Sparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning

TL;DR

S2S-ST introduces a single-shot, self-supervised framework that combines natural image co-learning and iterative refinement to accurately impute high-resolution spatial transcriptomics data from sparse samples, reducing costs.

Contribution

The paper proposes a novel sparser-to-sparse self-supervised learning approach with cross-domain co-learning and a cascaded network for improved ST imputation from minimal data.

Findings

Outperforms state-of-the-art in diverse tissue types

Reduces reliance on costly high-resolution data

Effective in reconstructing detailed spatial gene expression

Abstract

Spatial transcriptomics (ST) has revolutionized biomedical research by enabling high resolution gene expression profiling within tissues. However, the high cost and scarcity of high resolution ST data remain significant challenges. We present Single-shot Sparser-to-Sparse (S2S-ST), a novel framework for accurate ST imputation that requires only a single and low-cost sparsely sampled ST dataset alongside widely available natural images for co-training. Our approach integrates three key innovations: (1) a sparser-to-sparse self-supervised learning strategy that leverages intrinsic spatial patterns in ST data, (2) cross-domain co-learning with natural images to enhance feature representation, and (3) a Cascaded Data Consistent Imputation Network (CDCIN) that iteratively refines predictions while preserving sampled gene data fidelity. Extensive experiments on diverse tissue types, including breast cancer, liver, and lymphoid tissue, demonstrate that our method outperforms state-of-the-art approaches in imputation accuracy. By enabling robust ST reconstruction from sparse inputs, our framework significantly reduces reliance on costly high resolution data, facilitating potential broader adoption in biomedical research and clinical applications.