bioRxivBioinformaticsPreprint
Fault-tolerant 3D reconstruction from 2D spatial proteomics sections
Zhaojun ZhangiD, Yuqi TaniD, Michael P. SnyderiD, Garry P. NolaniD, Zongming MaiD
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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
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