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

Area of Science:

• Computational biology
• Bioinformatics
• Genomics

Background:

• Spatial transcriptomics integrates gene expression, spatial coordinates, and histology.
• Existing methods struggle with semantic conflicts from distorted views, leading to inaccurate representations.
• Technical constraints in data acquisition and processing introduce these distortions.

Purpose of the Study:

• To introduce Holistic Invariant RetrAcing for mitigating representation distortion (HiraST).
• To address semantic conflicts and improve consensus representations in spatial transcriptomics.
• To enhance the robustness and accuracy of multi-modal data analysis.

Main Methods:

• HiraST employs cross-view invariant retracing to align instance-level features and pseudo-label distributions.
• It jointly aligns features and labels to retrace invariant information across modalities.
• Holistic prototype learning uses low-frequency components to recalibrate corrupted views and reduce noise.

Main Results:

• HiraST demonstrates state-of-the-art performance on spatial transcriptomics datasets.
• The framework achieves strong results on incomplete multi-view clustering benchmarks.
• HiraST shows significant capability across various downstream tasks, validating its effectiveness.

Conclusions:

• HiraST effectively corrects distorted multi-view representations in spatial transcriptomics.
• The proposed dual alignment and prototype learning mechanisms enhance data consistency and robustness.
• HiraST offers a robust solution for accurate analysis of spatial transcriptomics data and related tasks.