Yvan Saeys

This paper introduces two tools for improving the trustworthiness of single-cell dimensionality reduction by enhancing quality metrics and multi-scale structure preservation.

BfBio predicts angiogenic stalk cell genes using a Personalized PageRank algorithm, achieving high AUC-ROC performance and identifying novel cancer-related genes.

CytoScan detects technical anomalies in cytometry data by identifying outliers and novelties, improving quality control for reliable analyses.

anndataR enables interoperability between R and Python by allowing native reading and writing of H5AD files in R, with compatibility testing and conversion to R single-cell objects.

Continued allergen exposure drives lung TLO to generate ASCs that home to IL1-instructed para-bronchial cuffs.

CytoNormPy provides a fast Python implementation of CytoNorm for removing batch effects in cytometry data, with added clustering and visualization tools.

This paper introduces an optimal transport-based framework for visualizing high-parameter flow cytometry data, providing biologically consistent layouts and enabling quantitative analysis of population changes.

This study shows that peripheral monocytes' epigenomic landscape is rewired by a distal tumor, shaping their transcriptional response upon tumor infiltration.

This study evaluates out-of-distribution detection methods for identifying novel cell types and data shifts in single-cell transcriptomics using synthetic and real datasets.

SPArrOW is a pipeline for spatial transcriptomics analysis that improves data quality, provides visual quality checks, and enables interactive exploration and optimization of processing parameters.

The paper uses immunophenotyping to identify patient subgroups in systemic autoimmune diseases, revealing shared immunophenotypes and cytokine signatures across different diagnoses.

The paper benchmarks 11 cell type deconvolution methods on spatial transcriptomics data using synthetic and real datasets, finding that some off-the-shelf methods outperform specialized ones.

The paper identifies transit amplifying cells in maize meristems using spatial transcriptomics and shows their growth and differentiation are independently controlled.

SpatialData, a Zarr/OME-NGFF-based format plus Python library, unifies images, labels, points, shapes and tables across spatial omics platforms with coordinate alignment, lazy loading and cross-modal aggregation.

This paper evaluates rejection strategies in single-cell annotation, finding partial rejection in hierarchical classifiers improves reliability without losing label information.

Combining pseudobulk differential expression, cell-type specificity, sample prevalence and NicheNet-v2 ligand activity, MultiNicheNet ranks condition-specific ligand-receptor-target signaling in multi-sample scRNA-seq with batches or multifactor designs.

The paper introduces SCIP, a scalable image processing pipeline for morphological profiling of image cytometry data, demonstrating its application on three datasets to classify and analyze cell types and drug responses.

This paper meta-analyzes single-cell method benchmarks to identify challenges in extensibility, interoperability, and reproducibility.

This study compares antibody- and lipid-based hashing methods for single-cell RNA-seq, finding antibody-based hashing more efficient and cost-effective for clinical samples.

DALI is a software tool that integrates multimodal single-cell RNA-seq and immune receptor data to analyze T and B cell diversity, supporting both bioinformaticians and immunologists.

dyngen is a multi-modal simulation tool that enables the development and testing of new computational methods for single-cell omics analyses.

This study benchmarks single cell RNA-seq technologies to assess their ability to detect transcriptional changes in NGP neuroblastoma cells after nutlin-3 treatment, finding heterogeneity in response not captured by bulk RNA-seq.

The paper introduces a generalized additive model framework for trajectory-based differential expression analysis in single-cell RNA-seq data, enabling flexible inference of within and between lineage gene expression differences.

This study compares classical machine learning and deep learning for classifying white blood cells using stain-free imaging flow cytometry, achieving balanced accuracies of 0.776 and 0.697 for white blood cells and 0.866 and 0.867 for eosinophils.

The paper presents a comprehensive human transcriptome atlas with single-nucleotide resolution, identifying thousands of novel RNA species and regulatory interactions.

This paper introduces a human-in-the-loop framework for improving EM image quality through interactive image restoration and analysis.

This paper benchmarks 29 trajectory inference methods on real and synthetic datasets, identifies top-performing tools, and provides guidelines for users.

Unsupervised trajectory inference by SCORPIUS outperformed Wanderlust, Monocle and Waterfall across ten scRNA-seq datasets and predicted falling protein synthesis during dendritic cell development, validated in vivo.