with Visium data from 10X Genomics, demonstrates that
TRIPLEX outperforms current state-of-the-art models in Mean Squared Error
(MSE), Mean Absolute Error (MAE), and Pearson Correlation Coefficient (PCC).
The model's predictions align closely with ground truth gene expression
profiles and tumor annotations, underscoring TRIPLEX's potential in advancing
cancer diagnosis and treatment.