INST-Align: Implicit Neural Alignment for Spatial Transcriptomics via Canonical Expression Fields

• URL: http://arxiv.org/abs/2604.12084v1
• Date: Mon, 13 Apr 2026 21:44:18 GMT
• Title: INST-Align: Implicit Neural Alignment for Spatial Transcriptomics via Canonical Expression Fields
• Abstract summary: INST-Align couples a coordinate-based deformation network with a shared Canonical Expression Field.<n>It achieves state-of-the-art mean OT Accuracy (0.702), NN Accuracy (0.719), and Chamfer distance, with Chamfer reductions of up to 94.9% on large-deformation sections.<n>It also yields biologically meaningful spatial embeddings and coherent 3D tissue reconstruction.
• Score: 1.0487944945684993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial transcriptomics (ST) measures mRNA expression while preserving spatial organization, but multi-slice analysis faces two coupled difficulties: large non-rigid deformations across slices and inter-slice batch effects when alignment and integration are treated independently. We present INST-Align, an unsupervised pairwise framework that couples a coordinate-based deformation network with a shared Canonical Expression Field, an implicit neural representation mapping spatial coordinates to expression embeddings, for joint alignment and reconstruction. A two-phase training strategy first establishes a stable canonical embedding space and then jointly optimizes deformation and spatial-feature matching, enabling mutually constrained alignment and representation learning. Cross-slice parameter sharing of the canonical field regularizes ambiguous correspondences and absorbs batch variation. Across nine datasets, INST-Align achieves state-of-the-art mean OT Accuracy (0.702), NN Accuracy (0.719), and Chamfer distance, with Chamfer reductions of up to 94.9\% on large-deformation sections relative to the strongest baseline. The framework also yields biologically meaningful spatial embeddings and coherent 3D tissue reconstruction. The code will be released after review phase.

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