ArticleCommunications biology2022
A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- AI-Revealed Transition From Density- to Connectivity-Controlled Mechanics in Hierarchical Porous Materials.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Development of a universal imaging "phenome" using shape, appearance and motion (SAM) features and the SAM Phenotype Observation Tool (SPOT).Nature communications · 2026Article
- Deep learning-based identification and quantification of rare circulating hybrid cells in orthotopic pancreatic cancer models.bioRxiv : the preprint server for biology · 2026Article
- Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics.Journal of translational medicine · 2026Review
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.Cell systems · 2026Article
- miniMTI: minimal multiplex tissue imaging enhances biomarker expression prediction from histology.Research square · 2026Article
- Unsupervised Variational-Autoencoder-Based Analysis of Morphological Representations in Magnetic-Nanoparticle-Treated Macrophages.Bioengineering (Basel, Switzerland) · 2026Article
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon.Molecular oncology · 2025Review
- Imputing single-cell protein abundance in multiplex tissue imaging.Nature communications · 2025Article
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- 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
- MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation.Communications biology · 2024Article
- Joint shape/texture representation learning for cardiovascular disease diagnosis from magnetic resonance imaging.European heart journal. Imaging methods and practice · 2024Article
- Article
- Quantifying the distribution of feature values over data represented in arbitrary dimensional spaces.PLoS computational biology · 2024Article
- Morphodynamical cell state description via live-cell imaging trajectory embedding.Communications biology · 2023Article
- Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays.PLoS computational biology · 2022Article
- Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic.PLoS computational biology · 2022Article
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8 authors.
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Abstract
Image-based cell phenotyping relies on quantitative measurements as encoded representations of cells; however, defining suitable representations that capture complex imaging features is challenged by the lack of robust methods to segment cells, identify subcellular compartments, and extract relevant features. Variational autoencoder (VAE) approaches produce encouraging results by mapping an image to a representative descriptor, and outperform classical hand-crafted features for morphology, intensity, and texture at differentiating data. Although VAEs show promising results for capturing morphological and organizational features in tissue, single cell image analyses based on VAEs often fail to identify biologically informative features due to uninformative technical variation. Here we propose a multi-encoder VAE (ME-VAE) in single cell image analysis using transformed images as a self-supervised signal to extract transform-invariant biologically meaningful features, including emergent features not obvious from prior knowledge. We show that the proposed architecture improves analysis by making distinct cell populations more separable compared to traditional and recent extensions of VAE architectures and intensity measurements by enhancing phenotypic differences between cells and by improving correlations to other analytic modalities. Better feature extraction and image analysis methods enabled by the ME-VAE will advance our understanding of complex cell biology and enable discoveries previously hidden behind image complexity ultimately improving medical outcomes and drug discovery.
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