A5008243505- Single-cell and spatial transcriptomics
- Gene Regulatory Network Analysis
- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Neuroblastoma Research and Treatments
- Cell Image Analysis Techniques
- Data Analysis with R
- Cancer-related molecular mechanisms research
- Material Properties and Processing
- T-cell and B-cell Immunology
- Immunotherapy and Immune Responses
- Scientific Computing and Data Management
- demographic modeling and climate adaptation
- Cancer, Hypoxia, and Metabolism
- Genomics and Chromatin Dynamics
- RNA Research and Splicing
- Mathematical Biology Tumor Growth
- Simulation Techniques and Applications
- Epigenetics and DNA Methylation
- Matrix Theory and Algorithms
- RNA modifications and cancer
- Image Processing and 3D Reconstruction
- Digital Humanities and Scholarship
- Advanced Proteomics Techniques and Applications
- Cancer Genomics and Diagnostics
Ghent University
2016-2025
VIB-UGent Center for Inflammation Research
2015-2025
Ghent University Hospital
2015-2025
Intuit (United States)
2021-2022
Cancer Research Institute Ghent
2015-2021
Abstract Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream trajectory inference, it is vital to discover genes that are (i) associated with lineages trajectory, or (ii) differentially expressed between lineages, illuminate underlying biological processes. Current data analysis procedures, however, either fail exploit continuous resolution provided pinpoint exact types differential We introduce...
Abstract A critical step in the analysis of large genome-wide gene expression datasets is use module detection methods to group genes into co-expression modules. Because limitations classical clustering methods, numerous alternative have been proposed, which improve upon by handling only a subset samples, modelling regulatory network, and/or allowing overlap between In this study we known networks do comprehensive and robust evaluation these different methods. Overall, decomposition...
Highlights•ZEB2 is highly expressed across the macrophage lineage•ZEB2 preserves tissue-specific identities of macrophages tissues•ZEB2 deficient are outcompeted by WT counterparts•LXRα crucial for Kupffer cell identity and maintained ZEB2SummaryHeterogeneity between different populations has become a defining feature this lineage. However, conserved factors remain largely unknown. The transcription factor ZEB2 best described its role in epithelial to mesenchymal transition; however, within...
1 Summary Recent advances in RNA sequencing enable the generation of genome-wide expression data at single-cell level, opening up new avenues for transcriptomics and systems biology. A application whole-transcriptomics is unbiased ordering cells according to their progression along a dynamic process interest. We introduce SCORPIUS, method which can effectively reconstruct an individual without any prior information about process. Comprehensive evaluation using ten scRNA-seq datasets shows...
Abstract Using single-cell-omics data, it is now possible to computationally order cells along trajectories, allowing the unbiased study of cellular dynamic processes. Since 2014, more than 50 trajectory inference methods have been developed, each with its own set methodological characteristics. As a result, choosing method infer trajectories often challenging, since comprehensive assessment performance and robustness still lacking. In facilitate comparison results these other gold standard,...
Neuroblastoma is characterized by substantial clinical heterogeneity. Despite intensive treatment, the survival rates of high-risk neuroblastoma patients are still disappointingly low. Somatic chromosomal copy number aberrations have been shown to be associated with patient outcome, particularly in low- and intermediate-risk patients. To improve outcome prediction neuroblastoma, we aimed design a prognostic classification method based on aberrations.In an international collaboration,...
Abstract We present dyngen, a multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. dyngen is more flexible than current engines, and allows better method development benchmarking, thereby stimulating testing of computational methods. demonstrate its potential spearheading methods on three applications: aligning cell developmental trajectories, cell-specific regulatory network inference estimation RNA velocity.
Abstract Biology has become a data-intensive science. Recent technological advances in single-cell genomics have enabled the measurement of multiple facets cellular state, producing datasets with millions observations. While these data hold great promise for understanding molecular mechanisms health and disease, analysis challenges arising from sparsity, technical biological variability, high dimensionality hinder derivation such mechanistic insights. To promote innovation algorithms...
Abstract With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide and method development. However, a lack standardisation extensibility in current limits their usability, longevity, relevance community. We present Open Problems, living, extensible, community-guided benchmarking platform including 10 tasks that we envision will raise standards for selection, evaluation, development methods analysis.
Trajectory inference methods have emerged as a novel class of single-cell bioinformatics tools to study cellular dynamics at unprecedented resolution. Initial development focused on adapting based clustering or graph traversal, but recent advances extend the field in different directions. A first includes probabilistic that report uncertainties about their outputs, and new consider complementary knowledge, such unspliced mRNA, time point information, other types omics data construct...
PurposeOur goal was to design a customized microarray, arrEYE, for high-resolution copy number variant (CNV) analysis of known and candidate genes inherited retinal dystrophy (iRD) retina-expressed noncoding RNAs (ncRNAs).MethodsarrEYE contains probes the full genomic region 106 iRD genes, including those implicated in retinitis pigmentosa (RP) (the most frequent iRD), cone–rod dystrophies, macular an additional 60 196 ncRNAs. Eight CNVs identified by other techniques were used as positive...
Abstract Gene regulatory networks (GRNs) underpin cellular identity and function, playing a key role in health disease. Despite various benchmarking efforts, existing studies remain limited the number of GRN inference methods, datasets, evaluation metrics. The absence universally accepted ground truth further complicates evaluation, requiring continuous refinement strategies. In addition, interactions are highly context-specific vary between perturbations, cell types, tissues, organisms....
// Anneleen Decock 1,2 , Maté Ongenaert 1 Robrecht Cannoodt 1,2,3,4,5 Kimberly Verniers Bram De Wilde 1,2,6 Geneviève Laureys 6 Nadine Van Roy Ana P. Berbegall 7 Julie Bienertova-Vasku 8 Nick Bown 9 Nathalie Clément 10 Valérie Combaret 11 Michelle Haber 12 Claire Hoyoux 13 Jayne Murray Rosa Noguera Gaelle Pierron 14 Gudrun Schleiermacher 15 Johannes H. Schulte 16 Ray L. Stallings 17,18 Deborah A. Tweddle 19 for the Children's Cancer and Leukaemia Group (CCLG), Katleen Preter 1,2,3 Frank...
Abstract Motivation During the last decade, trajectory inference (TI) methods have emerged as a novel framework to model cell developmental dynamics, most notably in area of single-cell transcriptomics. At present, more than 70 TI been published, and recent benchmarks showed that even state-of-the-art only perform well for certain types but not others. Results In this work, we present TinGa, new is fast flexible, based on Growing Neural Graphs. We performed an extensive comparison TinGa five...
Abstract Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression levels during biological processes such as cell cycle, type differentiation, and cellular activation. Downstream trajectory inference, it is vital to discover genes that are associated with lineages illuminate underlying processes. Furthermore, differentially expressed between developmental/activational might be highly relevant further unravel system...
Abstract We present dyngen, a novel, multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. dyngen is more flexible than current engines, and allows better method development benchmarking, thereby stimulating testing of novel computational methods. demonstrate its potential spearheading methods on three applications: aligning cell developmental trajectories, regulatory network inference estimation RNA velocity.