Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics

TL;DR

This paper introduces STRank, a new loss function for estimating gene expression from pathology images that focuses on relative expression patterns, making predictions more robust to noise and batch effects.

Contribution

The paper proposes a novel loss function called STRank that learns relative gene expression patterns, improving robustness over traditional absolute value estimation methods.

Findings

STRank outperforms existing methods on synthetic datasets.

The approach effectively mitigates batch effects and noise.

Experimental results validate the robustness of the proposed method.

Abstract

Gene expression estimation from pathology images has the potential to reduce the RNA sequencing cost. Point-wise loss functions have been widely used to minimize the discrepancy between predicted and absolute gene expression values. However, due to the complexity of the sequencing techniques and intrinsic variability across cells, the observed gene expression contains stochastic noise and batch effects, and estimating the absolute expression values accurately remains a significant challenge. To mitigate this, we propose a novel objective of learning relative expression patterns rather than absolute levels. We assume that the relative expression levels of genes exhibit consistent patterns across independent experiments, even when absolute expression values are affected by batch effects and stochastic noise in tissue samples. Based on the assumption, we model the relation and propose a novel loss function called STRank that is robust to noise and batch effects. Experiments using synthetic datasets and real datasets demonstrate the effectiveness of the proposed method. The code is available at https://github.com/naivete5656/STRank.