ArticleBioinformatics (Oxford, England)2023
Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- scRNA-seq and genomics analyses reveal key mechanisms of inverted papilloma-associated sinonasal squamous cell carcinoma malignant transformation.bioRxiv : the preprint server for biology · 2026Article
- Distinct Tumor-Immune Ecologies in Patients with Lung Cancer Predict Progression and Define a Clinical Biomarker of Therapy Response.Cancer research · 2026Article
- Detecting clinically relevant topological structures in multiplexed spatial proteomics using TopKAT.Patterns (New York, N.Y.) · 2026Article
- Using spatial proteomics to enhance cell type assignments in histology images.Cell reports methods · 2025Article
- Machine Learning-Driven Prediction of Spatiotemporal Dynamics of Active Nuclei DuringInternational journal of molecular sciences · 2025Article
- Effects of segmentation errors on downstream-analysis in highly-multiplexed tissue imaging.PLoS computational biology · 2025Article
- Myeloid-driven immunosuppression in head and neck cancer: single-cell ATAC/RNA and spatial transcriptomic perspectives.Frontiers in oncology · 2025Review
- Quantitative in toto live imaging analysis of apical nuclear migration in the mouse telencephalic neuroepithelium.Development, growth & differentiation · 2024Article
- Statistical analysis of multiple regions-of-interest in multiplexed spatial proteomics data.Briefings in bioinformatics · 2024Article
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- A Spatial Omnibus Test (SPOT) for Spatial Proteomic Data.Bioinformatics (Oxford, England) · 2024Article
- SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA.Journal of proteome research · 2024Article
- A Spatial Omnibus Test (SPOT) for Spatial Proteomic Data.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
9 authors.
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Abstract
motivationSpatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of such organization on disease progression and patient survival. However, to date, the majority of supervised learning methods utilizing these data types did not take full advantage of the spatial information, impacting their performance and utilization.
resultsTaking inspiration from ecology and epidemiology, we developed novel spatial feature extraction methods for use with spatial proteomics data. We used these features to learn prediction models for cancer patient survival. As we show, using the spatial features led to consistent improvement over prior methods that used the spatial proteomics data for the same task. In addition, feature importance analysis revealed new insights about the cell interactions that contribute to patient survival. AVAILABILITY AND IMPLEMENTATION: The code for this work can be found at gitlab.com/enable-medicine-public/spatsurv.
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