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Updated: Jul 1, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

組織構造を活用した時空間的細胞タイプデコンボリューション

Macrina Maria Lobo, Ziqi Zhang, Xiuwei Zhang

bioRxiv : the preprint server for biology

|February 23, 2026

Abstract:

Spot-based spatial transcriptomics (ST) captures aggregated transcriptomic profiles at spatial locations (spots) in tissue slices. Cell type deconvolution methods decode each spot and estimate the proportion of every cell type in the spot, necessary for uncovering spatial cell type distributions for further downstream analyses. Existing methods utilize cell type markers or reference transcriptomic (scRNA-seq) atlases at single cell (sc) resolution, or by aggregating profiles of identified cell types. However, current methods fail to effectively utilize the 3D tissue layout and single cell resolution reference. Some leverage 2D spatial organization assuming proximal spots are similar, which may be violated around boundaries or isolated cell types. We present SpaDecoder, a parallelized matrix factorization-based per-spot deconvolution method for multiple 3D spatial or temporal ST tissue slices effectively leveraging tissue structure with an adaptively inferred 3D neighborhood Gaussian kernel. We additionally account for variability in sc-reference profiles, along with batch effects. The mathematical framework of SpaDecoder allows it to be used for a range of downstream analyses. It can decode anteroposterior variability, impute gene expression, uncover putatively key tissue regions, identify colocalized cell types and predict spatio-temporal scRNA-seq cell locations. Ablation tests along with comparisons against other methods on various metrics, datasets, and scenarios, collectively show that SpaDecoder effectively harnesses 3D tissue structure and sc-reference profiles to improve cell type deconvolution. SpaDecoder is available at https://github.com/ZhangLabGT/spadecoder.

キーワード:

空間トランスクリプトミクス細胞タイプデコンボリューション 3D組織構造単一細胞RNAシーケンスバイオインフォマティクス

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Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

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