ArticleNature communications2023
Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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Who cites it
66 citing papers in PubMed.
- Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition in metastatic TNBC.Nature cancer · 2026Trial
- CellTune: an integrative software for accurate cell classification in spatial proteomics.Nature methods · 2026Article
- Data modeling the interplay between single-cell shape, single-cell protein expression, and tissue state.Cell reports methods · 2026Article
- Cancer-Associated Fibroblasts Promote Epithelial-Mesenchymal Transition and Classical to Basal Subtype Shift in Pancreatic Cancer.Cancer research · 2026Article
- Artificial Intelligence for Discovery in Life Sciences.Bioconjugate chemistry · 2026Review
- Anti-LAG-3 with or without anti-PD-1 in recurrent glioblastoma: a phase 1 trial.Nature medicine · 2026Article
- PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns.bioRxiv : the preprint server for biology · 2026Article
- Spatiotemporal organisation of residual disease in mouse and human BRCA1-deficient mammary tumours and breast cancer.Nature communications · 2026Article
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- MICRON learns outcome-associated representations of spatial immune microenvironments.bioRxiv : the preprint server for biology · 2026Article
- The immunometabolic topography of cellular organization and bacterial control in tuberculosis granulomas.Nature immunology · 2026Article
- MIMIC: a flexible pipeline to register and summarize IMC-MSI experiments.Communications biology · 2026Article
- The automated computational workflow QUICHE reveals structural definitions of antitumor responses in triple-negative breast cancer.Nature cancer · 2026Article
- Deep neural networks to register and annotate cells in moving and deforming nervous systems.eLife · 2026Article
- Metabolic plasticity of sphingolipids governs cancer cell fitness in acidic tumor ecosystems.bioRxiv : the preprint server for biology · 2026Article
- Article
- GRIDGENE: Guided Region Identification based on Density of GENEs-a transcript density-based approach to characterize tissues by spatial transcriptomics.Bioinformatics advances · 2026Article
- Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes.Bioinformatics advances · 2026Article
- The evolving landscape of spatial proteomics technologies in the AI age.Fundamental research · 2026Review
- Macrophage-Dendritic Cell-T-Cell Tetrads Orchestrate Antitumor Immunity and Response to Checkpoint Blockade.bioRxiv : the preprint server for biology · 2025Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.
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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.