INST-Align: Implicit Neural Alignment for Spatial Transcriptomics via Canonical Expression Fields
For computational biology researchers analyzing spatial transcriptomics data, INST-Align provides a method that simultaneously aligns and integrates multiple tissue slices, enabling coherent 3D reconstruction and biologically meaningful embeddings.
INST-Align addresses multi-slice spatial transcriptomics alignment by jointly handling non-rigid deformations and batch effects via a shared Canonical Expression Field, achieving state-of-the-art mean OT Accuracy (0.702), NN Accuracy (0.719), and up to 94.9% Chamfer reduction on large-deformation sections.
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.