Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling
Journal:
arXiv
Published Date:
May 15, 2025
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 adaptively capture
cross-slice deformations and enhance positional correlations between ST slices,
2) a pyramid gene co-expression correlation module to capture multi-scale
biological associations among genes, and 3) a cross-modal alignment module that
integrates the ST-paired hematoxylin and eosin (H&E)-stained images to filter
and align the essential cellular features across ST and H\&E images. Extensive
experiments on the public dataset demonstrate our superiority over
state-of-the-art approaches on both single-slice and multi-slice ST
interpolation. Codes are available at
https://github.com/XiaofeiWang2018/C2-STi.
