ArticleBMC research notes2022
On clustering for cell-phenotyping in multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) data.
Article in BMC research notes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Antibody-Based Multiplex Image Analysis: Standard Analytical Workflows and Artificial Intelligence Tools for Pathologists.Laboratory investigation; a journal of technical methods and pathology · 2025Review
- Spatial proteomics: unveiling the multidimensional landscape of protein localization in human diseases.Proteome science · 2024Review
- GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.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
- SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data.PLoS genetics · 2023Article
- GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.bioRxiv : the preprint server for biology · 2023Article
- MIAMI: mutual information-based analysis of multiplex imaging data.Bioinformatics (Oxford, England) · 2022Article
- DenVar: density-based variation analysis of multiplex imaging data.Bioinformatics advances · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveMultiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) images are usually phenotyped using a manual thresholding process. The thresholding is prone to biases, especially when examining multiple images with high cellularity.
resultsUnsupervised cell-phenotyping methods including PhenoGraph, flowMeans, and SamSPECTRAL, primarily used in flow cytometry data, often perform poorly or need elaborate tuning to perform well in the context of mIHC and MIBI data. We show that, instead, semi-supervised cell clustering using Random Forests, linear and quadratic discriminant analysis are superior. We test the performance of the methods on two mIHC datasets from the University of Colorado School of Medicine and a publicly available MIBI dataset. Each dataset contains a bunch of highly complex images.
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Registered trials
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