ArticleBioinformatics (Oxford, England)2022
Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Society for Immunotherapy of Cancer: updates and best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) image analysis and data sharing.Journal for immunotherapy of cancer · 2025Guideline
- Automated classification of cellular expression in multiplexed imaging data with Nimbus.Nature methods · 2025Article
- Toward universal immunofluorescence normalization for multiplex tissue imaging with UniFORM.Cell reports methods · 2025Article
- Integrative multi-omics reveals a regulatory and exhausted T-cell landscape in CLL and identifies galectin-9 as an immunotherapy target.Nature communications · 2025Article
- Functional protein biomarkers based on distributions of expression levels in single-cell imaging data.Bioinformatics (Oxford, England) · 2025Article
- Molecular and Anatomical Strengthening of "Winner" Climbing Fiber Synapses in Developing Mouse Purkinje Cells.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Emerging AI approaches for cancer spatial omics.GigaScience · 2025Review
- Highly Multiplexed Tissue Imaging in Precision Oncology and Translational Cancer Research.Cancer discovery · 2024Review
- Consensus tissue domain detection in spatial omics data using multiplex image labeling with regional morphology (MILWRM).Communications biology · 2024Article
- GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.Bioinformatics (Oxford, England) · 2024Article
- Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2024Article
- Quantitative performance assessment of Ultivue multiplex panels in formalin-fixed, paraffin-embedded human and murine tumor specimens.Scientific reports · 2024Article
- MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation.Communications biology · 2024Article
- mxfda: a comprehensive toolkit for functional data analysis of single-cell spatial data.Bioinformatics advances · 2024Article
- Accounting for intensity variation in image analysis of large-scale multiplexed clinical trial datasets.The journal of pathology. Clinical research · 2023Article
- Dual-modality imaging of immunofluorescence and imaging mass cytometry for whole-slide imaging and accurate segmentation.Cell reports methods · 2023Article
- GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.bioRxiv : the preprint server for biology · 2023Article
- A platform-independent framework for phenotyping of multiplex tissue imaging data.PLoS computational biology · 2023Article
- Dual-modality imaging of immunofluorescence and imaging mass cytometry for whole slide imaging with accurate single-cell segmentation.bioRxiv : the preprint server for biology · 2023Article
- Systematic replication enables normalization of high-throughput imaging assays.Bioinformatics (Oxford, England) · 2022Article
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9 authors.
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Abstract
motivationMultiplexed imaging is a nascent single-cell assay with a complex data structure susceptible to technical variability that disrupts inference. These in situ methods are valuable in understanding cell-cell interactions, but few standardized processing steps or normalization techniques of multiplexed imaging data are available.
resultsWe implement and compare data transformations and normalization algorithms in multiplexed imaging data. Our methods adapt the ComBat and functional data registration methods to remove slide effects in this domain, and we present an evaluation framework to compare the proposed approaches. We present clear slide-to-slide variation in the raw, unadjusted data and show that many of the proposed normalization methods reduce this variation while preserving and improving the biological signal. Furthermore, we find that dividing multiplexed imaging data by its slide mean, and the functional data registration methods, perform the best under our proposed evaluation framework. In summary, this approach provides a foundation for better data quality and evaluation criteria in multiplexed imaging. AVAILABILITY AND IMPLEMENTATION: Source code is provided at: https://github.com/statimagcoll/MultiplexedNormalization and an R package to implement these methods is available here: https://github.com/ColemanRHarris/mxnorm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.