Jovan Tanevski

ParTIpy scales archetypal analysis for biological trade-offs and integrates with single-cell workflows, enabling task inference in large datasets.

This paper constructs a transcriptional patient map of SLE using single-cell and bulk RNA-seq data, identifying conserved immune programs in blood and kidney tissue.

This study compares and optimizes methods for analyzing cell type neighbor preferences in spatial tissue data, introducing COZI which outperforms existing approaches.

TOAST improves spatial omics data alignment by integrating spatial constraints into optimal transport, enhancing accuracy and biological interpretability.

This study shows that different modes of macrophage cell death affect wound healing outcomes, with necroptosis impairing repair and apoptosis promoting better tissue recovery.

SpaCEy identifies spatial tissue patterns linked to clinical outcomes using explainable graph neural networks in proteomic cancer data.

The paper presents a multi-modal atlas of human kidney disease, identifying cellular and spatial mechanisms of injury resolution or failure and linking them to clinical outcomes.

The paper introduces Kasumi, a method to identify persistent local patterns in spatial omics data for improved patient stratification in cancer.

This study evaluates how different transcriptomics analysis pipelines affect downstream functional enrichment results using FLOP, finding that data filtering significantly impacts gene set correlation.

This study maps acute murine myocardial infarction with subcellular resolution, identifying a novel endocardial route of immune cell infiltration and vWF as a key mediator.

LIANA+ is a framework for analyzing cell-cell communication in single-cell and spatial transcriptomics data, integrating multiple methods and supporting multi-omics data.

This study generates a high-resolution spatial multi-omic map of human myocardial infarction by integrating single-cell gene expression, chromatin accessibility, and spatial transcriptomic data to reveal cellular zones of injury, repair, and remodeling.

MISTy is a framework for analyzing spatial omics data to identify inter-cellular signaling interactions across different spatial contexts in breast cancer datasets.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

The paper predicts cellular positions in the Drosophila embryo using scRNA-seq data by benchmarking methods against spatially annotated genes.

The paper benchmarks bulk functional genomics tools on scRNA-seq data, showing comparable performance to bulk data and suggesting their applicability over dedicated single-cell tools.