All You Need is Color: Image based Spatial Gene Expression Prediction using Neural Stain Learning
This work addresses the challenge of predicting gene expression from histology images for cancer research, offering a more efficient and accurate approach compared to deep learning methods, though it is incremental in its domain-specific application.
The authors tackled the problem of predicting spatial gene expression from routine histology images by modeling stain absorption characteristics, and their Neural Stain Learning method outperformed existing models with only 11 trainable parameters, showing higher correlations with true expression values for more genes.
"Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption characteristics?" In this work, we propose a "stain-aware" machine learning approach for prediction of spatial transcriptomic gene expression profiles using digital pathology image of a routine Hematoxylin & Eosin (H&E) histology section. Unlike recent deep learning methods which are used for gene expression prediction, our proposed approach termed Neural Stain Learning (NSL) explicitly models the association of stain absorption characteristics of the tissue with gene expression patterns in spatial transcriptomics by learning a problem-specific stain deconvolution matrix in an end-to-end manner. The proposed method with only 11 trainable weight parameters outperforms both classical regression models with cellular composition and morphological features as well as deep learning methods. We have found that the gene expression predictions from the proposed approach show higher correlations with true expression values obtained through sequencing for a larger set of genes in comparison to other approaches.