bioRxivBioinformaticsPreprint
Fault-tolerant 3D reconstruction from 2D spatial proteomics sections
Zhaojun ZhangiD, Yuqi TaniD, Michael P. SnyderiD, Garry P. NolaniD, Zongming MaiD
Abstract
Reconstructing 3D molecular volumes from sparsely sampled 2D tissue sections is limited by per-section marker dropout and tissue loss. We present 3D-Omics-Flow, a generative pipeline that jointly repairs damaged sections and interpolates between them at single-cell resolution. Across datasets spanning health and disease, 3D-Omics-Flow expands 3D spatial proteomics to practical sampling regimes, enabling atlas construction and downstream analysis from imperfect 2D section stacks.
- assay: spatial transcriptomics
- assay: proteomics
- research focus: dimensionality reduction
- research focus: spatial domain detection
- research focus: imputation
- approach: deep learning
- approach: statistical model
- Citations
- 0
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