ArticleCommunications biology2024
SPADE: spatial deconvolution for domain specific cell-type estimation.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- BART-spatial unravels biologically significant transcriptional regulators from spatial omics data.bioRxiv : the preprint server for biology · 2026Article
- KGLAR: Deconvoluting Spatial Transcriptomics Data with Single-cell Transcriptomes through Knowledge-guided NMF and Least Angle Regression.Interdisciplinary sciences, computational life sciences · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- High-resolution mapping of single cells in spatial context.Nature communications · 2025Article
- Deconvolution and inference of spatial communication through optimization algorithm for spatial transcriptomics.Communications biology · 2025Article
- Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network.BMC bioinformatics · 2024Article
- Statistical batch-aware embedded integration, dimension reduction, and alignment for spatial transcriptomics.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Understanding gene expression in different cell types within their spatial context is a key goal in genomics research. SPADE (SPAtial DEconvolution), our proposed method, addresses this by integrating spatial patterns into the analysis of cell type composition. This approach uses a combination of single-cell RNA sequencing, spatial transcriptomics, and histological data to accurately estimate the proportions of cell types in various locations. Our analyses of synthetic data have demonstrated SPADE's capability to discern cell type-specific spatial patterns effectively. When applied to real-life datasets, SPADE provides insights into cellular dynamics and the composition of tumor tissues. This enhances our comprehension of complex biological systems and aids in exploring cellular diversity. SPADE represents a significant advancement in deciphering spatial gene expression patterns, offering a powerful tool for the detailed investigation of cell types in spatial transcriptomics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.