Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling
NingFeng Que, Xiaofei Wang, Jingjing Chen, Yixuan Jiang, Chao Li

TL;DR
This paper introduces C2-STi, a novel method for interpolating missing spatial transcriptomics slices using cross-modal and cross-slice modeling, improving 3D tissue analysis accuracy.
Contribution
The paper presents the first approach for interpolating missing ST slices at arbitrary positions by integrating structural, gene co-expression, and image alignment modules.
Findings
Outperforms state-of-the-art methods on public datasets.
Effectively captures tissue deformations and gene correlations.
Enhances 3D spatial gene profiling accuracy.
Abstract
Spatial transcriptomics (ST) is a promising technique that characterizes the spatial gene profiling patterns within the tissue context. Comprehensive ST analysis depends on consecutive slices for 3D spatial insights, whereas the missing intermediate tissue sections and high costs limit the practical feasibility of generating multi-slice ST. In this paper, we propose C2-STi, the first attempt for interpolating missing ST slices at arbitrary intermediate positions between adjacent ST slices. Despite intuitive, effective ST interpolation presents significant challenges, including 1) limited continuity across heterogeneous tissue sections, 2) complex intrinsic correlation across genes, and 3) intricate cellular structures and biological semantics within each tissue section. To mitigate these challenges, in C2-STi, we design 1) a distance-aware local structural modulation module to…
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Taxonomy
TopicsSingle-cell and spatial transcriptomics · Cell Image Analysis Techniques · Gene expression and cancer classification
MethodsALIGN
