Mor Nitzan

CRISP identifies and localizes interactions between biological signals in single-cell and spatial transcriptomic data, revealing their effects on tissue function and gene regulation.

The paper profiles microRNA expression in human brain cell types to reveal cell-type specificity and age-related changes.

CO_SCPLOWASEIC_SCPLOW identifies condition-specific cell-cell interactions in spatial omics data by modeling edges rather than cells, revealing spatial reorganization in disease states.

This study introduces a neighborhood-informed framework to improve positional decoding in Drosophila embryos, closing the positional information gap and enhancing cell identity specification.

The paper introduces bioIB, a framework using the Information Bottleneck method to extract compressed, signal-informative representations of scRNA-seq data, demonstrating its utility in diverse biological contexts.

The paper shows intestinal epithelial zonation arises from self-organization via local cell-cell communication, not external cues or intrinsic programs.

This study shows that combining differential adhesion with cell signaling avoids kinetic traps, enabling robust multicellular self-organization in synthetic systems.

This study uses spatial transcriptomics to analyze tumor microenvironment remodeling dynamics in triple-negative breast cancer, identifying early stromal programs as potential therapeutic targets.

moscot unifies optimal transport for temporal, spatial and spatiotemporal single-cell mapping, using multimodal cost spaces and low-rank solvers to scale to 1.7 million mouse embryo cells.

This paper introduces Annotatability, a method that uses deep network training dynamics to identify and correct ambiguous cell annotations in single-cell and spatial omics data.

The paper introduces moslin, a method that combines lineage and gene expression data across time points to predict cell fate and decision driver genes.

The paper develops a computational framework to study division of labor in tissues by analyzing cell interactions and spatial cues using single-cell RNA-seq and spatial transcriptomics data.

This paper introduces biolord, a deep generative framework for disentangling biological factors in single-cell data, enabling prediction of cell states and responses to drug perturbations.

scPrisma decouples and enhances periodic and linear biological signals in single-cell data using spectral template matching, demonstrating effectiveness across multiple biological systems.

TACCO is a framework for annotating cell identities in single-cell and spatial omics data, excelling in speed and performance across datasets.

SiFT filters biological signals in single-cell data to uncover hidden processes, outperforming existing methods in preprocessing and revealing disease-related dynamics in COVID-19 data.

The paper introduces Tangram, a method that aligns single-nucleus RNA-seq data with spatial data to create an anatomically integrated spatial map of the mouse brain.

The paper introduces cfChIP-seq to analyze cell-free nucleosomes and shows it can detect disease-related transcriptional changes and patient-specific features in cancer.

This paper develops a mathematical model to study how cells can use diffusible signals to grow into clusters with different shapes, identifying rules for generating rod-like structures and multiple protrusions.

The paper introduces a method to reconstruct spatial positions of cells from single-cell transcriptomes without reference imaging data, leveraging expression space distances.