Denis Schapiro

BaSiCPy improves illumination correction in microscopy images by addressing foreground correlations and enabling automated processing.

This study uses single-cell RNA-seq to analyze transcriptional changes in mouse hearts during perinatal development, identifying gene regulatory patterns and a cell type-independent imprinted gene signature.

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

This study uses spatial analysis to identify immune and metabolic niches in multiple myeloma, finding that CD4+ T cell proximity predicts disease progression.

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

The paper introduces APP, a clustering method that reduces high-dimensional biological data to lower dimensions to improve clustering performance across multiple data types.

micronuclAI automates micronuclei quantification to assess chromosomal instability, achieving near-human performance on cancer cell line images.

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.

The paper develops a computational framework for spatial omics power analysis using in silico tissue generation and applies it across diverse spatial data modalities.

This study maps cell types and their spatial distribution in multiple sclerosis lesions to understand disease progression and identify potential therapeutic targets.

Galaxy-ME is a web-based software hub for analyzing and visualizing multiplex tissue imaging datasets, demonstrating its utility on normal and cancerous tissue 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.

This study uses spatial transcriptomics to analyze cell type dynamics in rheumatoid arthritis synovium, revealing chemokine-driven interactions in inflamed tissue.

MIAAIM integrates multimodal imaging data across tissue types to map tissue states and link cellular phenotypes with molecular microenvironments.

MCMICRO processes multiplexed tissue images into single-cell data, demonstrating its versatility across different tissues and imaging platforms.

This study uses single-nucleus and spatial transcriptomics to analyze pancreatic cancer before and after neoadjuvant treatment, revealing changes in cell states and immune infiltration that may inform therapy.

This paper introduces SVCA, a method to analyze spatial molecular data and quantify the impact of cell-cell interactions on gene expression in breast cancer tissue.

This paper introduces HDST, a high-resolution spatial transcriptomics method, and demonstrates its application in mouse brain tissue to resolve spatial expression patterns and cell types.

The paper introduces histoCAT, a tool for analyzing single-cell spatial omics data to explore cell phenotypes and interactions in tissues, demonstrating its application on human breast cancer mass cytometry images.