Holistic Invariant Retracing for Distortion-Resilient Multi-Modal Learning in Spatial Transcriptomics

Spatial transcriptomics provides a multi-modal perspective by simultaneously capturing gene expression profiles, spatial coordinates, and histological images. While existing methods focus on maintaining view consistency to handle distribution shifts, they frequently neglect semantic conflicts introduced by distorted views-a common limitation arising from technical data acquisition and processing constraints. These conflicts lead to distorted consensus representations. To address this challenge, we propose Holistic Invariant RetrAcing for mitigating representation distortion (HiraST). Our framework explicitly corrects distorted multi-view representations through two complementary mechanisms: 1) Cross-view invariant retracing, which jointly aligns instance-level features and pseudo-label distributions to retrace invariant information. This dual alignment ensures that semantically similar cells or tissue regions remain consistent across heterogeneous modalities, even in the presence of acquisition-induced distortions; and 2) holistic prototype learning, which leverages low-frequency structural components to recalibrate corrupted views and enhance robustness against noise. Extensive experiments on spatial transcriptomics datasets and incomplete multi-view clustering benchmarks demonstrate our framework's state-of-the-art performance. Meanwhile, HiraST demonstrates strong capability across various downstream tasks. The demo code of this work is publicly available at https://github.com/hexiao0275/HiraST.