A5072149605- Learning Styles and Cognitive Differences
- Single-cell and spatial transcriptomics
- Cell Image Analysis Techniques
- T-cell and B-cell Immunology
- Visual and Cognitive Learning Processes
- Digestive system and related health
- Diabetes and associated disorders
- Pancreatic function and diabetes
- Gene Regulatory Network Analysis
- Immunodeficiency and Autoimmune Disorders
- Gene expression and cancer classification
- Cognitive Science and Education Research
- Explainable Artificial Intelligence (XAI)
- Genomic variations and chromosomal abnormalities
- Artificial Intelligence in Healthcare and Education
- AI in cancer detection
- Bioinformatics and Genomic Networks
- Gut microbiota and health
- Immune Cell Function and Interaction
- Genomics and Rare Diseases
- Topological and Geometric Data Analysis
- Immunotherapy and Immune Responses
Charles University
2023-2025
Ghent University
2021-2025
VIB-UGent Center for Inflammation Research
2021-2025
Understanding complex, organ-level single-cell datasets represents a formidable interdisciplinary challenge. This study aims to describe developmental trajectories of thymocytes and mature T cells. We developed tviblindi , trajectory inference algorithm that integrates several autonomous modules - pseudotime inference, random walk simulations, real-time topological classification using persistent homology, autoencoder-based 2D visualization the vaevictis algorithm. integration facilitates...
Detailed knowledge of human B-cell development is crucial for the proper interpretation inborn errors immunity and malignant diseases. It interest to understand kinetics protein expression changes during development, but also properly interpret major possibly alternative developmental trajectories. We have investigated samples from healthy individuals with aim describing all validated a 30-parameter mass cytometry panel demonstrated utility "vaevictis" visualization stages. used trajectory...
High-dimensional flow cytometry is the gold standard to study human immune system in large cohorts. However, sample sizes increase inter-experimental variation because of technical and experimental inaccuracies introduced by batch variability. Our high-throughput processing pipeline combination with 28-color focuses on increased throughput (192 samples/experiment) high reproducibility. We implemented quality control checkpoints reduce variation. Finally, we integrated FlowSOM clustering...
Understanding complex, organ-level single-cell datasets represents a formidable interdisciplinary challenge. This study aims to describe developmental trajectories of thymocytes and mature T cells. We developed tviblindi , trajectory inference algorithm that integrates several autonomous modules - pseudotime inference, random walk simulations, real-time topological classification using persistent homology, autoencoder-based 2D visualization the vaevictis algorithm. integration facilitates...
Understanding complex, organ-level single-cell datasets represents a formidable interdisciplinary challenge. This study aims to describe developmental trajectories of thymocytes and mature T cells. We developed tviblindi , trajectory inference algorithm that integrates several autonomous modules - pseudotime inference, random walk simulations, real-time topological classification using persistent homology, autoencoder-based 2D visualization the vaevictis algorithm. integration facilitates...
Abstract Detailed knowledge of the human B-cell development is crucial for proper interpretation inborn errors immunity and malignant diseases. It interest to understand kinetics protein expression changes during B cell development, but also properly interpret major possibly alternative developmental trajectories. We have investigated bone marrow peripheral blood samples from healthy individuals with aim describe all trajectories across two tissues. validated a 30-parameter mass cytometry...
Understanding complex, organ-level single-cell datasets represents a formidable interdisciplinary challenge. This study aims to describe developmental trajectories of thymocytes and mature T cells. We developed tviblindi , trajectory inference algorithm that integrates several autonomous modules - pseudotime inference, random walk simulations, real-time topological classification using persistent homology, autoencoder-based 2D visualization the vaevictis algorithm. integration facilitates...
Recent advances in dimensionality reduction have achieved more accurate lower-dimensional embeddings of high-dimensional data. In addition to visualisation purposes, these can be used for downstream processing, including batch effect normalisation, clustering, community detection or trajectory inference. We use the notion structure preservation at both local and global levels create a deep learning model, based on variational autoencoder (VAE) stochastic quartet loss from SQuadMDS algorithm....
Abstract Dimensionality reduction techniques are essential in current single-cell ‘omics approaches, offering biologists a first glimpse of the structure present their data. These methods most often used to visualise high-dimensional and noisy input datasets, but also frequently applied for downstream learning. By design, every dimensionality technique preserves some characteristics original, data, while discarding others. We introduce ViScore , framework validation low-dimensional...
Abstract Population-wide assessment of the human immune system is needed to better understand homeostasis, responses against pathogens or efficacy vaccines. High-dimensional flow cytometry gold standard for in-depth immunophenotyping but large cohorts requires multiple experiments over a prolonged period time and bear sources error, reducing precision required sensitive analyses. We performed study using 28-color investigate heritability homeostasis T cell functions in 3000 individuals....